bldc motor drive controller for electric vehicles · bldc motor drive controller for electric...

BLDC Motor Drive Controller

for Electric Vehicles

Alireza Tashakori Abkenar

Faculty of Science, Engineering and Technology

Swinburne University of Technology

A thesis submitted for the degree of

Doctor of Philosophy

May 2014

I would like to dedicate this thesis to my loving parents.

Abstract

Electric vehicles are the best solution for green transportation due to their

high efficiency and zero greenhouse gas emissions. Various electric motors have

been used as the propulsion system of electric vehicles. Performance of brushed

Direct Current (DC) motors, induction motors, switched reluctance motors, and

permanent magnet Brushless DC (BLDC) motors are compared according to

the in-wheel motor technology requirements under normal and critical conditions

through simulation. This study shows that BLDC motors are the most suitable

electric motor for the high performance electric vehicles. An accurate model

of a BLDC motor is needed to investigate the motor performance for different

control algorithms. Therefore a BLDC motor with an ideal back-Electro Motive

Force (EMF) voltage and its control drive are modelled in Simulink. Correct

performance of the BLDC motor drive model is validated through experimental

data.

Direct torque control technique is a type of flux linkage based sensorless con-

trol methods in the BLDC motors. In this thesis, direct torque control switching

technique of the BLDC motor is discussed. Results of this study show effec-

tive torque control, reduction of torque ripples and improved performance of the

BLDC motor compared to the conventional switching control techniques.

An optimized back-EMF zero crossing detection based sensorless technique of

the BLDC motor is presented in this thesis. The proposed sensorless algorithm

generates commutation signals of the BLDC motor according to back-EMF zero

crossing detection points of only one phase of the motor. This algorithm is

simple and remarkably reduces sensing circuitry, noise susceptibility and cost of

the sensorless BLDC motor drives. A digital pulse width modulation (PWM)

switching technique is implemented to control the speed of the BLDC motor.

Stability of the proposed sensorless BLDC motor drive using a digital PWM

speed controller is analysed by Lyapunov’s second method. A novel condition

for duty cycle of the PWM speed controller is introduced for stability analysis

of the BLDC motor drive. Effectiveness of the proposed sensorless algorithm

and correctness of the introduced PWM controller stability condition are verified

ii

Abstract

through simulation and experimental results.

Robust performance of the in-wheel BLDC motor drives is an important fac-

tor in safety of the electric vehicles. Effect of inverter switch faults of an in-wheel

BLDC motor on performance of the four wheel drive electric vehicle is studied

through simulation. Results show unstable performance of the electric vehicle af-

ter fault occurrence and demonstrate need of the fault tolerant control system for

the in-wheel motors. This thesis presents two novel fault tolerant control systems

for inverter switch faults and position detection sensor faults in the BLDC motor

drives. Performance of the BLDC motor is studied under various fault conditions

through a validated simulation model. Knowledge based tables were developed

to diagnose the inverter switch and Hall Effect sensor faults based on discrete

Fourier transform analysis of the BLDC motor line voltages. The developed fault

diagnosis algorithms are simple and capable of detecting the fault occurrence,

identify fault type and the faulty switch or position sensor of the BLDC mo-

tor drive. Simulation results and the proposed knowledge based fault diagnosis

tables are validated through experimental data. The proposed fault diagnosis

algorithms do not need massive computational effort and can be implemented as

a subroutine of the main control algorithm of the BLDC motor.

iii

Acknowledgement

First and foremost my deepest gratitude goes to my supervisor Dr. Mehran

Motamed Ektesabi for accepting me as a PhD student. I would like to thank for

his guidance and support not only on the research topic but also in my personal

life throughout these years. Our regular meetings and discussions helped me a

lot through my research during my PhD candidature.

I gratefully acknowledge the financial, academic and technical support of the

Faculty of Engineering and Industrial Science, Swinburne University of Technol-

ogy and its staff that made my PhD research work possible.

Lastly, I would like to thank my family specially my parents, to whom I

dedicate this thesis. Words can not express how grateful I am to my mother,

father and my sister for their love, encouragement and all of the sacrifices that

they have made on my behalf.


iv

Declaration

I hereby declare that this Ph.D. thesis entitled BLDC Motor Drive Con-

troller for Electric Vehicles has been compiled by me under the supervision of

Dr. Mehran Motamed Ektesabi at Faculty of Engineering and Industrial science,

Swinburne University of Technology, Melbourne, Australia.

This thesis contains no material which has been accepted for the award of

any other degree or diploma, except where due reference is made. To the best of

my knowledge, this thesis contains no material previously published or written

by another person except where due reference is made in the text of the thesis.


Place: Melbourne

Date:

v

Publication

Portions of the material in this thesis have previously appeared in the following

publications:

Book Chapter:

1. A. Tashakori and M. Ektesabi, “Direct torque control of in-wheel bldc motor

used in electric vehicle”, In Gi-Chul Yang, Sio-long Ao, and Len Gelman,

editors, IAENG Transactions on Engineering Technologies, volume 229 of

Lecture Notes in Electrical Engineering, pp. 273-286, Springer Netherlands,

2013.

Journals:

2. A. Tashakori and M. Ektesabi, Position sensors fault tolerant control system

in BLDC motors, Engineering Letters, Volume 22 Issue 1, pp. 39-46, Feb

2014.

3. A. Tashakori and M. Ektesabi, “Comparison of different PWM switching

modes of BLDC motor as drive train of electric vehicles”, World Academy of

Science, Journal of Engineering and Technology 2012, Vol. 67, pp. 719-725.

Peer Reviewed Conference Papers:

4. A. Tashakori and M. Ektesabi, “Fault Diagnosis of In-wheel BLDC Motor

Drive for Electric Vehicle Application”, Proceeding of the 2013 IEEE Intel-

ligent Vehicles Symposium, pp. 925-930, June 2013, Gold Coast Australia.

5. A. Tashakori and M. Ektesabi, “A simple fault tolerant control system for

Hall Effect sensors failure of BLDC motor”, Proceeding of the 8th IEEE

Conference on Industrial Electronics and Applications (ICIEA 2013), pp.

1011-1016, June 2013, Melbourne Australia.

6. A. Tashakori and M. Ektesabi, “Stability analysis of sensorless BLDC motor

drive using digital PWM technique for electric vehicles”, Proceeding of 38th

Annual Conference on IEEE Industrial Electronics Society (IECON 2012),

pp. 4898-4903, October 2012, Montreal Canada.

vi

Publication

7. A. Tashakori, M. Ektesabi, “Direct torque controlled drive train for electric

vehicle”, Lecturer notes in engineering and computer science: Proceeding

of the world congress on engineering 2012 (WCE 2012), pp. 948-952, July

2012, London UK.

8. A. Tashakori, M. Ektesabi, and N. Hosseinzadeh, “Characteristics of suit-

able drive train for electric vehicle,” in Proceeding of the International Con-

ference on Instrumentation, Measurement, Circuits and Systems (ICIMCS

2011), Vol. 2, pp. 51-57, ASME, 2011.

9. A. Tashakori, M. Ektesabi and N. Hosseinzadeh, “Modelling of BLDC mo-

tor with ideal back-EMF for automation application”, Lecture Notes in En-

gineering and Computer Science: Proceedings of The World Congress on

Engineering 2011 (WCE 2011), Vol. 2, pp. 1504-1508, July 2011, London

UK.

vii

Contents

Contents viii

Nomenclature xiii

List of Figures xiv

List of Tables xix

1 Introduction 1

2 Selection of a Suitable Motor for Electric Vehicles 7

2.1 Chapter Overview . . . . . . . . . . . . . . . . . . . . . . . . . . . 7

2.2 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8

2.3 The Drive Train of Electric Vehicles . . . . . . . . . . . . . . . . . 10

2.3.1 Conventional AC and DC Motors . . . . . . . . . . . . . . 12

2.3.2 Switched Reluctance Motors . . . . . . . . . . . . . . . . . 14

2.3.3 BLDC Motors . . . . . . . . . . . . . . . . . . . . . . . . . 16

2.4 Motor Comparison . . . . . . . . . . . . . . . . . . . . . . . . . . 20

2.4.1 Performance Comparison of the Motors under Normal Con-

dition . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 23

2.4.2 Performance Comparison of the Motors under Critical Con-

dition . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 25

2.4.2.1 Transient Electric Faults . . . . . . . . . . . . . . 26

2.4.2.2 Vibration and Mechanical Shocks . . . . . . . . . 28

2.5 Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 31

viii

CONTENTS

3 Modelling of the BLDC Motor Drive for EV Application 33


3.2 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 33

3.3 Overall View of the BLDC Motor Drive . . . . . . . . . . . . . . . 34

3.4 Modelling of the BLDC Motor . . . . . . . . . . . . . . . . . . . . 35

3.5 Modelling of the BLDC Motor Drive . . . . . . . . . . . . . . . . 40

3.6 Simulation Results and Discussion . . . . . . . . . . . . . . . . . . 42

3.7 Simulation Model Validation . . . . . . . . . . . . . . . . . . . . . 46

3.8 Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 48

4 Direct Torque Control Drive of BLDC Motor for EV Application 49


4.2 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 50

4.3 Direct Torque Control of the BLDC Motor Using Three Phase

Conduction Mode . . . . . . . . . . . . . . . . . . . . . . . . . . . 52


4.5 Experimental Results . . . . . . . . . . . . . . . . . . . . . . . . . 61

4.6 Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 63

5 Stability Analysis of a Novel Sensorless Drive of BLDC Motor 65


5.2 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 66

5.3 Proposed Sensorless Technique for BLDC Motor . . . . . . . . . . 70

5.4 Stability Analysis of Digital PWM Controller . . . . . . . . . . . 75


5.6 Experiment Results . . . . . . . . . . . . . . . . . . . . . . . . . . 85

5.7 Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 92

6 Fault Diagnosis of the BLDC Motor Drive for EV Application 93


6.2 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 94

6.3 Inverter Open Circuit Switch Faults . . . . . . . . . . . . . . . . . 100

6.3.1 EV Dynamics Analysis under Inverter Open Circuit Switch

Fault . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 101

ix

CONTENTS

6.3.1.1 No Fault Condition . . . . . . . . . . . . . . . . . 103

6.3.1.2 VSI Open Circuit Fault . . . . . . . . . . . . . . 106

6.3.2 Fault Diagnosis . . . . . . . . . . . . . . . . . . . . . . . . 109

6.3.2.1 Fault Detection . . . . . . . . . . . . . . . . . . . 109

6.3.2.2 Fault Identification . . . . . . . . . . . . . . . . . 110

6.3.3 Experimental Results . . . . . . . . . . . . . . . . . . . . . 115

6.3.4 Remedial Strategy . . . . . . . . . . . . . . . . . . . . . . 119

6.4 Position Detection Sensors Failure . . . . . . . . . . . . . . . . . . 121

6.4.1 Performance of the BLDC Motor under Position Sensor Faults122

6.4.1.1 Hall Effect Signal is Constant Zero . . . . . . . . 122

6.4.1.2 Hall Effect Signal is Constant One . . . . . . . . 124

6.4.2 Fault Diagnosis . . . . . . . . . . . . . . . . . . . . . . . . 125

6.4.3 Experimental Results . . . . . . . . . . . . . . . . . . . . . 129

6.4.4 Remedial Strategy . . . . . . . . . . . . . . . . . . . . . . 134

6.5 Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 135

7 Conclusion 137

References 141

Appendix A 154

A Reference Links of Table 2.1 . . . . . . . . . . . . . . . . . . . . . 154

B Details of the motor models in Chapter 2 . . . . . . . . . . . . . . 155

C State Space Equation of BLDC Motor . . . . . . . . . . . . . . . 157

D Clarke Transformation . . . . . . . . . . . . . . . . . . . . . . . . 158

E Lyapunov’s Second Method for Stability . . . . . . . . . . . . . . 158

F Comaprison of Different PWM Switching Techniques of The BLDC

Motor . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 159

F.1 Normal Condition . . . . . . . . . . . . . . . . . . . . . . . 159

F.2 Critical Condition . . . . . . . . . . . . . . . . . . . . . . . 162

F.2.1 Mechanical Shocks . . . . . . . . . . . . . . . . . 163

F.2.2 Inverter Switch Faults . . . . . . . . . . . . . . . 165

G EV Model Simulation Results under Inverter Open Circuit Switch

Fault . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 165

x

CONTENTS

Appendix B 170

xi

Nomenclature

Roman Symbols

ωref Reference speed of the controller

ωm Angular speed of the rotor

ΘS Stator flux angle

θe Electrical angle of the rotor

θm Mechanical angle of the rotor

ϕrα α-axis rotor flux vector

ϕrβ β-axis rotor flux vector

ϕSα α-axis stator flux vector

ϕSβ β-axis stator flux vector

E Back-EMF voltage

eα α-axis back-EMF

eβ β-axis back-EMF

F (θe) Reference back-EMF signals of the BLDC motor with respect to the elec-

trical angle of the rotor

i Current

iα α-axis current vector

xii

Nomenclature

iβ β-axis current vector

Ke Back-EMF constant

KL flux linkage of the BLDC motor

Kt Torque constant

L Inductance

M Mutual inductance

P Number of poles

R Resistance

Te Electric torque

Tl Load torque

VDC Voltage of the inverter DC link

V Voltage

Vα α-axis voltage vector

Vβ β-axis voltage vector

xiii

List of Figures

2.1 Four wheel drive train of an IECEV . . . . . . . . . . . . . . . . . 9

2.2 Various switched reluctance motor geometries . . . . . . . . . . . 15

2.3 Schematic diagram of a two pole BLDC motor drive . . . . . . . . 17

2.4 The internal view of a BLDC motor . . . . . . . . . . . . . . . . . 18

2.5 Ideal current, back-EMF and commutation signals of BLDC motor 19

2.6 Transient speed responses of the motors under normal condition . 23

2.7 Transient torque responses of the motors under normal condition . 24

2.8 Transient torque/speed characteristics of the motors . . . . . . . . 25

2.9 Speed responses of the motors under same transient electrical fault 27

2.10 Torque responses of the motors under same transient electrical fault 27

2.11 Speed responses of the motors under same mechanical shocks . . . 28

2.12 Torque responses of the motors under same mechanical shocks . . 29

3.1 Overall structure of the BLDC motor drive . . . . . . . . . . . . . 35

3.2 Ideal reference back-EMF waveforms of the BLDC motor model . 37

3.3 BLDC motor simulation model . . . . . . . . . . . . . . . . . . . 39

3.4 Schematic diagram of a 3 phase, 4 poles, star connected BLDC

motor drive . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 41

3.5 Three phase VSI simulation model . . . . . . . . . . . . . . . . . 42

3.6 Speed characteristics of the BLDC motor simulation model . . . . 43

3.7 Torque characteristics of the BLDC motor simulation model . . . 44

3.8 Voltage, Current and Hall Effect signal of phase A of the BLDC

motor . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 45

3.9 Back-EMF signals of the BLDC motor model . . . . . . . . . . . 45

3.10 Experimental test set-up of the BLDC motor . . . . . . . . . . . . 46

xiv

LIST OF FIGURES

3.11 Line voltage and Hall Effect signal of phase A of the BLDC motor 47

4.1 Overall structure of DTC drive of the BLDC motor . . . . . . . . 53

4.2 Speed and torque responses of the direct torque controlled BLDC

motor drive . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 56

4.3 Pulsating torque of the BLDC motor for different hysteresis band

limits . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 57

4.4 Calculated stator flux magnitude and flux angle of the BLDC motor 58

4.5 Stator flux linkage trajectory of the BLDC motor for 5 and 10 N.m

loads . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 59

4.6 Speed response of the BLDC motor under sudden increase of load 60

4.7 Torque response of the BLDC motor under sudden increase of load 60

4.8 Experimental set-up of the BLDC motor . . . . . . . . . . . . . . 62

4.9 Torque characteristics of the experimental BLDC motor . . . . . . 63

5.1 Equivalent electrical circuit of the BLDC motor drive . . . . . . . 71

5.2 Ideal commutation signals, terminal and back-EMF voltages of the

BLDC motor . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 72

5.3 Schematic diagram of the proposed BLDC motor sensorless drive 74

5.4 Line voltage, Back-EMF and ZCD points of phase A of BLDC motor 80

5.5 Zero crossing points and the commutation signal of phase A . . . 80

5.6 Current, commutation signal and ZCD points of phase A . . . . . 81

5.7 Speed response of the BLDC motor and duty cycle values selected

by PI controller . . . . . . . . . . . . . . . . . . . . . . . . . . . . 82

5.8 State plane of digital PWM speed controller . . . . . . . . . . . . 82

5.9 Speed and torque characteristics of the BLDC motor during brake 83

5.10 Duty cycle values during the brake condition . . . . . . . . . . . . 84

5.11 State plane of digital PWM speed controller during the brake . . 85

5.12 Experimental speed response of the sensorless BLDC motor drive 86

5.13 Experimental speed response of the BLDC motor drive using sensors 87

5.14 Generated commutation signals by sensorless drive of BLDC motor 87

5.15 PWM switching signals applied to the upper side switches of VSI 88

5.16 Line voltage and commutation signal of the phase C of BLDC motor 89

5.17 The in-wheel BLDC motor set-up in a light weight EV . . . . . . 89

xv

LIST OF FIGURES

5.18 Line voltage and commutation signal of the in-wheel BLDC motor

at different operating condition of the light weight EV . . . . . . . 91

6.1 Overall BLDC motor drive model . . . . . . . . . . . . . . . . . . 95

6.2 BLDC motor output characteristics and VSI switching steps . . . 96

6.3 Schematic diagram of the four in-wheel drive EV model . . . . . . 102

6.4 EV speed under no fault condition . . . . . . . . . . . . . . . . . 103

6.5 Normal tire forces under no fault condition . . . . . . . . . . . . . 104

6.6 Speed responses of the BLDC motors under no fault condition . . 105

6.7 Torque responses of the BLDC motors under no fault condition . 105

6.8 EV speed under open circuit fault of switch S1 . . . . . . . . . . . 106

6.9 Normal tire forces under open circuit fault of switch S1 . . . . . . 107

6.10 Torque responses of the BLDC motors under open circuit fault of

switch S1 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 108

6.11 Speed responses of the BLDC motors under open circuit fault of

switch S1 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 108

6.12 Line voltage and Hall Effect signal of phase A of BLDC motor . . 111

6.13 Line voltages of BLDC motor during open circuit fault of switch S1 112

6.14 Line voltages of BLDC motor during open circuit fault of switch S2 113

6.15 The modified LV development board control drive of BLDC motor 115

6.16 Line voltages of BLDC motor under open circuit fault of switch S1 116

6.17 Line voltages of BLDC motor under open circuit fault of switch S2 117

6.18 Schematic diagram of the proposed four switches topology inverter 119

6.19 Schematic diagram of the proposed fault tolerant inverter with a

redundant leg . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 120

6.20 Speed and torque responses of BLDC motor under Ha = 0 fault

condition . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 123

6.21 Line voltages of BLDC motor under Ha = 0 fault condition . . . . 124

6.22 Line voltages of BLDC motor under Ha = 1 fault condition . . . . 126

6.23 Amplitude spectrum of the phase A line voltage of BLDC motor . 127

6.24 Half-bridge gate driver and inverter of LV development board . . 129

xvi

LIST OF FIGURES

6.25 Corresponding switching LED lights on the control board under

position sensor faults of phase A: (a) Open circuit fault (b) Short

circuit fault . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 130

6.26 Line voltages of the experimental BLDC motor under Ha = 0 fault 131

6.27 Line voltages of the experimental BLDC motor under Ha = 1 fault 132

6.28 Amplitude spectrum of the phase A line voltage of experimental

BLDC motor . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 133

6.29 Speed response of the fault tolerant controlled BLDC motor drive 135

B1 Block diagram of the induction motor drive model . . . . . . . . . 155

B2 Block diagram of the DC motor drive model . . . . . . . . . . . . 156

B3 Block diagram of the switched reluctance motor drive model . . . 156

B4 Block diagram of the BLDC motor drive model . . . . . . . . . . 157

F1 Speed responses of BLDC motor for different PWM switching modes160

F2 Torque responses of BLDC motor for different PWM switching

modes . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 161

F3 Torque responses of BLDC motor for different PWM switching

modes . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 161

F4 Line voltage of BLDC motor for different PWM switching modes . 162

F5 Duty cycle chosen by PI controller for different PWM switching

modes . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 163

F6 Torque responses of the BLDC motor under mechanical shocks for

different PWM switching modes . . . . . . . . . . . . . . . . . . . 164

F7 Duty cycle chosen by PI controller under mechanical shocks for


F8 Speed responses of the BLDC motor under inverter switch faults

for different PWM switching modes . . . . . . . . . . . . . . . . . 166

F9 Duty cycle chosen by PI controller under inverter switch faults for


G1 EV speed characteristics under open circuit fault of switch S2 . . 168

G2 Normal tire forces under open circuit fault of switch S2 . . . . . . 168

G3 Torque characteristics of the BLDC motors under open circuit fault

of switch S2 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 169

xvii

LIST OF FIGURES

G4 Speed characteristics of the BLDC motors under open circuit fault

of switch S2 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 169

xviii

List of Tables

2.1 Drive Train Specifications of the Electric Vehicles Available in the

World Market . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 11

2.2 Brushed DC Motor Specifications . . . . . . . . . . . . . . . . . . 21

2.3 Induction Motor Specifications . . . . . . . . . . . . . . . . . . . 22

2.4 Switched Reluctance Motor Specifications . . . . . . . . . . . . . 22

2.5 BLDC Motor Specifications . . . . . . . . . . . . . . . . . . . . . 22

2.6 Motors Comparison According to the In-wheel Motor Specifications 30

3.1 Hall Effect Signals and Inverter Switches Status of the BLDC Motor 40

3.2 Specifications of the BLDC Motor Model BLK423S . . . . . . . . 43

3.3 Specifications of the Experimental In-wheel BLDC Motor . . . . . 46

4.1 Three Phase Conduction Switching Mode for DTC of the BLDC

Motor . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 55

4.2 Specification of BLDC Motor Used in Simulation Model . . . . . . 56

4.3 Specifications of the Experimental BLDC Motor . . . . . . . . . . 62

6.1 Common Faults in the BLDC Motor Drive . . . . . . . . . . . . . 98

6.2 Specification of the Vehicle’s Body Used in the EV Model . . . . . 102

6.3 Simulation PSD Values for Open Circuit of S1 . . . . . . . . . . . 112

6.4 Simulation PSD Values for Open Circuit of S2 . . . . . . . . . . . 113

6.5 Proposed Knowledge Based Table for Inverter Switches Faults Di-

agnosis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 114

6.6 Experimental PSD Values for Open Circuit of S1 . . . . . . . . . 118

6.7 Experimental PSD Values for Open Circuit of S2 . . . . . . . . . 118

xix

LIST OF TABLES

6.8 Effect of the Various Sensor Faults on the Switching Signals of the

VSI . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 125

6.9 PSD Values for Ha = 0 Fault Condition . . . . . . . . . . . . . . . 127

6.10 PSD Values for Ha = 1 Fault Condition . . . . . . . . . . . . . . . 127

6.11 Proposed Knowledge Based Table for Position Sensor Faults Diag-

nosis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 128

6.12 PSD Values for Experimental Ha = 0 Fault Condition . . . . . . . 134

6.13 PSD Values for Experimental Ha = 1 Fault Condition . . . . . . . 134

xx

Chapter 1

Introduction

The idea of employing electric power instead of fossil fuels as motive energy

of vehicles is not new. Scientists and manufacturers have attempted to design an

Electric Vehicle (EV) since long time ago. Rodert Anderson had built the first

electric carriage in 1839 and David Salomon developed an electric car using a

light electric motor in 1870 [1]. Since then, the heavy electric batteries and poor

performance electric motors were the main concern. Interest on electric vehicles

reduced due to development of electric self-starters for the gasoline vehicles and

low price of oil, until early 1980’s when environmental concerns raised up [2].

Nowadays, hybrid electric vehicles are more popular than pure electric vehicles

due to the better range and lack of enough infrastructures for charging battery.

Conventional electric vehicles have a central electric motor that actuates two

or all four wheels of the vehicle [3]. In-wheel motor technology is of interest for

high performance electric vehicles by researchers and auto-mobile manufacturers

in recent years. However the in-wheel motor idea first introduced in 1884 by

Wellington Adams who have built and attached an electric motor directly in the

vehicle’s wheel through complicated gearings. In-wheel motor electric vehicles

employ motors embedded inside each wheel. Since in an in-wheel motor EV

individual control of each wheel is possible; better vehicle speed, torque and

acceleration control can be achieved. Using in-wheel motor technology improves

drive train efficiency, dynamic stability control and safety of electric vehicles [4][5].

1

1. Introduction

As mentioned earlier, poor performance of the electric motors has been of

concern by researchers and various electric motor types have been used in electric

vehicles so far. There is always an important question, what is the most suitable

electric motor for electric vehicles? The answer to this question depends highly

on the type of the EV application. The scope of this thesis is on high performance

pure electric vehicles comparable with other gasoline vehicles. As there is no com-

prehensive comparison on electric motors for the high performance electric vehicle

application; in this thesis various common motors such as brushed DC, induc-

tion, switched reluctance and permanent magnet BLDC motor are compared in

the context of an in-wheel motor vehicle. In Chapter 2, advantages and disadvan-

tages of each motor are discussed according to in-wheel motor requirements and

their output characteristics such as speed and torque are compared under same

operating condition. As a result of this study, the BLDC motor is introduced as

the most suitable in-wheel motor for high performance electric vehicles.

BLDC motors were first introduced by T.G. Wilson and P.H. Trickey in 1962

for some specific low power applications and named as “a DC machine with solid

state commutation” [6]. Higher power BLDC motors came on the market after

the development of the high power transistors and permanent magnet materials.

The first high power BLDC motor (50 horsepower or more) was designed by

Robert E. Lordo at Powertec Industrial Corporation in the late 1980s [6].

This thesis focuses on the three phases, star connected BLDC motors. Control

of the BLDC motor depends on position of the permanent magnet rotor. Elec-

tronic commutation increases complexity of the BLDC motor drives compared

to the other motors. Precise simulation model of the BLDC motor is required

to study behaviour of the motor for different control algorithms. Therefore a

model of the 3 phases, star connected BLDC motor drive with ideal trapezoidal

back-EMF waveforms is presented in Chapter 3. The mathematical model of

the BLDC motor and principle of its operation are also discussed in details. To

control speed of the BLDC motor, a digital Pulse width modulation controller is

implemented in the model. The BLDC motor drive model is validated through

experimental results.

2

1. Introduction

There are two major commutation techniques for the BLDC motors based on

the rotor position detection method. Hall Effect sensors are generally mounted

inside the BLDC motor to detect the rotor position in sensor mode. Sensor-

less control schemes are generally based on back-EMF detection of the unexcited

phase and flux linkage trajectory of the BLDC motor [7]. Simple BLDC motor

construction, low manufacturing cost and less maintenance need are main ad-

vantages of sensorless control techniques. However sensorless control algorithms

of the BLDC motor are much more complex than the conventional switching

techniques [8].

Torque ripple is one of the main limitations of the BLDC motor in EV appli-

cation. Cogging torque, reluctance torque and mutual torque are various electric

torque components in the BLDC motor [9]. Cogging torque is the result of interac-

tion between the permanent magnet rotor magnetic flux and variable permeance

of the air gap due to the geometry of stator slots. Cogging torque, distortion of

the trapezoidal distribution of the magnetic flux in the air gap and differences

between permeances of the air gap in the d and q axes are the main sources of

the torque ripples in the BLDC motor [10]. Cogging torque is a more dominant

component at low speeds and fortunately its effect is filtered by the motor inertia

at high speeds.

In Chapter 4, direct torque control switching technique of the BLDC motor

in constant torque region below the rated speed is discussed. Direct torque con-

trol technique is a flux linkage based sensorless method with no position sensors

used to detect the rotor position. In this technique, hysteresis controllers are

implemented to limit the torque error level. The simulation results show effective

control of the produced electric torque of the BLDC motor. Hysteresis controller

effectively limits the torque ripple amplitude of the BLDC motor compared to

the conventional Hall Effect switching techniques. Direct toque controlled BLDC

motor is also tested under sudden change of the load torque. Dynamic torque

response of the motor is much faster than the conventional reported controllers.

Direct torque control of the in-wheel motors increase efficiency and safety of elec-

tric vehicles.

3

1. Introduction

Although commutation of the BLDC motor is much simpler by using Hall

Effect position sensors but it has some critical drawbacks such as regular need of

the motor maintenance, high electromagnetic interference radiation and limita-

tions due to the temperature sensitivity of the in-built sensors [11]. Back-EMF

based sensorless drives of the BLDC motor are widely used in industrial appli-

cations. Back-EMF zero crossing detection, back-EMF integration, back-EMF

harmonic analysis are examples of the back-EMF based sensorless technique of

the BLDC motor [7]. In Chapter 5, various back-EMF based sensorless drives of

the BLDC motor are discussed in details and their advantages and disadvantages

are highlighted.

In this thesis a novel back-EMF based sensorless control algorithm of the

BLDC motor is proposed. BLDC motor is commutated through back-EMF zero

crossing detection of one phase of the motor. Sensing circuitry, noise susceptibility

and cost of the sensorless BLDC motor drives are reduced by the proposed tech-

nique. Digital pulse width modulation technique (PWM) using a Proportional

Integral (PI) controller is employed to control the speed of the BLDC motor.

Details of the PWM speed controller are presented in the Chapter 5.

Stable performance of in-wheel motors is significant in overall safety of the

electric vehicles. Stability of the proposed back-EMF based sensorless BLDC

motor drive using digital PWM technique is studied by Lyapunov second method.

This analysis results in deriving a new equation to calculate the ideal duty cycle

value of the PWM controller that keeps the BLDC motor stable at the desired

speed. Effect of the load torque is also considered in stability analysis.

Accuracy of the proposed sensorless technique to control the BLDC motor and

correctness of the introduced novel equation for stability analysis of the motor

drive are investigated through simulation and experiment. Good agreements

between simulation and experimental results validate correctness of the proposed

sensorless technique and stability analysis condition of the BLDC motor.

Safety is the most important factor in automotive applications. Safety of elec-

tric vehicles is highly dependant on the reliability and robustness of the in-wheel

motors as any malfunction or fault in drive train of electric vehicles may result in

a fatal accident. Fault tolerant control systems (FTCS’s) are one of the effective

solutions to increase robustness of the electric motors. FTCS’s are designed to

4

1. Introduction

detect and isolate various faults and apply appropriate remedial actions to keep

the stable performance of the motor in post-fault condition [12].

Hazard conditions in the drive train of an electric vehicle can be divided into

the electrical and the mechanical faults. In a BLDC motor drive faults may

happen in the stator, the rotor or the inverter. Common faults in a BLDC motor

drive are analysed and two fault diagnosis systems are proposed in Chapter 6; one

for the inverter open circuit switch faults and the other for Hall Effect position

sensors failure in the BLDC motor drives. A four in-wheel drive electric vehicle

using BLDC motors is modelled in Simulink to analyse the effect of inverter open

circuit switch faults on the EV performance. Dynamic parameters of the electric

vehicle such as speed, vertical force on tires due to the vehicle’s body, speed

and torque characteristics of each in-wheel motor are compared and discussed in

details under healthy and faulty conditions. Simulation results show that the EV

performance is not stable and proves the need of FTCS’s for in-wheel motors.

Signal analysis based, model based and knowledge based methods are three

main fault diagnosis algorithms for electric motors [13]. Advantages and disad-

vantages of each fault diagnosis method are discussed in Chapter 6. The BLDC

motor behaviour is also studied under inverter open circuit as well as Hall Effect

sensors faults through a validated simulation model. Inverter open circuit switch

faults and position sensors failure effect directly on the output voltage of the vari-

able source inverter (VSI). In Chapter 6, reported fault tolerant control systems

for the mentioned faults of the BLDC motor are presented and their merits and

demerits are also highlighted.

Three phase line voltages of the BLDC motor are analysed and discussed in

details under fault condition. Two fault diagnosis systems are proposed based on

Discrete Fourier Transform (DFT) analysis of line voltages of the BLDC motor.

The proposed fault diagnosis systems are categorised in knowledge based systems

where the knowledge is gathered by analysing the line voltages under fault condi-

tion through the validated simulation model of the BLDC motor. The proposed

fault diagnosis algorithms are not only capable of detecting inverter switch and

position sensor faults, but also can identify faulty switches and faulty sensors.

The developed knowledge based fault diagnosis systems are validated through

experimental data too.

5

1. Introduction

In this study, suitable fault tolerant inverter drives of the BLDC motor for EV

applications are discussed and a fault tolerant control VSI with a redundant leg is

recommended for inverter open circuit faults. A novel technique is introduced to

generate the commutation signal of the faulty position sensor based on electrical

delays between commutation signals in the BLDC motors. The proposed fault

tolerant control systems of the BLDC motor are simple, fast and do not need

complex calculations.

At a glance, this thesis is focused on improving control drives of a three

phase BLDC motor for electric vehicle application with novelty in controllability,

safety and fault tolerance. First of all advantages of the BLDC motor over other

motor types for in-wheel motor application are discussed in Chapter 2. Modelling

of the BLDC motor with ideal trapezoidal back-EMF and principle of the motor

operation are presented in Chapter 3. Various sensorless control algorithms of the

BLDC motor, direct torque and back-EMF based sensorless control techniques,

are proposed in Chapters 4 and 5. A novel stability analysis condition for PWM

speed controllers of the BLDC motor is also presented in Chapter 5. Finally

in Chapter 6, simple fault tolerant control techniques for inverter switch faults

and position sensor faults in the BLDC motor drives are proposed. This chapter

presents a very short introduction of the thesis and complete literature reviews

are given inside the chapters.

6

Chapter 2

Selection of a Suitable Motor for

Electric Vehicles

2.1 Chapter Overview

One way to limit the emission of greenhouse gases to the atmosphere is to

use Electric Vehicles. Electric vehicles are of interest to most of the automotive

manufacturers due to their high efficiency and zero greenhouse gas emissions.

Different types of electrical motors have been used as the propulsion system of

electric vehicles so far. However there is not an overall comparison study that an-

swers clearly which electric motor is the most suitable choice for electric vehicle’s

drive train. In this chapter a brushed DC motor, an Induction Motor (IM), a

Switched Reluctance Motor (SRM) and a permanent magnet Brushless DC mo-

tor (BLDC) are simulated and their output characteristics are compared under

normal and critical conditions with respect to in-wheel motor technology require-

ments. Merits and demerits of each electric motor are highlighted, and BLDC

motor is recommended as the most suitable electric motor for high performance

electric vehicles.

7

2. Selection of a Suitable Motor for Electric Vehicles

2.2 Introduction

Vehicles with an Internal Combustion Engine (ICE) and conventionally trans-

formed/retrofitted electrical vehicles have a central drive train propelling two

rear, front or all four wheels of the vehicle [3]. In-wheel motor technology uses

separate motors mounted inside the tire to propel an EV. In-wheel motors have

been a focus for research in the last decade. Applying in-wheel motor technology

increases the overall safety and efficiency of electric vehicles [5]. Better dynamic

stability control of electric vehicles is possible by using four in-wheel motors [4].

This approach improves controllability of each individual wheel and decreases

the total chassis weight [14]. It is possible to achieve better acceleration, torque

control and regenerative braking in electric vehicles by applying the in-wheel mo-

tor technology. Some of the major requirements of a high performance electric

vehicle are summarized as follows [15]:

• being safe and causing no environmental hazards;

• being autonomous;

• having a good mileage (a minimum range between charges of at least 50

miles when loaded with two 166-pound occupants and operated at a con-

stant 45 mph1);

• having a quick charging time (The battery charger shall be capable of

recharging the main propulsion battery to a state of full charge from any

possible state of discharge in less than 12 hours1);

• having acceleration of 10-15 seconds for the speed range of 0 to 100 Km/h;

• being able to be driven up a 5 to 10 percent ramp at the legal speed under

full load condition (a minimum payload of 400 pounds1).

Nowadays conventional hydraulic, pneumatic and mechanical control systems

are being replaced by electronic control systems, by-wire technologies, electrome-

chanical actuators, and human machine interfaces in the automotive industry

1EV America Technical Specification, Effective from 1 Oct 1999, is given in appendix B.

8


[16]. An Intelligent Electronically Controlled Electric Vehicle (IECEV) is be-

ing targeted by implementing By-Wire Steering (BWS) system, Brake by-Wire

(BbW) system, Dynamic Radar Cruise Control (DRCC) system, Pre-Collision

Safety System (PCS), Intelligent Parking Assist System (IPAS), Electronic Sta-

bility Control (ESC), Traction Control (TRAC) etc., in an in-wheel motor elec-

tric vehicle. The reputed car companies such as BMW, Toyota, Lexus, Mercedes

Benz, Land Rover, Volkswagen and General Motors have used various by-wire

systems in their vehicles. Mercedes Benz and Toyota are using BbW systems

in their vehicles. The BWS systems are also currently used in electric forklifts,

stock pickers and some tractors [17]. A schematic diagram of a four-wheel drive

IECEV is shown in Figure 2.1. Integration of an in-wheel motor and its intelli-

gent controller results in a drive train for the electric vehicles which is safer, more

efficient and reliable [18].

Figure 2.1: Four wheel drive train of an IECEV

9


In-wheel motor requirements are discussed in the next section. Advantages

and disadvantages of brushed DC motors, induction motors, switched reluctance

motors and BLDC motors are discussed according to the in-wheel motor require-

ments in following sections. Simulation models of the motors are tested under

various (normal and critical) operating conditions. Presented comparison simu-

lation results in this chapter have been published by Tashakori et al. [3][5].

2.3 The Drive Train of Electric Vehicles

Drive train specifications of the electric vehicles available in the world mar-

ket are given in alphabetical order in Table 2.11. As can be seen, BLDC and

induction motors are the most popular from the manufacturer’s point of view.

Companies such as Mercedes-Benz, Lightning Car and ECOmove have designed

in-wheel motor electric vehicles in the recent years. In-wheel motor technology is

considered the most suitable solution for the high performance electric vehicles

driving force system nowadays.

It is important to choose the correct in-wheel motor to build an efficient and

reliable IECEV [3]. An overall comparison of electric motors is needed to select an

appropriate machine to fulfil the in-wheel motor technology requirements. Some

of the most important requirements of the in-wheel motors are [19]:

• high torque at low speeds;

• high torque/power to size ratio;

• constant power in wide speed range;

• high efficiency;

• high dynamic response (fast torque and speed response);

• accurate electronic controllability;

• robustness and reliability of the motor and its drive;

• low Electro Magnetic Interface (EMI) noise susceptibility

• reasonable cost of production.

1Reference links are given in Appendix A.

10

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There are three main noise sources in electrical motors: 1- Mechanical noise

due to shaft misalignment, rotor imbalance or bearing problems; 2- Aerodynamic

noise due to internal or external fans; 3- Electromagnetic noise produced by the

air gap magnetic flux waves [20]. Most of the electronic control systems (such as

motor control drive, electronic stability control system and so on) are compacted

and placed near the tire due to the confined space in in-wheel motor electric vehi-

cles. Therefore in-wheel motor EMI noise may cause malfunction or performance

degradation in the adjacent electronic systems on board and nearby vehicles, for

example in a traffic jam. Common mode currents noise, differential noise, radi-

ated noise and bearing noise are various types of EMI noise which are generated

by high frequency pulse width modulation (PWM) switching and surge voltage

appearing on motor terminals [21]. Implementation of noise control methods

increases complexity of motor controllers and is quite difficult in the electric ve-

hicles, because defining of the EMI noise propagation route is complicated due to

high density packaging [22]. Therefore noise susceptibility of the in-wheel motors

is a critical factor in overall performance of the EV drive train.

In-wheel motors must also be capable of the frequent start, stop and reverse

rotation with maximum output electric torque. A high performance electric vehi-

cle should be able to start from halt position and repeatedly accelerate in a short

time to overcome the inertia of the load [5]. However the average operational

efficiency of a torque converter in vehicles during city traffic conditions is less

than 60% [23].

2.3.1 Conventional AC and DC Motors

Selection of a suitable in-wheel motor for the high performance EV drive train

demands considerations on power, voltage and current handling, torque/speed

characteristics, power to size ratio, noise susceptibility, maintenance and control-

lability of motor. Since conventional AC and DC motors are discussed enough in

the literature, there is no need to discuss their structural and operational char-

acteristics in this chapter. However in this section, their merits and demerits are

discussed according to the requirements of in-wheel motors.

12


Angular velocity difference between the produced flux of stator and flux of

rotor causes slip in the conventional AC (squirrel cage induction) motors. The

rotor speed always lags the angular velocity of the stator magnetic field by slip

speed. Slip is directly proportional to load torque in the induction motors. Slip

causes vibrations of the induction motor at the starting time which is not suit-

able for the in-wheel motor technology. AC induction motors generally produce

lower torque, draw higher initial current and have slip as compared to the con-

ventional DC (brushed DC) motors that experience no slip [3]. As the speed of

the induction motor approaches the rated speed, the current and slip decrease

and the electrical torque increases. On the other hand, in the DC motor torque

is inversely proportional to the angular velocity of the rotor. Therefore DC mo-

tors produce higher electric torque at low speeds that is essential for the in-wheel

motors.

The output power to size ratio of the in-wheel motors is a significant factor

due to the space limitations inside the tire. The heat produced by armature

winding of the DC motors is dissipated in the air gap and increases the air gap

temperature. Therefore DC motors have a moderate or a low output power to

size ratio. Since both the stator and the rotor of induction motors have windings,

size of the motor is large and output power to size ratio of the motor is low [3].

Therefore both conventional DC and AC motors do not have a suitable output

power to size ratio.

Extended speed range of the in-wheel motors with constant power is an impor-

tant factor in the EV application. Torque of DC motors is decreased effectively

over the base speed; therefore they have a limited extended speed range. Break

down flux weakening speed of the induction motors is almost two times of their

rated speed [23]. A specific design of a spindle induction motor with a field orien-

tation control drive can be run up to five times of the rated speed [24], however

construction complexity and size of the motor is increased which is not suitable

for the in-wheel motor application.

Noise in the DC motors is mostly due to PWM switching, therefore filters are

used to smooth the average voltage and reduce motor noise. PWM switching,

surge voltage and aerodynamic noise, due to the internal fan, are the main noise

sources in induction motors. Modulation techniques of the two level inverter have

13


also a crucial effect in noise emission of the induction motors. Randomize Space

Vector Modulation (RSVM) technique increases acoustic noise, whereas the Off-

line Optimized Pulse Pattern (OOPP) method minimizes the current harmonics

and reduce noise emission [25].

Controllers of the DC motors are much simpler and cheaper compared to

that of the induction motor controllers. Complex control techniques and poor

dynamic characteristics of the induction motors at starting time make them an

unsuitable choice for the EV drive train application. DC motors show better

dynamic characteristics at starting time but the existence of brushes increase

the need of motor maintenance, reduces efficiency, reliability and the Ingress

Protection (IP) rating of the in-wheel motors [3].

2.3.2 Switched Reluctance Motors

Switched reluctance motors, also known as the variable reluctance motors, are

type of synchronous motors. However in a comparison to the regular synchronous

motors it has no field winding, slip ring and brushes [26]. Reluctance motors were

first built nearly 200 years ago. Davidson’s motor, one of the most well known

reluctance motors, was built in 1839 [27]. Structure of the SR motors is similar

to the BLDC motors, though it has a ferromagnetic rotor instead of a permanent

magnet rotor. Therefore a SR motor construction cost is cheaper than that of a

BLDC motor. As shown in Figure 2.2, different switched reluctance geometries

are possible by changing the number of stator phases, number of stator poles and

number rotor poles [26].

Switched reluctance motors have electronic commutation control system which

provides sequential pulses to the stator windings [3]. Each phase of the SR

motor is independent physically and electrically from the other motor phases.

Therefore direction of the produced torque is independent of the current direction

and depends on the rotor position and the sequence of energized phases [26]. By

energizing the stator windings, the rotor moves into the alignment with the stator

poles to minimize the reluctance in the air gap. Inductance of the stator windings

increases when the stator and the rotor poles are aligned. Positive electric torque

is produced when the gradient of the inductance is positive [27].

14


Figure 2.2: Various switched reluctance motor geometries

Desirable features of the switched reluctance motor that make them attrac-

tive for traction applications are: simple and rugged construction, high speed

operation, wide speed range with constant power, hazard free operation, high re-

liability and low manufacturing cost [23]. The major drawbacks of the SR motors

are large torque ripples, rotor position detection, low power factor and acoustic

noise [28]. Using PWM control technique reduces acoustic noise of the switched

reluctance motors compared to the hysteresis current controllers [29]. Five and

six phase switched reluctance motors produce lesser torque ripples, however their

control techniques are more complex. High amplitude torque ripples and noise

susceptibility of the switched reluctance motor drives are not suitable for the in-

wheel motor applications. Efficiency of the switched reluctance motors is similar

to the induction motors and is lower than that of the BLDC motors [30].

15


2.3.3 BLDC Motors

Permanent magnet synchronous motors have received a considerable attention

in the industrial application since 1970’s. Nowadays they are used in various ap-

plications such as automotive, aerospace, medical equipment, industrial automa-

tion and instrumentation. Permanent magnet synchronous motors are mainly

divided into two various types based on their back-EMF waveform; the one with

a sinusoidal-wave back-EMF that is called Permanent Magnet Synchronous AC

Motor (PMSM) and the other with a trapezoidal-wave back-EMF that is called

Permanent Magnet Brushless DC (BLDC) Motors. A BLDC motor with the

trapezoidal back-EMF produces larger torque compared to a PMSM with the

sinusoidal back-EMF [31]. The focus of this thesis is on the three phase star

connected BLDC motors. A schematic diagram of a two pole BLDC motor and

its drive system is shown in Figure 2.3 [12].

BLDC motors are a novel type of the conventional DC motors where com-

mutation is done electronically, not by brushes. Therefore a BLDC motor needs

less maintenance, has lower noise susceptibility and lesser power dissipation in

the air gap compared to a brushed DC motor due to absence of the brushes.

Permanent magnet rotors can vary from two pole pairs to eight pole pairs [32].

Magnet material is chosen with respect to the required magnetic field density

in the rotor. Ferrite magnets are usually used to make the permanent magnet

rotor of the BLDC motor, however they have the disadvantage of low flux den-

sity. In contrast, alloy materials such as Neodymium (Nd), Samarium Cobalt

(SmCo), Ferrite and Boron (NdFeB) have higher magnetic density. Hence these

alloy magnets produce more torque for the same volume compared to the ferrite

magnets; therefore they improve power to size ratio of the BLDC motor which is

more suitable for the in-wheel motors [32].

BLDC motor needs a complex control algorithm due to the electronic com-

mutation that is done according to the exact position of the permanent magnet

rotor. There are two algorithms for rotor detection; one method that uses sen-

sors and the other does not that is called sensorless [33]. Hall Effect sensors

are normally mounted on the non-rotating end inside the BLDC motor with 120

electrical degree phase difference at the constant position to detect rotor angle.

16


Figure 2.3: Schematic diagram of a two pole BLDC motor drive

17


Optical encoders are used as position sensors for high resolution applications.

The internal view of a BLDC motor is shown in Figure 2.4 [32].

Figure 2.4: The internal view of a BLDC motor

Hall Effect signals are generated according to the permanent magnet rotor

position. These signals are decoded in controller to choose the correct voltage

space vector that must be fed to the three phase Voltage Source Inverter drive of

the BLDC motor. Ideal back-EMF voltage, current, commutation signals and on

switches of the VSI drive of the BLDC motor are shown in Figure 2.5 [34].

It is a fact that noise susceptibility of the BLDC motors is less than the other

motor types, specifically the SR motors. Sound pressure (acoustic noise) of a

BLDC motor and a SR motor are measured experimentally and compared for the

same working conditions in the context of electric brakes [29]. Results show that

acoustic noise of the SR motor is 6 dB-A more than the BLDC motor at 1000

RPM speed under 0.65 N.m load torque. The sound pressure levels of the BLDC

and SR motors at 5000 RPM speed under 0.2 N.m load are measured 48 dB-A

and 69 dB-A respectively [29]. Therefore acoustic noise of the BLDC motor is

much higher than the SR motor at high speed operating condition.

18


Figure 2.5: Ideal current, back-EMF and commutation signals of BLDC motor

Manufacturing costs of the BLDC motor are higher than the other motor

types due to the permanent magnet material price in the world market. The

other disadvantage of the BLDC motors is that their extended speed range with

constant power is less than twice the synchronous speed due to the limited field

weakening capability [35]. An additional field winding can be used to solve this

problem in a way that the field produced by the permanent magnet rotor is

weakened in the extended constant-power speed region by controlling the DC

19


field current. These motors are called permanent magnet hybrid motor and their

maximum speed is up to four times of the synchronous speed [23]. However, low

efficiency of these motors at high speeds and complex structure are their main

drawbacks. Using a multi-gear transmission can solve the extended constant

power speed range limitation of the BLDC motors.

High efficiency, high speed ranges and high dynamic response due to a per-

manent magnet (low inertia) rotor are the immediate advantages of the BLDC

motor for in-wheel motor technology application [5]. The high output power

to size ratio of the BLDC motor, due to absence of the field windings, makes

it suitable as an in-wheel motor where the space and the weight are significant

considerations. The absence of brushes also effectively reduces the maintenance

needs of the BLDC motors that is an advantage for the EV applications. Noise-

less operation of the BLDC motor also makes it more convenient to design the

integral in-wheel motors [18].

2.4 Motor Comparison

Choosing a suitable electric motor for the in-wheel drive train of electric vehi-

cles is an important parameter which affects overall performance of the vehicle.

Appreciated research works have been reported on the motor selection for hy-

brid and electric vehicles [19][23][30][31][35]. Some of the reported research works

have suggested that the switched reluctance motor is a better choice for the hy-

brid electric vehicle (HEV) and EV applications [19][23][30]. Wider speed range

with constant power of the SR motors compared to the BLDC motors is the

most important discussed reason for recommending the SR motors for HEV and

EV applications [23][30]. Brushed DC, induction, BLDC and switched reluc-

tance motors are compared based on efficiency, weight and manufacturing cost

and the SR motors are recommended by Xue et al. [19] due to the high cost

and difficulties in accessing magnetic materials. Torque ripples reduction con-

trol techniques are suggested to overcome the main drawback of the SR motors.

Although the extended speed range of the BLDC motors is less than SR mo-

tors, applying a multi-gear transmission can solve the problem. Brushed DC,

induction, BLDC and switched reluctance motors are compared based on power

20


density, efficiency, controllability, reliability, technological maturity and cost and

the induction motors are recommended for HEV application [35]. The BLDC

motor is recommended for the EV drives due to its high power density, efficiency

and smooth torque response [31].

All the reported comparisons are based on literature review of the motor

specifications and there is no performance analysis and comparison based on sim-

ulation or experimental results. In this chapter a DC motor, an induction motor,

a switched reluctance motor, a BLDC motor and their controllers are modelled.

Details of the motor models are presented in Appendix A. Their output character-

istics such as speed and torque are compared under the same operation conditions

(the same input power, load torque and reference speed of the controllers).

Simulation results are discussed under normal and critical operating condi-

tions. Critical condition analysis is important with respect to safety of the electric

vehicle. The term normal condition is defined as the normal operation of the EV

with constant speed on a flat, uphill or downhill roads (load torque is constant).

Critical conditions are considered as the operation of the EV under electrical

faults and mechanical shocks. The electrical faults may happen in the electric

motor or its controller and the mechanical shocks on the in-wheel motors may

occur due to various road conditions, sudden braking, or sudden change of vehi-

cle direction [5]. Specifications of various electric motors used in the simulation

models are presented in Tables 2.2, 2.3, 2.4, 2.5.

Table 2.2: Brushed DC Motor Specifications

Description Value Unit

DC Voltage 400 V

Resistance 1.78 Ω

Inductance 0.21 H

Inertia 0.08 kg-m2

Damping Ratio 0.004 N.m.s

21


Table 2.3: Induction Motor Specifications


DC Voltage 400 V

Phase Resistance 0.73 Ω

Phase Inductance 0.003 H

Inertia 0.034 kg-m2


Poles 4 -

Table 2.4: Switched Reluctance Motor Specifications


DC Voltage 400 V

Phase Resistance 2 Ω

Unaligned Inductance 0.67×10−3 H

Aligned Inductance 23.6×10−3 H

Inertia 0.008 kg-m2


Poles 6/4 -

Table 2.5: BLDC Motor Specifications


DC Voltage 400 V

Phase Resistance 2 Ω

Phase Inductance 8×10−3 H

Inertia 0.8×10−3 kg-m2


Torque constant 1.4 N.m/A

Poles 8 -

22


2.4.1 Performance Comparison of the Motors under Nor-

mal Condition

Simulation models of the brushed DC motor, the induction motor, the switched

reluctance motor and the BLDC motor are tested for 1500 RPM reference speed

of the controller under 10 N.m load torque. Transient speed responses of the

motors are plotted and shown in Figure 2.6 [5].

Figure 2.6: Transient speed responses of the motors under normal condition

As can be seen in the figure, speed response of the BLDC motor is much faster

than the other motor types. Higher dynamic response of the BLDC motor is due

to its permanent magnet (low inertia) rotor. Fast dynamic response is one of the

most important requirements of the in-wheel motors. Simulation results show

that the DC motor has the second fastest dynamic response and the switched

reluctance motor has the slowest dynamic response among the motors. The in-

duction motor has the most speed oscillations in transient time though it has an

acceptable dynamic response [5].

Transient torque responses of the motors are plotted and shown in Figure 2.7

[5]. The DC motor has the highest initial torque value and the BLDC has the

23


fastest torque response. Torque response of the BLDC motor is also approached

the final value, the load torque, much faster than the other motors. Therefore

as can be seen the BLDC motor has a wider speed range with constant torque

below the rated speed. Torque fluctuation of the induction motor in transient

time can be seen in the figure. Slip of the induction motor at low speeds acts

an essential role in output characteristics of the motor in transient condition.

Slip is dependent on supply voltage frequency, rotor resistance and torque load.

Change of voltage frequency results in slip variations and torque oscillation in

the induction motor in transient condition. As can be seen in the figure, torque

ripple is one of the major drawbacks of a switched reluctance motor. Torque

ripple results in fluctuation of delivered output power from the motor to the tires

which is not acceptable for an in-wheel motor EV. Low efficiency and low speed

ranges are the major drawbacks of the conventional DC motors for the in-wheel

motor application even though it has the highest initial torque value and high

dynamic speed response. Therefore the BLDC motor is the most suitable choice

as an in-wheel motor according to torque response analysis [3][5].

Figure 2.7: Transient torque responses of the motors under normal condition

24


Transient torque/speed characteristics of the brushed DC motor, the induction

motor, the switched reluctance motor and the BLDC motor from the halt position

up to the controller reference speed (1500 RPM) are shown in Figure 2.8 [5]. It

is shown that the BLDC motor has the minimum torque oscillation and the

switched reluctance motor has the maximum torque oscillation in the transient

time. Torque fluctuation of the induction and the switched reluctance motors

during transient condition can be seen in the figure. Output electric torque of the

BLDC motor reaches the load torque when the speed of the motor passes the 53.3

percent of its final value. Therefore the BLDC motor has a better torque/speed

characteristics for in-wheel application compared to the other motors [3][5]. The

BLDC motor has the best overall output characteristics with respect to the in-

wheel technology requirements during the normal operating condition.

Figure 2.8: Transient torque/speed characteristics of the motors

2.4.2 Performance Comparison of the Motors under Crit-

ical Condition

Safety of the vehicle’s passengers is the most significant issue in automobile

industry. The most of research topics in the automotive industry are concentrated

25


on developing intelligent systems to improve safety, efficiency and convenience

of driver and passengers in the vehicle. Since electric motors are used as the

propulsion system for electric vehicles, therefore robustness and reliability of the

electric motor drive in abnormal conditions play a critical role in overall safety

and performance of the EV.

Electrical motors are subjected to various types of fault inside the motor or

its drive. In-wheel motors are also subjected to the mechanical shocks (sudden

change of load torque and vibration) due to the operational and environmental

conditions. Therefore an in-wheel motor and its controller must be reliable during

the mechanical shocks, the transient electric faults and at initial times of major

electric faults until fault tolerant control systems make the appropriate decisions.

In this chapter different fault conditions are modelled and applied to the respec-

tive electric motor models and the behaviour of the motors are compared during

the critical operating condition.

2.4.2.1 Transient Electric Faults

Various electric faults may happen in an electric motor or its control drive. In

this section behaviour of the induction, switched reluctance and BLDC motors

is studied during a transient three phase to ground fault of the line voltages. A

single phase to ground electric fault is applied for the DC motor. The fault is

applied at t = 0.4 s for duration of 0.1 s while the motors are running in the

normal condition at 1500 RPM reference speed under 10 N.m load torque. Speed

characteristics of the motors during the transient electrical fault condition are

plotted and shown in Figure 2.9 [5].

Induction and switched reluctance motors have more stable speed responses

during the transient electric fault. The DC motor becomes completely unstable

during the fault. The BLDC motor has a fast speed response to the fault due to

the permanent magnet rotor and high dynamic response characteristics. BLDC

motor speed is also reduced remarkably during transient electric fault. Torque

characteristics of the motors during the transient electrical fault condition are

plotted and shown in Figure 2.10 [5].

26


Figure 2.9: Speed responses of the motors under same transient electrical fault

Figure 2.10: Torque responses of the motors under same transient electrical fault

27


As can be seen from the figure, the DC motor behaves as a generator during

the transient fault. Torque ripples amplitude of the switched reluctance motor

increases drastically during fault condition. Induction motors and BLDC motors

have the least torque fluctuation of all. Since delivered power to the wheels is

directly proportional to the speed response and the produced electric torque of

the motor, the induction motor and the BLDC motor have desired behaviour

during transient electric fault in order.

2.4.2.2 Vibration and Mechanical Shocks

Abrupt changes of the load torque on an electric motor are called mechanical

shocks. Mechanical shocks may be applied to the in-wheel motor due to sudden

changes of the road condition, brakes or changes of the vehicle direction by driver.

Sudden 30% changes of the load torque are applied to the simulation models of

motors to study their behaviour during the mechanical shocks. Speed responses

of the motors under same mechanical shocks are plotted and shown in Figure 2.11

[5].

Figure 2.11: Speed responses of the motors under same mechanical shocks

28


As can be seen from the figure, the induction motor has the least speed vari-

ations where the BLDC motor has the most speed changes due to high dynamic

response especially at the exact times of load change. However the BLDC motor

follows the reference speed after the load change much faster than other motors.

The DC and switched reluctance motors have almost the same speed response to

the mechanical shocks. However low dynamic response of the switched reluctance

motor prevents the motor from following the reference speed quickly enough after

the load change. Torque responses of the motors under same mechanical shocks

are plotted and shown in Figure 2.12 [5].

Figure 2.12: Torque responses of the motors under same mechanical shocks

As can be seen torque ripples of the switched reluctance motor are increased

at the times of load change. Torque of the BLDC motor approaches the load

torque much faster than the other motors. In contrast, the DC motor shows

the slowest torque response among all. Induction motor torque ripples also are

increased due to the mechanical shocks; however its dynamic response is not

quick enough. Comparison discussions according to the in-wheel technology and

motor specifications are summarized in Table 2.6. Numeric values from 1 to 5

are assigned in order to the terms very bad, bad, moderate, good and very good

29


according to the EV application requirements. As it can be seen in the table, the

BLDC motor got the highest points and is the well suitable choice for drive train

of high performance electric vehicles.

Table 2.6: Motors Comparison According to the In-wheel Motor Specifications

Features BLDC motor SR motor Induction motor DC motor

Commutation electronic electronic - brushes

Slip - - applicable -

Efficiency 5 3 3 2

High rated speed 5 5 3 3

Extended constant 3 5 4 2

power speed range

Control complexity 2 2 3 5

Torque/Speed 5 3 4 3

Dynamic response 5 2 3 4

Power/size ratio 4 4 3 3

Operation life time 5 5 3 2

Maintenance needs 5 5 4 2

Noise susceptibility 5 2 3 3

Speed during fault 3 5 4 2

Torque during fault 4 2 4 1

Speed during 3 4 5 4

mechanical shocks

Torque during 4 2 3 3

mechanical shocks

Manufacturing Cost 2 4 5 5

Total 60 53 54 44

30


2.5 Conclusion

There is a growing interest in electric vehicles for future transportation due to

its zero carbon emissions and high efficiency. Correct electric motor selection for

propulsion system of the high performance electric vehicle is essential to attain

the maximum safety and efficiency. In-wheel motor technology as a propulsion

system in electric vehicles is one of the main research interests in automotive

industry. In this chapter behaviour of the brushed DC, induction, BLDC and

switched reluctance motors are studied and compared with respect to the in-

wheel motor requirements under normal and critical (the electric fault and the

abrupt mechanical shocks) conditions in order to select the most suitable electric

motor for electric vehicles. The BLDC motor has the most suitable characteristics

during normal condition operation according to the in-wheel motor requirements.

Better torque/speed characteristics, higher efficiency, higher output power to size

ratio, higher dynamic response, higher operating life, lower maintenance, noiseless

operation and higher speed ranges are advantages of the BLDC motor in normal

operation over all the other motors according to the discussed comparison results.

Comparison results show that the induction motor is the most robust among

all the other motors during the critical condition; however speed range limitations,

low efficiency of motor at high speed, slow dynamic response and slip of the

motor at low speed make it a poor choice for high performance electric vehicles.

The switched reluctance motor has also similar speed characteristics to induction

motors during critical condition; however its torque ripple amplitude shows a

remarkable increase. Low efficiency, high amplitude torque ripples and noise

susceptibility in the switched reluctance motor drive are its main drawbacks which

make it unsuitable for the in-wheel motor application. Output characteristics of

the DC motors during electric fault are the worst among all the motors; however

the DC motor behaviour is more robust during mechanical shocks. Low efficiency,

low speed ranges and periodic need of maintenance are factors which limit the

use of the DC motor for high performance electric vehicles.

Although the BLDC speed response has sharp notches at the time of the load

change, but its fast dynamic response is adequate to follow the reference speed.

BLDC motors also have a more desirable torque response in critical condition

31


than switched reluctance motors. Implementing a fault tolerant control system

will increase reliability of the BLDC motor drives during critical conditions. Fault

diagnosis systems of the BLDC motor drives for some specific faults are presented

in Chapter 6. Finally with respect to the presented comparison discussions in this

chapter, the BLDC motor is recommended as the most suitable choice as drive

train of the high performance electric vehicles.

32

Chapter 3

Modelling of the BLDC Motor

Drive for EV Application


Electric motors play a significant role in electric vehicles. In-wheel motor tech-

nology improves efficiency and safety of the high performance electric vehicles.

BLDC motors are recommended as the propulsion system in the electric vehi-

cles due to their high efficiency, desired torque versus speed characteristics, high

power density and low maintenance cost. An accurate and precise model of the

BLDC motor is required to study different control algorithms of an EV drive

train. Therefore in this chapter, a BLDC motor drive with an ideal back-EMF is

modelled in Simulink. Correct performance of the simulation model of a BLDC

motor drive are validated through experimental data.

3.2 Introduction

BLDC motors are in the category of synchronous motors. Principle of their

operation is similar to the brushed DC motors, however BLDC motors are com-

mutated electronically and have a permanent magnet rotor. Electronic commu-

tation increases the control drive complexity of the BLDC motor. As discussed

in the previous chapter, control techniques of the BLDC motors are divided into

33

3. Modelling of the BLDC Motor Drive for EV Application

two categories; control drives using sensors and sensorless drives. An accurate

model of the BLDC motor that gives the precise values of torque, speed, current

and back-EMF is required to study the various control schemes of the motor [36].

This chapter focuses on modelling of a three phase, star connected BLDC motor

using three Hall Effect sensors for rotor position detection. Sensorless control

techniques of the BLDC motors are discussed in the next chapters.

Various BLDC motor drive models have been reported for different applica-

tions in the last decade [37-45]. Although the previously reported research works

contributed to the BLDC motor modelling, there is not a simple BLDC motor

model with the ideal trapezoidal back-EMF appropriate for the EV application

[33]. In this chapter a three phase, star connected BLDC motor with ideal trape-

zoidal back-EMF waveforms is simulated in Simulink. Overall view of the BLDC

motor drives, modelling of the motor, BLDC motor controllers, simulation and

experimental results are discussed and presented in next sections respectively.

Presented results in this chapter have been published by Tashakori et al. [33].

3.3 Overall View of the BLDC Motor Drive

A BLDC motor, a three phase voltage source inverter and a closed loop control

algorithm are the main sections of the BLDC motor drives. A BLDC motor

includes two separate electrical and mechanical parts. Three Hall Effect sensors

(with 120 electrical degree phase difference) detect the rotor position of the motor.

Hall Effect signals are decoded in the controller and the appropriate voltage space

vectors are chosen to supply the motor. Corresponding switching signals are fed

to the three phase VSI to supply voltages to the windings of BLDC motor. In this

section speed of the BLDC motor is adjusted by a digital PWM speed controller

in a closed loop scheme. Overall structure of the BLDC motor drive is shown

in Figure 3.1. Each part of the BLDC motor drive model is modelled separately

and integrated in the overall simulation model [33].

34


Figure 3.1: Overall structure of the BLDC motor drive

3.4 Modelling of the BLDC Motor

This section presents modelling of a three phase, four poles, star connected per-

manent magnet synchronous motor with the trapezoidal back-EMF. The trape-

zoidal back-EMF implies that the mutual inductance between stator and rotor is

non-sinusoidal, thus an “abc” phase variable model is more applicable than a d-q

axis model [42]. Following assumptions are made to simplify the mathematical

equations and the overall BLDC motor model.

• magnetic circuit saturation is ignored;

• stator resistance, self and mutual inductances of all three phases are equal

and constant;

• hysteresis and eddy current losses are eliminated;

• inverter semiconductor switches are ideal.

The simplified electrical and mechanical mathematical equations of the BLDC

motor can be written as below,

Va = Ria + (L−M)diadt

+ Ea (3.1)

35


Vb = Rib + (L−M)dibdt

+ Eb (3.2)

Vc = Ric + (L−M)dicdt

+ Ec (3.3)

Ea = KeωmF (θm)

Eb = KeωmF (θm − 2π3

)

Ec = KeωmF (θm + 2π3

)

(3.4)

Tea = KtiaF (θm)

Teb = KtiaF (θm − 2π3

)

Tec = KticF (θm + 2π3

)

(3.5)

Te = Tea + Teb + Tec (3.6)

Te − Tl = Jd2θmdt2

+dθmdt

(3.7)

θe =P

2θm (3.8)

ωm =dθmdt

(3.9)

Where Va,b,c is voltage of phase a, b, c that is applied from inverter to the BLDC

motor; ia,b,c is current of phase a, b, c; Ea,b,c is back-EMF voltage of phase a, b, c

and Te(a,b,c) is produced electric torque in phase a, b, c. An embedded program

has been written to generate the ideal back-EMF reference signal, F (θe), with

respect to the electrical degree angle of the permanent magnet rotor. Since phase

windings are distributed symmetrically in the stator, back-EMF signals have 120

electrical degrees phase shift with respect to each other. Ideal output charac-

teristics of the BLDC motor drive are shown in Figure 2.5 on Page 19. Ideal

reference back-EMF waveforms of the BLDC motor model with respect to the

rotor electrical degree are shown in Figure 3.2 [33].

36


Figure 3.2: Ideal reference back-EMF waveforms of the BLDC motor model

Since the neutral point of the BLDC motors is not stable and most of the

times is also not provided by manufacturers, phase voltage differences are used

to generate state space equations (refer to the equations (C5) and (C6) on page

158). Although the neutral point of the BLDC motor is not stable, it is possible to

estimate it with zero crossing detection of the unexcited phase back-EMF voltage.

State space to Laplace transform and reverse can be done for linear and zero

initial condition systems. Therefore two simple Laplace equations of electrical and

mechanical systems of the BLDC motor supplied by phase to neutral voltages are

derived (refer to the equations (3.12) and (3.15)) and used to model the BLDC

motor instead of state space equations [33].

37


va,b,c(t) = Ria,b,c(t) + Ldia,b,cdt

+ ka,b,cωm(t) (3.10)

Laplace transform of the equation (3.10) is,

Va,b,c(s) = RIa,b,c(s) + Ls[Ia,b,c(s)− ia,b,c(0)] + ka,b,cωm(s) (3.11)

By solving the equation (3.11) for I(s) if initial condition of system is zero

(i(0) = 0), the electrical system Laplace equation of each phase is derived as,

I(s)

V (s)− kωm(s)=

1

R + Ls(3.12)

Electric torque of each phase and total electric torque produced by the BLDC

motor are calculated from equations (3.5) and (3.6) respectively. Total electric

torque is applied to the mechanical system of BLDC motor.

Te(t)− Tl = Jdωm(t)

dt+ βωm(t) (3.13)

Laplace transform of the equation (3.13) is

Te(s)− Tl = Js[sωm(s)− ωm(0)] + βωm(s) (3.14)

By assuming that the initial speed of motor is zero and solving the equation

(3.14) for ωm(s), mechanical system Laplace equation of the BLDC motor is

derived as,

ωm(s)

Te(s)− Tl=

1

β + Js(3.15)

Back-EMF signals of the BLDC motor are generated according to the electri-

cal degree of rotor for each phase and applied as a negative feedback to the input

voltages. This approach makes the BLDC motor model simpler and more con-

venient for various control technique implementation. BLDC motor simulation

model is shown in Figure 3.3 [33].

38


Fig

ure

3.3:

BL

DC

mot

orsi

mula

tion

model

39


3.5 Modelling of the BLDC Motor Drive

The schematic diagram of a three phase, four poles, star connected BLDC

motor drive is shown in Figure 3.4. A three phase inverter is used to supply

voltage to the BLDC motor windings. Metal Oxide Semiconductor Field Effect

Transistors (MOSFET) are used to model the three phase VSI in Simulink. The

simulation model of three phase VSI is shown in Figure 3.5.

Two phase conduction mode voltage space vectors of the VSI are selected

based on Hall Effect position sensor signals. Three Hall Effect sensors are de-

tecting permanent rotor position of the BLDC motor. In this model Hall Effect

signals of the BLDC motor are generated through an embedded Matlab code file

according to the electrical degree rotation of the motor. Electrical degree sec-

tions, corresponding Hall Effect signals and inverter switches status of the BLDC

motor are shown in Table 3.1.

Table 3.1: Hall Effect Signals and Inverter Switches Status of the BLDC MotorElectrical Hall A Hall B Hall C Inverter switches status

degree S1 S2 S3 S4 S5 S6

0-60 0 1 0 On Off Off Off Off On

60-120 1 1 0 On Off Off On Off Off

120-180 1 0 0 Off Off Off On On Off

180-240 1 0 1 Off On Off Off On Off

240-300 0 0 1 Off On On Off Off Off

300-360 0 1 1 Off Off On Off Off On

Speed is directly proportional to the average value of applied voltages in the

BLDC motor. Variable DC link inverters and pulse width modulation switching

techniques are the two basic methods to control the average applied voltage to the

BLDC motor. Variable DC link inverters have a poor harmonic control and extra

conversion systems compared to the PWM controlled inverters. One direction

power flow, high stresses of the components and high peak currents that cause

EMI problems are the main drawbacks of variable DC link inverters. The main

disadvantages of PWM inverters are complexity of controller and high frequency

switching losses [46]. Performance comparison of PWM inverter and variable DC

link inverter for high-speed (up to 50000 RPM) sensorless control of the BLDC

40


Figure 3.4: Schematic diagram of a 3 phase, 4 poles, star connected BLDC motordrive

41


Figure 3.5: Three phase VSI simulation model

motor states that more stable sensorless operation can be obtained using the

variable DC link inverters at high speeds [47]. A regenerative brake system is an

essential factor to increase the battery life or mileage of the electric vehicle. One

direction power flow characteristics of the variable DC inverters is not suitable for

the regenerative brake system in the electric vehicles. Therefore PWM switching

technique is more suitable to control the average output voltage of VSI. Details

of the PWM speed controller of the BLDC motor are discussed in Chapter 5.

3.6 Simulation Results and Discussion

A three phase star connection BLDC motor with the ideal trapezoidal back-

EMF and its control drive are modelled in Simulink. The BLDC motor model

BLK423S specifications manufactured by Anaheim Automation Company are

used in model. The BLDC motor model BLK423S specifications are summarized

in Table 3.2. Specification of the power MOSFET model IRFR2407 (refer to

42


Appendix B) is used to model the three phase VSI.

Simulation model is tested for 2000 RPM controller reference speed and 5.9

N.m torque load. PWM speed control signals are applied to the upper switches

in each leg of VSI. Speed characteristic of the BLDC motor simulation model

is shown in Figure 3.6. As can be seen speed of the BLDC motor follows the

reference speed of the PWM speed controller.

Table 3.2: Specifications of the BLDC Motor Model BLK423S


DC voltage 310 V

Rated speed 3000 RPM

Phase resistance 0.38 Ω

Phase inductance 2.95×10−3 H


Damping ratio 0.001 N.m.s

Poles 8 -

Figure 3.6: Speed characteristics of the BLDC motor simulation model

43


Figure 3.7: Torque characteristics of the BLDC motor simulation model

The torque characteristic of the BLDC motor simulation model is shown in

Figure 3.7. Initial produced electrical torque of the BLDC motor simulation

model is 92.6 N.m. Produced electric torque of the BLDC motor in steady state

condition pulsating around the load torque value.

Line voltage, Current and corresponding Hall Effect signal of the phase A of

BLDC motor model are shown in Figure 3.8. The line voltage of the motor is

measured with respect to the negative terminal of the DC link of inverter (and

scaled down by factor 0.1) to have a comprehensive view of all three signals in

one figure. As can be seen in Figure 3.8, the BLDC motor current is maximum

(10.4 Amperes) when the Hall Effect signal rises to logic one and is minimum

when the Hall Effect signal falls to logic zero.

The trapezoidal back-EMF signals of the BLDC motor model with respect to

electrical degree rotation of the rotor are shown in Figure 3.9. As can be seen in

figure, there are exact 120 electrical degree phase difference between back-EMF

signals of the BLDC motor.

44


Figure 3.8: Voltage, Current and Hall Effect signal of phase A of the BLDC motor

Figure 3.9: Back-EMF signals of the BLDC motor model

45


3.7 Simulation Model Validation

A three phase in-wheel BLDC motor hub designed for electric motor cycle ap-

plication is used as a practical test motor to validate the BLDC motor simulation

model. The experimental set-up of the BLDC motor is shown in Figure 3.10.

The BLDC motor simulation model is developed based on specifications of the

experimental test motor. Specifications of the three phase in-wheel BLDC motor

hub are given in Table 3.3.

Figure 3.10: Experimental test set-up of the BLDC motor

Table 3.3: Specifications of the Experimental In-wheel BLDC MotorDescription Value Unit

DC voltage 48 V

Rated speed 600 RPM





Poles 8 -

46


Figure 3.11: Line voltage and Hall Effect signal of phase A of the BLDC motor

47


The experimental in-wheel BLDC motor and simulation model are tested

under the same operating conditions for 600 RPM reference speed. The inbuilt

drum brake of the in-wheel motor hub is used to apply load to the test motor. The

applied load torque to the motor is 1.54 N.m according to manufacturer test data-

sheet. The PWM switching signal is applied to the upper switches of VSI. The

line voltage and corresponding Hall Effect signal of phase A of the test BLDC

motor and simulated motor model are shown in Figure 3.11. The simulation

results and the test data are not 100% match. The pattern of the voltage and

duration of electrical degrees are almost same; however duty cycle of the control

PWM signal is different. Good agreements between the simulation results and

experiment results validate the simulation model of the in-wheel BLDC motor.

3.8 Conclusion

Improving control strategies of the in-wheel motors result in improving overall

performance of the electric vehicle. An accurate model of the in-wheel motor

which provides precise information of produced torque and motor speed values

is needed to study the behaviour of electric vehicles under different control algo-

rithms and working conditions. In this chapter, the simulation model of a three

phase star connected BLDC motor model with the ideal back-EMF is presented.

The proposed model is simulated in Matlab/Simulink. Simulation results under

load conditions show proper performance of the BLDC motor model. A three

phase in-wheel BLDC motor hub designed for electric motor cycle application is

used as an experimental test motor to validate the BLDC motor model. Exper-

imental results prove correct performance of the simulation model of the BLDC

motor. The presented simulation model is simple and based on Laplace trans-

form of mathematical equations of the BLDC motor. Simplicity and discussed

specifications of the proposed model make it useful in the design of the BLDC

motor drives with different control algorithms for the EV application.

48

Chapter 4

Direct Torque Control Drive of

BLDC Motor for EV Application


Two BLDC motor control methods exists based on using sensors for permanent

magnet rotor position detection or not. Simpler motor construction, manufac-

turing cost reduction, less maintenance needs and no possibility of the motor

malfunctions due to unbalanced positioning or failure of the position sensors are

immediate advantages of sensorless control techniques. Various back-EMF mon-

itoring algorithms and flux linkage based techniques are discussed to commutate

the BLDC motor in the sensorless mode. Direct torque control technique (DTC)

is a flux linkage based sensorless control method. It does not use any sensors

for detecting permanent magnet rotor position. Correct speed and torque con-

trol of an in-wheel motor results in controlling of drive train output power in

the electric vehicle. In this chapter direct torque control switching technique of

the BLDC motor for the EV propulsion application is discussed. Results show

effective control of the torque and remarkable reduction of the torque ripples

amplitude compared to conventional reported switching techniques. Improving

torque control of the EV drive train results in more efficient and safer vehicles.

49

4. Direct Torque Control Drive of BLDC Motor for EV Application

4.2 Introduction

Generally three Hall Effect sensors are mounted inside the BLDC motor with

120 electrical degrees phase difference to detect permanent magnet rotor position

in the sensor mode control scheme. Eliminating rotor position detection sensors

in the BLDC motor reduces the cost and construction complexity of the motor.

However the BLDC motor control algorithm will be more complicated by imple-

menting the sensorless control methods. In the sensorless control mode, rotor

position is detected through output parameters of the motor such as voltage and

current. Back-EMF sensing, back-EMF integration, freewheeling diode conduc-

tion of unexcited phase, flux linkage based, speed independent position function

and third-harmonic analysis of back-EMF are sensorless techniques for commu-

tation of the BLDC motor [7]. Back-EMF sensing at low speeds and transient

time and discontinuous response due to high commutation rates are the main

disadvantages of the sensorless techniques [48].

Valuable research works have been published on different sensorless control

algorithms of the BLDC motor [7][14][49][50]. A DSP-controlled PWM chopper

with a C-dump converter drive has been presented for BLDC motors [49]. A

dual speed and current closed-loop control are used to keep a constant voltage

to frequency ratio to maintain constant torque operation of the BLDC motor.

Forced commutation RC circuits and the effect of snubber circuits to control

commutation and dvdt

rating on switches have been discussed. Simulation results

show a number of current spikes that increase torque ripples of the BLDC motor

that is not suitable for the in-wheel application. A current controlled PWM

technique with a four switch inverter drive has been reported for BLDC motors

[14]. Difficulties in generating 120 conducting current profiles for the three phase

BLDC motor with four switch inverters and current distortion of two phases due

to back-EMF of the silent phase are main drawbacks of the proposed technique

[48]. Kim and Ehsani have discussed a sensorless control technique with a new flux

linkage function for BLDC motors [7]. A speed-independent flux linkage position

function, “G(θ)”, has been defined according to the rotor mechanical angle. This

technique provides a precise commutation pulse even in transient state and is

able to detect position of the rotor at around 1.5% of nominal speed. Therefore

50


problems of sensorless control techniques at low speeds have been improved by the

proposed approach. It is suitable for in-wheel BLDC motors where it is needed

to control the EV at low speeds, for example in a traffic jam. A BLDC motor

control drive with two modes of conduction angle control and current control

operations has been introduced by Rodriguez and Emadi [50]. Torque and current

are directly proportional in electric motors, therefore current control results to

torque control of the BLDC motor. Speed oscillations of the BLDC motor is

reduced up to maximum of 3.4% by the proposed digital controller. Implementing

a torque ripple reduction techniques to the proposed digital controller makes it

more suitable for the EV application [8].

Cogging torque due to the stator slots interacting with the rotor magnetic

field, reluctance torque due to the variation in phase inductance and mutual

torque due to the mutual coupling between the stator winding current and rotor

magnetic field are the main electric torque production sources in BLDC motors

[9]. Skewing of rotor magnets with respect to the rotor axis, skewing of the stator

and coordinating the number of stator sluts in the motor design are techniques

which remarkably reduce the effect of the first two torque production sources

[51]. In general there are three types of permanent magnet rotors for the BLDC

motor; polar magnets or surface mounted magnets, sub-polar (inset) magnets

and buried (interior) magnets [51]. The surface-mounted magnet rotors enlarge

the effective air gap and minimize armature effect on the rotor magnetic field.

Torque ripples are reduced due to the larger effective air gap and smoother flux

density distribution in the air gap. Therefore they are widely used in the high

performance BLDC motors [9].

DTC technique is a sensorless control technique because it does not use any

sensors for detecting position of the permanent magnet rotor. In electric motors,

output power is directly proportional to the produced electric torque and speed of

the motor. Therefore simultaneous torque and speed control is important factor in

the drive train of electric vehicles. In-wheel motors need to operate at high speed

that is inappropriate for frequent start, stop and low speed operation of electric

vehicles. Therefore a gear box is used to reduce electric motor speed and increase

produced torque when the electric vehicle is operating at low speeds. Small

speed fluctuations are damped by the gear box due to the large mechanical time

51


constant, but torque oscillations are more significant. Improving performance of

the in-wheel motors increases the safety of electric vehicles. Safety and efficiency

of the electric vehicles increase by delivering the desired ripple free torque to the

wheels of an electric vehicle in various operating conditions. Therefore direct

torque control switching technique is a suitable choice for the high performance

electric vehicles [8]. Presented simulation and experimental results in this chapter

have been published by Tashakori et al. [8][48].

4.3 Direct Torque Control of the BLDC Motor

Using Three Phase Conduction Mode

Direct torque control technique for induction motors was introduced for the

first time by Takahashi and Noguchi in 1986 [52], and later by Depenbrock

in 1988 [53]. Many research works on DTC of BLDC motors have been re-

ported for various applications that need precise torque control in the last decade

[54][55][56][57][58][59]. Direct torque control of BLDC motor as a drive train of

hybrid electric vehicles have been reported by Gupta et al. [58].

A schematic diagram of DTC drive of the BLDC motor is shown in Figure 4.1.

Torque error, stator flux error and stator flux angle must be calculated to select

the correct voltage space vector for switching in DTC drive of the BLDC motor.

Flux linkage error is eliminated in the DTC model presented in this chapter due to

variations of stator flux magnitude by changes in resistance, current and voltage

and specifically sharp dips at every commutation interval [54].

In-wheel BLDC motors should operate in both the constant torque region and

the extended constant power region. Back-EMF of motor is less than DC link

voltage of the VSI in constant torque region (below rated speed) and is more than

DC link voltage value above the base speed. Stator inductance avoids an abrupt

increase of phase current in constant power region and distorts the output torque

of the BLDC motor. Therefore in this chapter, it is considered that the BLDC

motor is operating in constant torque region below the rated speed.

Accurate estimation of flux linkage magnitude and produced electrical torque

is important in DTC drive of the in-wheel motors. In some techniques, current

52


Figure 4.1: Overall structure of DTC drive of the BLDC motor

sensors have been used to determine flux linkage and estimate voltage from the DC

bus of inverter [56][58][59]. This method is too sensitive to voltage errors caused

by dead-time effects of the inverter switches, voltage drop of power electronic

devices and fluctuation of the DC link voltage [51]. In this chapter both current

and voltage signals are measured for accurate estimation of flux linkage magnitude

and produced electric torque [57].

Precise estimation of electric torque and flux angle of the BLDC motor mainly

depend on accurate sensing of phase currents and voltages of the motor. Varia-

tions of stator winding resistance due to changes in temperature cause errors in

the stator flux estimation. Analogue integrators also produce DC offset to the

signal that causes errors in torque estimation. As it is assumed that the BLDC

motor is operating in constant torque region below the rated speed, therefore the

53


stator flux magnitude does not change during operation. The second algorithm

proposed by Hu et al. [60] with limiting level of 2KLπ/(3√

3) (where KL is the

flux linkage of motor) is used to eliminate analogue integrator DC drift error [48].

The balanced three phase system (voltages and currents) is converted to the

αβ-axis references by applying Clarke transformation (refer to the equations (D1)

and (D2) on the page (158)). Stator flux linkage magnitude, stator flux angle and

electrical torque of motor can be estimated by [8],

ϕSα =

∫(VSα − iSα).dt (4.1)

ϕSβ =

∫(VSβ − iSβ).dt (4.2)

ϕrα = ϕSα − LiSα (4.3)

ϕrβ = ϕSβ − LiSβ (4.4)

|ϕS| =√ϕ2Sα + ϕ2

Sβ (4.5)

ΘS = tan−1(ϕSβϕSα

)(4.6)

eα =dϕrαdt

(4.7)

eβ =dϕrβdt

(4.8)

Te =3

2

P

2

[eαωiSα +

eβωiSβ

](4.9)

After calculating the values of stator flux linkage in the stationary αβ-axis

from equations (4.1) and (4.2), flux linkage magnitude and angle are determined

from equations (4.5) and (4.6). By substituting the αβ-axis rotor flux vectors

values calculated from equations (4.3) and (4.4), produced electric torque of the

54


BLDC motor is estimated from equation (4.9).

Hysteresis controller generates a square wave pulse if the torque error is over

the predefined band limits (Terror = Tref − Testimated). Output of the hysteresis

controller is “1” if the electric torque produced by the BLDC motor is more than

reference torque input of the controller and is “0” if the produced electric torque

is less than the reference torque. In this chapter the simulation model is tested

for hysteresis band limits of 1, 0.1 and 0. 01 to show the torque ripples reduction

capability of the DTC. The maximum switching frequency for minimum value of

hysteresis band limits is around 10KHz.

The three phase conduction mode is used for switching of VSI drive of BLDC

motor. Six non zero voltage vectors that have been used to switch VSI are

V1(100), V2(110), V3(010), V4(011), V5(001), V6(101). The estimated stator flux an-

gle of the BLDC motor is divided into six equal sectors (each sector is 60 degrees)

starting from 30 degrees. The correct voltage space vector is chosen according to

the hysteresis torque controller output and flux angle sectors of the BLDC motor

[61]. Switching look-up table for choosing voltage space vectors of VSI is shown

in Table 4.1.

Table 4.1: Three Phase Conduction Switching Mode for DTC of the BLDC Motor

Torque error Flux angle sectors

error 1 2 3 4 5 6

1 V6(101) V1(100) V2(110) V3(010) V4(011) V5(001)

0 V2(110) V3(010) V4(011) V5(001) V6(101) V1(100)


The proposed DTC drive of the BLDC motor is simulated in Simulink. Spec-

ification and parameters of BLDC motor used in the simulation model are listed

in Table 4.2. Simulation results show that the DTC algorithm precisely estimates

torque, flux linkage magnitude and angle of the BLDC motor. Torque ripples of

the BLDC motor for various hysteresis controller band limits of DTC technique

are compared with the conventional Hall Effect switching control technique.

55


Table 4.2: Specification of BLDC Motor Used in Simulation Model


DC voltage 400 V





Flux linkage 0.175 Wb

Poles 4 -

DTC drive of the BLDC motor is run at 1500 RPM under 10 N.m torque

load (hysteresis band limits are set to 0.01). Speed and torque responses of the

direct torque controlled BLDC motor drive are shown in Figure 4.2 [48]. Fast

and smooth torque response of BLDC motor is remarkable in Figure 4.2.

Figure 4.2: Speed and torque responses of the direct torque controlled BLDCmotor drive

56


DTC drive is also tested for various hysteresis band limits and results are

compared with the conventional Hall Effect switching control technique of the

BLDC motor. Torque responses of the conventional control drive and DTC drive

of the BLDC motor for various hysteresis band limits under same speed and

load condition are shown in Figure 4.3 [48]. The electric torque ripple amplitude

decreases as the hysteresis band limits are reduced.

Figure 4.3: Pulsating torque of the BLDC motor for different hysteresis bandlimits

Amplitude of the torque ripples is decreased up to four percent of the reference

torque (0.4 N.m) in the proposed DTC model. That is ten times smaller than the

conventional Hall Effect switching control technique. The proposed DTC drive of

the BLDC motor has a better torque characteristics compared to the presented

57


model in [58]. High frequency switching in the VSI drive of the BLDC motor is the

main disadvantage of small hysteresis band limits. Switching frequency is directly

proportional to the switching loss in the inverters and practically hysteresis band

limits can not be less than a particular threshold [48].

The stator flux magnitude and flux angle of the BLDC motor calculated by

DTC technique are shown in Figure 4.4 [48]. As can be seen the stator flux

magnitude is almost constant, around 0.22 Wb, in constant torque region below

the rated speed. The calculated flux magnitude is almost the same as the limiting

level of integration algorithm (2KLπ/(3√

3)). The change of the stator flux angle

from 0 to 360 degrees shows a full electric rotation of the rotor.

Figure 4.4: Calculated stator flux magnitude and flux angle of the BLDC motor

58


The direct torque controlled BLDC motor drive is tested at 1500 RPM under

5 and 10 N.m load torques. Stator flux linkage trajectory of the BLDC for 5

and 10 N.m load torques are shown in Figure 4.5. Six equal flux angle sectors

discussed in the switching table of the DTC can be seen in the figure. Stator

flux linkage locus of 10 N.m load torque has higher flux magnitude and sharper

changes at the boundaries of the flux angle sectors [48].

Figure 4.5: Stator flux linkage trajectory of the BLDC motor for 5 and 10 N.mloads

Robustness and fault tolerance of the in-wheel motor and its controller are the

most significant parameter with respect to the electric vehicle’s safety. Therefore

in this section, behaviour of the proposed DTC drive and the conventional control

drive of the BLDC motor are compared under the same sudden change of the load

torque. An abrupt fifty percent increase to the load is applied at t=0.4 s while

the BLDC motor is running at 1500 RPM under 10 N.m load torque. Speed

response of DTC and conventional switching control drives of the BLDC motor

under sudden increase of the load torque are shown in Figure 4.6.

59


Figure 4.6: Speed response of the BLDC motor under sudden increase of load

Figure 4.7: Torque response of the BLDC motor under sudden increase of load

60


As can be seen in the figure, the speed response of the BLDC motor for

DTC drive follows the controller reference speed almost fifteen times faster than

conventional switching technique after abrupt change of the load. However speed

fluctuation of the DTC drive is more than the conventional switching technique.

Torque response of DTC and conventional switching control drives of the

BLDC motor under sudden increase of the load torque are shown in Figure 4.7.

As can be seen the torque response of the DTC drive is much faster than the Hall

Effects switching technique of the BLDC motor. Dynamic torque response of the

electric motor plays an important role in the overall stability of the EV when it

is subjected to frequent changes of the load torque. Therefore the DTC drive is

more suitable for in-wheel BLDC motors compared to the conventional control

drive in high performance electric vehicles.

4.5 Experimental Results

Performance of the proposed direct torque controlled BLDC motor is investi-

gated through experiment. A low voltage development board of the microchip

using PIC18F4231 microcontroller is programmed to test the proposed DTC drive

control system on a 24 volts experimental test BLDC motor. The experimental

set-up of the BLDC motor is shown in Figure 4.8. MOSFET switches are used in

VSI drive of the BLDC motor. Specifications of the experimental BLDC motor

is given in Table 4.3 (refer to the hurst motor data-sheet in Appendix B).

Direct torque control drive of the experimental BLDC motor is tested at

2000 RPM (below rated speed of the experimental BLDC motor) under 0.1 N.m

reference torque and for 0.01 hysteresis band limits. Torque characteristics of the

experimental BLDC motor are shown in Figure 4.9. As can be seen, produced

electric torque of the BLDC motor pulsates around 0.1 N.m (load torque) with

the maximum torque ripples amplitude of 0.12 N.m.

61


Figure 4.8: Experimental set-up of the BLDC motor

Table 4.3: Specifications of the Experimental BLDC Motor


DC voltage 24 V

Rated Speed 3000 RPM

Rated torque 0.28 N.m




Poles 8 -

62


Figure 4.9: Torque characteristics of the experimental BLDC motor

4.6 Conclusion

Electric vehicles are a viable alternative for the future transportation that does

not emit greenhouses gases into the atmosphere. BLDC motors are commonly

used by auto-mobile manufacturers as a propulsion system of the electric vehicles.

Torque control of the in-wheel BLDC motors is an important factor in overall

safety of the electric vehicles. In this chapter direct torque control switching

technique of the BLDC motor is introduced as a suitable choice for the EV drive

train application. DTC drive model of the BLDC motor is simplified, flux linkage

observation is eliminated, for the constant torque region operation.

The proposed DTC model of the BLDC motor is simulated in Simulink. Sim-

ulation results show that the estimated torque by state observer is as same as the

produced electric torque of the BLDC motor. It is also possible to control the

torque ripples of the BLDC motors by adjusting the hysteresis band limit. Sim-

ulation results show effective reduction of torque ripple amplitude by DTC drive

compared to the conventional control system of the BLDC motor. The proposed

63


DTC drive is tested on a low voltage BLDC motor through the experimental set-

up. Experimental results show effective control of torque and correct performance

of the proposed drive. Developed DTC switching technique of BLDC motor is

capable of minimizing the torque ripples and delivering the smoother mechanical

power to the wheels.

64

Chapter 5

Stability Analysis of a Novel

Sensorless Drive of BLDC Motor


Permanent magnet brushless DC motors have been widely used in traction

applications such as propulsion system of electric vehicles in the last decade. Sen-

sorless control drives of the BLDC motor have been extensively used in industrial

application in recent years. In this chapter a novel sensorless technique based

on back-EMF zero crossing detection (ZCD) of one phase of the BLDC motor is

proposed for EV application. The presented sensorless algorithm is simple and

remarkably reduces sensing circuitry, noise susceptibility and cost of the BLDC

motor control drives. Speed controller of the BLDC motor is a digital pulse width

modulation technique using a Proportional Integral controller. Stability of the

proposed sensorless BLDC motor drive using digital PWM technique is analysed

using Lyapunov stability method. Based on Lyapunov stability criterion a novel

condition for stability analysis of the PWM speed controller is derived. Effective-

ness of the proposed sensorless technique and precision of the introduced stability

analysis condition of the PWM speed controller are proved through simulation

and experiment.

65

5. Stability Analysis of a Novel Sensorless Drive of BLDC Motor

5.2 Introduction

BLDC motors have been used extensively in various industrial applications

such as auto-mobile and aerospace industries in the 21st century. As it is discussed

in previous chapters, BLDC motor is a type of conventional DC motor that is

commutated electronically instead of using brushes. Schematic diagram of a 3

phases, 4 poles, star connected BLDC motor drive is shown in Figure 3.4 on

Page 41. Detecting position of the permanent magnet rotor is the key point

for electronic commutation of the BLDC motor. Position of rotor is normally

detected by three Hall Effect sensors mounted in non-rotating end of motor with

120 electrical degree phase difference. Signal of the Hall Effect sensor is high or

low as the rotor magnetic poles N or S passes near the sensor [32]. Therefore

output of each sensor is high for 180 electrical degree and is low for the next

180 degree according to the rotor position. Correct voltage space vectors for

switching inverter drive of the BLDC motor are chosen by decoding Hall Effect

signals (refer to Table 3.1 on the page 3.1).

Commutation of the BLDC motors using position sensors are much easier than

sensorless methods. However increase of the motor manufacturing cost, problems

due to the sensors breakdown, need of sensors to be mounted accurately that

increase complexity of the manufacturing process, regular need of maintenance,

extra wiring and limited operation of the motor due to temperature sensitivity

of sensors are the main drawbacks of using position sensors to commutate BLDC

motor [11].

Sensorless control drive of the BLDC motors have became popular in some

specific applications such as EV in the last decades. Reduction complexity of the

BLDC motor construction, cost and need of maintenance are immediate advan-

tages of sensorless control. Various sensorless control techniques of the BLDC

motor are reported so far. Back-EMF zero crossing detection, back-EMF in-

tegration, back-EMF harmonic analysis, freewheeling diode conduction of the

unexcited phase and flux linkage based methods are examples of reported sen-

sorless technique of the BLDC motor [7]. However transient time response and

high commutation rates are the main drawbacks of the BLDC motor sensorless

drives [48]. Since the trapezoidal back-EMF permanent magnet synchronous mo-

66


tors require accurate phase current control and any phase errors in commutation

signals cause significant pulsating torque and increase copper losses in the BLDC

motor [62].

Back-EMF based sensorless methods mainly rely on zero crossing detection

of the phase voltages. There are 120 electrical degree conduction period and

180 electrical degree conduction period switching techniques for VSI drive of the

BLDC motors. There is an unexcited phase, silent phase, winding during each

step in 120 conduction electrical degree mode that is used for Back-EMF ZCD

and rotor positioning in sensorless drives of the BLDC motor [63]. Phase voltages

of the BLDC motor during the silent period is the same as back-EMF voltage

[64]. Since neutral point of the BLDC motor windings is neither provided by the

manufacturer nor stable during high frequency PWM switching; the back-emf

of each phase is detected through the line voltage of the same phase of motor

with respect to the negative terminal of VSI DC power supply [34]. This method

also eliminates unwanted common mode noises on the voltage signal. Therefore

the measured back-EMF voltages does not need filtering and are less susceptible

for switching noise [65]. Filters produce phase delays to the measured voltages

that is dependent to the frequency or the motor speed. Reduction of torque

per ampere capability of the BLDC motor, increase of torque ripples, additional

copper loss, limited speed range operation of the motor, poor signal to noise

ratio during starting time and severe commutation delays at high speeds are the

main disadvantages of filtering phase delays [62][66]. Sensing line voltages of the

BLDC motor with respect to the negative terminal of DC link of VSI also avoids

filtering delay problems [67]; however operation of the BLDC motor at low speeds

is the main drawback of this method [7]. Difficulties of back-EMF sensing at low

speeds (proper rotor positioning is not possible below 20% of the rated speed) and

position detection errors during quick acceleration or deceleration of the BLDC

motor are the general problems in ZCD of the back-EMF sensing based sensorless

methods [65].

Sensorless techniques based on zero crossing detection of line voltage differ-

ences of the BLDC motor is introduced [11][68]. Three phase line voltages of

the BLDC motor have to be measured separately in the proposed methods that

increase the measuring circuitry. There is 30 electrical degree phase delay be-

67


tween ZCD points and commutation points at each phase of the BLDC motor.

The proposed method by Kim et al. [68] is much simpler because it does not

need implementation of electrical degree phase delay after ZCD points. However

the BLDC motor operation at low speeds is a drawback for the both proposed

sensorless drives.

Digital PWM control and hysteresis current control techniques are more com-

mon in the BLDC motor speed controllers than sliding mode control technique

[69]. Pulse width modulation technique is used for speed control in this chap-

ter. High frequency PWM signal is superimposed on the neutral point of star

connected windings and induce noise to the measured line voltages. Back-EMF

integration and third harmonic voltage integration are introduced to reduce the

inverter switching noise effects and avoid using filters in the BLDC motor sen-

sorless drives [66]. In the first method, commutation points are detected through

back-emf ZCD of the unexcited phase of the motor as soon as the voltage integral

pass a predefined threshold [64]. However accurate control and poor performance

of the BLDC motor at low speeds are weaknesses of back-EMF integration ap-

proach [11][66]. The other main problem of this technique is DC drift errors

produced by analogue integrators, as it is discussed in Chapter 4, specifically at

low speeds [64].

Addition of three phase voltages of the BLDC motor results in the third har-

monic and multiples of the third harmonic components due to the symmetric

three phase star connected windings. Zero crossing of integrated third harmonic

signal occurs at the exact current commutation points of the BLDC motor [69].

This technique has a wider speed range compare to the back-EMF integration

method. Need of the neutral point voltage of the motor, variation of the winding

inductance due to the rotation of permanent magnet motor (which needs to be

constant in this method), low amplitude of the third harmonic components at

low speeds, variation of magnitude and phase of harmonic components due to

magnetic saturation and unbalanced situation in the surface mounted permanent

magnet machines are the main limitations of the BLDC motor third harmonic

back-EMF based sensorless drives [7][70]. An application-specific integrated cir-

cuit (ASIC) based controller using third harmonic integration method with a

phase locked-loop (PLL) is proposed to improve the BLDC motor performance at

68


ultra-high speeds [71]. The current phase delay is eliminated and the BLDC mo-

tor commutation is improved by the proposed technique. Sensorless commutation

of the BLDC motor based on conducting status of anti-parallel connected free-

wheeling diode of the unexcited phase has a wide speed range and simple starting

procedure [65]. Position detection errors during transient state, additional inde-

pendent voltage sources, need for the proper isolation mechanism, complexity of

algorithm and high cost of the controller drive are the main drawbacks of this

method [66][72].

Switching signals in flux linkage based sensorless methods, same as the direct

torque control discussed in Chapter 4, are extracted through flux linkage magni-

tude and flux angle of the BLDC motor. Flux linkage magnitude and flux angle

values are calculated from the motor voltage and current signals. Flux based

sensorless methods have significant flux estimation errors at low speeds [34]. Kim

and Ehsani are reported a speed independent method to solve the flux estima-

tion errors of the flux based sensorless methods of the BLDC motor at low speed

[7]. The proposed method is based on the derivative of phase currents and needs

digital implementation, thus it is susceptible to noise [11].

In this chapter, a simple sensorless commutation technique is proposed for

the BLDC motor based on the ZCD of back-EMF of one phase of motor instead

of measuring back-EMF of all the three phases. Back-EMF sensing circuitry

and cost of the sensorless drive of BLDC motor is effectively reduced in the pro-

posed method. Back-EMF of BLDC motor is sensed through line voltage of one

phase of motor with respect to the negative terminal of DC bus of VSI. There-

fore the sensed back-EMF voltage does not need filtering and is less susceptible

to the noise. The proposed commutation technique also can be used as a re-

medial strategy in the Hall Effect sensors fault tolerant control system (refer to

Chapter 6, Section 6.5). The proposed sensorless technique is suitable to design

integral in-wheel BLDC motor for electric vehicles due to its simplicity and low

electromagnetic interference (EMI) [34].

Back-EMF of the BLDC motor is directly proportional to the rotor speed,

therefore back-EMF based sensorless methods have a poor performance at low

speeds. The main problem raises up at start up of the BLDC motor where there

is no back-EMF at stand still situation. Therefore a starting procedure is needed

69


to speed up the BLDC motor to a point that is possible to measure the back-

EMF voltage and implement the sensorless algorithm [65][73]. Various start-up

methods are reported for the sensorless BLDC motor drives [73][74][75][76]. Start

up techniques of the sensorless BLDC motor drives are not within the scope of this

chapter. Starting algorithm reported by Iizuka et al. [74] is used in the proposed

method in this chapter. The discussed sensorless commutation technique in this

chapter is applicable after prepositioning and start up process.

Digital pulse width modulation technique is used for speed control of the

BLDC motor. Stable performance of the motor and its drive is critical and im-

portant in applications such as the electric vehicles that any instability of the

system may put life of the passengers in danger. Up to the knowledge of the

author, there are not much reported research works on stability analysis of the

PWM control drives of BLDC motors so far. Milivojevic et al. [77] have discussed

stability analysis of FPGA based PWM controller drive of the BLDC motor ac-

cording to the Lyapunov stability method. Merits and demerits of the proposed

stability analysis method are discussed in details in section 5.4. The introduced

stability analysis method is improved and a new equation is introduced to analyse

the stability of the BLDC motor drives using PWM speed controller for EV appli-

cation. The discussed stability analysis method is validated through simulation

and experimental results. Some of the presented simulation and experimental

results in this chapter have been published by Tashakori et al. [34].

5.3 Proposed Sensorless Technique for BLDC

Motor

A comprehensive knowledge of the BLDC motor performance such as exact

information about sensors output signals with respect to the permanent magnet

rotor position and correlation between the back-EMF, line voltages and commu-

tation points of the BLDC motor is needed to develop a sensorless algorithms.

Equivalent electrical circuit of a three phase star connected BLDC motor and

VSI drive are shown in Figure 5.1.

70


Figure 5.1: Equivalent electrical circuit of the BLDC motor drive

Terms Va, Vb and Vc are referred to the line voltages of the BLDC motor, Vn

is the neutral point voltage and Vdc is the DC bus voltage of the inverter. Ideal

commutation signals, terminal and back-EMF voltages of the BLDC motor are

shown in Figure 5.2. As can be seen in the figure, at each instant of time only

two switches of inverter is conducting according to the permanent magnet rotor

position (refer to the Table 3.1 on the page 40). It means that two phases are

conducting and current of one phase is zero, therefore it is possible to measure

the back-EMF voltage through the unexcited phase. Consider phase B and C are

conducting and phase A is the silent phase. Therefore,

ia + ib + ic = 0⇒ ib = −ic and ia = 0 (5.1)

Resistance and inductance of stator windings are assumed to be constant.

Magnetic circuit saturation and losses are also ignored in calculations. Then

71


Figure 5.2: Ideal commutation signals, terminal and back-EMF voltages of theBLDC motor

terminal voltage equations of the BLDC motor can be expressed as [34],

Va = Ea + Vn (5.2)

Vb = Rib + Ldibdt

+ Eb + Vn (5.3)

Vc = Ric + Ldicdt

+ Ec + Vn (5.4)

where Eb = −Ec and Va + Vb = Vdc, therefore the neutral point voltage can be

72


calculated by adding equations (5.3) and (5.4),

Vn =VDC

2(5.5)

Substituting the calculated neutral voltage in equation (5.2), terminal voltage

of the unexcited phase can be written as,

Va = Ea +VDC

2(5.6)

Zero crossing points of back-EMF can be detected from terminal voltage of

the floating phase through equation (5.6). Thus zero crossing points of back-EMF

voltage (Ea = 0) occur when the corresponding floating terminal voltage is [72],

Va =VDC

2(5.7)

Therefore as it is also shown in the Figure 5.2, zero crossing points of back-

EMF voltage of the unexcited phase happen when terminal voltage of the corre-

sponding phase of the BLDC motor is equal to half of the DC power supply of

inverter. On the other words as can be seen in the figure, commutation instants

of the BLDC motor occur 30 electrical degree after the ZCD points and there

is the exact 120 electrical phase shift between commutation signals of different

phases in the BLDC motor.

Knowing the exact back-EMF zero crossing points of one phase is enough

to generate commutation signals of the other two phases of the BLDC motor.

Therefore in this chapter an optimized sensorless commutation technique of the

BLDC motor is introduced based on back-EMF zero crossing detection of one

phase of motor. In the proposed method line voltage of only one phase of the

BLDC motor is sensed with respect to the VSI DC link instead of measuring all

three terminal voltages [34]. A reference commutation signal is generated based

on ZCD of the line voltage. The reference commutation signal is set to logic one

at zero crossing points of rising edge and it is set to logic zero at zero crossing

points of falling edge of the measured line voltage. Then commutation signals

of all three phases are generated according to their correlated electrical degree

delays from the reference signal.

73


A simple formula is introduced to calculate time of the electrical degree delay

with respect to the speed of the BLDC motor. If speed of the motor is constant

during the commutation intervals (controller keeps the motor speed constant) and

the number of motor pole pairs (Ratio of electrical degree to mechanical degree

rotation of rotor) is known; therefore the time needed for one electrical degree

rotation of the rotor can be calculated in seconds as below [34],

Tone electrical degree =60

P2

(360× ωref )(5.8)

Where P is the number of the motor poles and ωref is the reference speed in

RPM. Practically it is easy and convenient to calculate and implement the elec-

trical degree delays as time delays in microcontrollers. Schematic diagram of the

proposed BLDC motor sensorless drive is shown in Figure 5.3.

Figure 5.3: Schematic diagram of the proposed BLDC motor sensorless drive

74


5.4 Stability Analysis of Digital PWM Controller

Hysteresis current control and pulse width modulation control techniques are

the most widely used methods in the BLDC motor speed control drives [69][78].

Speed of the BLDC motor is directly proportional to the applied terminal voltages

of the motor. A three phase voltage source inverter is used to supply the BLDC

motor in the six-step algorithm. Variable DC link inverter and PWM technique

are commonly used techniques for adjusting the average output voltage of the

VSI. In the first method voltage of the DC power supply of the inverter changes

according to the motor speed. In PWM technique, a high frequency duty cycle

controlled signal is added to switching signals of the VSI. By adjusting the duty

cycle of the PWM signal according to the motor speed, it is possible to the applied

voltages to the BLDC motor and consequently the speed of the motor. The PWM

signal can either be multiplied to the switching signal of upper switches, lower

switches, or all six switches of the VSI. Pattern of the applied line voltage to the

BLDC motor varies for different PWM switching mode [5]. In this chapter, PWM

signals are applied to the upper switches of the VSI in the simulation model and

experimental set-ups.

A digital control scheme for the BLDC motor drives is reported by Sathyan

et al. [78] based on the two predetermined duty cycle values (state high, DH ,

and state low, DL, PWM duty cycles) for PWM signal. Controller switches

between state high and state low according to the BLDC motor speed. Predefined

duty cycle values limit the functionality of the controller for the variable speed

applications such as electric vehicle. A proportional and integral controller is

used to adjust the duty cycle of PWM signal with respect to the speed error.

Ideally one duty cycle is chosen by PI controller, (0 ≤ D ≤ 1), at any particular

reference speed. However practically the controller adjusts duty cycle values in

close boundaries of the ideal duty cycle instead of having two predefined states

[34]. Therefore there is no need to know desired duty cycle states for different

speed operations and it is suitable for the application with the frequent change

of speed such as the in-wheel motors.

Stable performance of the BLDC motor control drive is critical in application

such as EV with respect to the safety point of views. In this chapter stability

75


of the proposed sensorless BLDC motor drive using digital PWM controller is

analysed through Lyapunov’s second method stability criterion (refer to the Ap-

pendix A, Section Four). Milivojevic et al. [77] are discussed stability analysis

of a FPGA-based controller of the BLDC motor using PWM technique based on

Lyapunov stability method. Stability analysis results in introducing limit con-

ditions for DHmax and DLmin (two predefined duty cycle states) that define the

stable operation range of the BLDC motor drive. Effect of the load torque and

rate of change of load torque are not considered in the analyses that are impor-

tant in EV application due to the frequent change of load on tires. The effect of

load torque is also considered in the discussed stability analysis in this chapter.

Speed error is considered as the structural variable or switching surface of the

PWM controller to apply the Lyapunov stability condition.

s = ωref − ω (5.9)

Candidate Lyapanuv function introduced in [77] is used for stability analysis

of digital PWM controller.

C(s(x)) =1

2sT (x)s(x) (5.10)

According to the Lyapunov stability criterion, PWM controller is stable equi-

librium if the Lyapunov candidate function is locally positive at s(x) = 0, then

its derivative should be locally negative (dCdt< 0⇒ ∂C

∂s∂s∂t< 0). In the other word

the control system is Lyapunov stable if s and its first derivative s, have opposite

signs [34].

Mathematical equations of electrical and mechanical systems of the BLDC

motor can be expressed as,

DVDC = Ri+ Ldi

dt+Keω (5.11)

Kti− Tl = jdω

dt+ βω (5.12)

From now on it is assumed that torque constant and back-EMF constant are

equal (K) in this chapter. A second order differential equation with ω as the

76


state variable can be derived by substituting the current value from equation

(5.12) into the equation (5.11).

d2ω

dt+ a1

dω

dt+ a2ω − b1D + b2Tl + b3

dTldt

= 0 (5.13)

where constants a1, a2, b1, b2, b3 are defined as below to simplify the equation

(5.13).

a1 = (βL+ jR

jL) (5.14)

a2 = (βR +K2

jL) (5.15)

b1 =KVDCjL

(5.16)

b2 =R

jL(5.17)

b3 =1

j(5.18)

State variable x in the candidate function represents speed of the motor [77].

Therefore derivative of switching surface function is,

s =∂s

∂ω

dω

dt= −dω

dt(5.19)

Solving equation (5.13) for derivative of speed and substitute the result in

equation (5.19) results in,

s = a1ω + a2

∫ω.dt− b1

∫D.dt+ b2

∫Tl.dt+ b3Tl (5.20)

According to the Lyapunov stability criterion, if s is negative (actual speed is

more than reference speed) then s should be positive and if s is positive (actual

speed below reference speed) s should be negative. Therefore limit conditions of

the digital PWM speed controller’s duty cycle for system to be Lyapunov stable

77


are [34],

a1b1

dω

dt+a2b1ω +

b2b1Tl +

b3b1

dTldt

> D − ε if s < 0 (5.21)

a1b1

dω

dt+a2b1ω +

b2b1Tl +

b3b1

dTldt

< D + ε if s > 0 (5.22)

Ideal performance of the digital PWM controller concludes to have a constant

duty cycle if the actual speed of the BLDC motor is exactly as same as the

reference speed (dωdt

= 0). Furthermore if it is assumed that there is no load

torque change during operation of the motor (dTldt

= 0); then equations (5.21) and

(5.22) can be merged as,

D = (βR +K2

KVDC)ωref + (

R

KVDC)Tl (5.23)

Duty cycle condition for stability analysis of the digital PWM controller for

the constant torque and speed applications is expressed by equation (5.23). As the

controller does not work ideal in practice and the motor speed fluctuates around

the reference speed; therefore the duty cycle values chosen by the PI controller

oscillate around the ideal duty cycle value expressed by equation (5.23). However

change of the speed and load torque must be considered for stability analysis of

the BLDC motor in the electric vehicle that torque and speed parameters changes

continuously due to frequent start, stop, acceleration and deceleration.


The proposed back-EMF based sensorless commutation technique of the BLDC

motor is simulated in Simulink. The BLDC motor specifications used in the

simulation model are given in Table 2.5 on page 22. The reference commutation

signal is generated based on the back-EMF ZCD of the measured line voltage

of phase A of the BLDC motor. The line voltage of phase A is measured with

respect to the negative terminal of the VSI DC link. An embedded Matlab code

has been written to detect zero crossing points of the back-EMF based on the

discussed method. The reference commutation signal is set to logic one at the

78


rising ZCD point and is set to logic zero at the falling ZCD point. The main

three commutation signals of the BLDC motor are generated by implementing

the electrical degree delays calculated from the equation (5.8). Commutation

signal of the measured phase has 30 electrical degree delay and the other two

commutation signals have 150 and 270 electrical degree delays from the reference

signal respectively.

The generated commutation signals are decoded and the correct switching

signals are applied to the inverter. MOSFET switches are used to model the three

phase voltage source inverter. The inverter is switched in a six-step sequence to

direct the current to the three-phase BLDC motor. Digital PWM speed controller

is implemented to control speed of the motor. A Matlab file is embedded in

Simulink model to generate a duty cycle controlled PWM signal. Duty cycle

of the PWM signal is determined by a PI controller based on the speed error.

Duty cycle controlled PWM signals is multiplied to switching signal of the upper

switches in each phase of the VSI. The start-up algorithm proposed by Iizuka

et al. [74] is used to run the BLDC motor up to the point that the proposed

sensorless drive is able to detect the back-EMF voltage and control the motor.

The proposed sensorless drive simulation model of the BLDC motor is tested

for 2000 RPM reference speed of the PWM controller under 5 N.m torque load.

Line voltage of phase A, corresponding back-EMF of phase A and zero crossing

detected points by controller are shown in Figure 5.4 [34]. As can be seen zero

crossing points of the back-EMF occurs exactly when the line voltage value pass

through half of the inverter DC supply. There are two zero crossing points during

each electrical cycle of phase voltage; one at rising edge and the other at the falling

edge of the line voltage. These ZCD points are the reference for the commutation

signal to set high and low respectively.

Zero crossing points and the commutation signal of phase A of the BLDC

motor are shown with respect to the electrical degree in Figure 5.5 [34]. As it

is magnified in the figure, the commutation signal of phase A is delayed exactly

30 electrical degree from back-EMF zero crossing points. Commutation signal of

each phase is logic one for 180 electrical degree and logic zero for the other 180

electrical degree during one full electric rotation of the BLDC motor.

79


Figure 5.4: Line voltage, Back-EMF and ZCD points of phase A of BLDC motor

Figure 5.5: Zero crossing points and the commutation signal of phase A

80


Current and commutation signal of phase A of the BLDC motor are shown

in Figure 5.6. As can be seen in the figure, current flows at the same time

of rising edge of the corresponding commutation signal. At each commutation

sequence one phase is connected to the positive terminal of inverter DC link

(current entering the phase winding), and one phase is connected to the negative

terminal of inverter DC link (current exiting the phase winding) and the third

phase is not excited (current is zero). Therefore as can be seen in the figure, phase

current has one positive cycle, one negative cycle and two unexcited periods (zero

current) during one full electrical rotation of the BLDC motor. Zero crossing

points of back-EMF occur in the period that phase current is zero.

Figure 5.6: Current, commutation signal and ZCD points of phase A

Speed response of the BLDC motor sensorless drive and chosen duty cycles

by PI controller are shown in Figure 5.7 [34]. By considering the operation of the

BLDC motor under constant speed and load torque, the ideal PWM duty cycle

percentage calculated from the equation (5.23) according the motor parameters

and 2000 RPM reference speed is 75%. As can be seen PI controller has chosen

duty cycles around the ideal value.

81


Figure 5.7: Speed response of the BLDC motor and duty cycle values selected byPI controller

Figure 5.8: State plane of digital PWM speed controller

82


State plane of the PWM speed controller is plotted in Figure 5.8. It can

be seen that the PWM controller keeps the BLDC motor speed at the stable

equilibrium around the switching surface at 2000 RPM. Therefore the proposed

sensorless drive of the BLDC motor using digital PWM speed controller is Lya-

punov stable.

The BLDC motor model is tested in brake condition of an electric vehicle

from constant speed and torque operation to full stop position. Brake condition

is chosen to study the proposed sensorless drive using PWM speed controller

performance under variable speed and load torque conditions. The BLDC motor

model is run at 2000 RPM under 5 N.m torque load (constant speed and torque

operation) and a soft brake is applied at t = 1 s to the motor and vehicle is

supposed to stop at t = 3 s. Brake duration is two seconds for the electric

vehicle to stop completely (zero speed). It is assumed that brake is applied both

electrically, decreasing reference speed of controller to zero and mechanically,

increasing the load torque on the wheels. Speed and torque characteristics of the

BLDC motor during brake condition are shown in Figure 5.9 [34].

Figure 5.9: Speed and torque characteristics of the BLDC motor during brake

83


The BLDC motor has a stable speed response and speed follows the reference

speed of the controller. Produced electric toque and torque ripples of the BLDC

motor is increased after the mechanical brake is applied to the motor. Maximum

torque ripples occurs when the BLDC motor speed is around 1300 RPM. The

produced electric torque of the motor is zero when vehicle is fully stopped.

Ideal, estimated and chosen duty cycle values by the PI controller during the

brake condition are shown in Figure 5.10 [34]. The first graph shows the ideal

duty cycle values calculated from equation (5.23). Changes of speed and load

torque of the BLDC motor are not considered in the first graph. The second

graph shows the estimated duty cycle values calculated from equations (5.21)

and (5.22). Changes of speed and load torque of the BLDC motor are considered

in the second graph. The third graph shows the simulation results for duty cycle

values chosen by the PI controller. As can be seen in the figure, duty cycle

values of the simulation results are following pattern of the estimated duty cycle

values. Therefore simulation results validate correctness of the equations (5.21)

and (5.22) for variable speed and torque operation condition of the BLDC motor.

Figure 5.10: Duty cycle values during the brake condition

84


State plane of the PWM speed controller during the brake condition is plotted

in Figure 5.11. As can be seen in the figure, the system remains at the stable

equilibrium point around the reference speed at each instant of time during the

brake condition. Simulation results show that the PI controller has selected the

correct duty cycle values to keep the BLDC motor drive stable during the brake

condition.

Figure 5.11: State plane of digital PWM speed controller during the brake

5.6 Experiment Results

Effectiveness of the proposed sensorless drive of the BLDC motor using digital

PWM speed controller is investigated through experiment. Experimental test rig

of the BLDC motor is same as reported in Chapter 4, Section 5. A low voltage de-

velopment board of microchip using PIC18F4231 microcontroller is programmed

to test the proposed sensorless BLDC motor drive. PIC microcontroller is also

programmed to implement the digital PWM speed controller of the BLDC motor

in a closed loop scheme. One of the in-built Hall Effect sensors of the motor is

85


used to estimate the actual speed of the motor. MOSFET switches are employed

in the three phase voltage source inverter to supply the BLDC motor.

Experimental speed response of the BLDC motor from the halt position up

to 2000 RPM reference speed of the PWM controller under 0.1 N.m torque load

is shown in the Figure 5.12 [34]. It takes around two seconds till the proposed

sensorless commutation algorithm to be able to sense the back-EMF and speed

up the motor to the reference speed. Experimental speed response of the BLDC

motor drive using sensors is also shown in Figure 5.13 to highlight the starting

delay in sensorless BLDC motor drives. The speed response of the sensorless

BLDC motor drive oscillates around 2027 RPM. Speed error is 1.35% of the

speed controller reference speed that is acceptable.

Figure 5.12: Experimental speed response of the sensorless BLDC motor drive

Three phase commutation signals of the BLDC motor that are generated

by the proposed sensorless method are shown in Figure 5.14 [34]. As shown

in the figure, they are logic for 180 electrical degree and there is 120 electrical

degree phase differences between commutation signals. Phase differences between

commutation signals are implemented based on the electrical degree time delays

(refer to the equation (5.8) on the page 74).

86


Figure 5.13: Experimental speed response of the BLDC motor drive using sensors

Figure 5.14: Generated commutation signals by sensorless drive of BLDC motor

87


PWM switching signals that are applied to the upper side switches of the VSI

are shown in Figure 5.15 [34]. The Ideal duty cycle percentage calculated from

the equation (5.23) based on the experimental motor specification and operation

condition is 60.31%. As shown in figure, duty cycle values chosen by controller

vary from 47% to 67% that are in the close boundary of the ideal duty cycle.

Figure 5.15: PWM switching signals applied to the upper side switches of VSI

Line voltage and corresponding generated commutation signal of the phase

C of sensorless drive of the BLDC motor are shown in Figure 5.16. As can

be seen phase C line voltage of the motor is exactly in the same phase with

the corresponding generated commutation signal. Experimental results prove

effectiveness and stable performance of the sensorless drive of the BLDC motor

using digital PWM speed controller.

Performance of the digital PWM speed controller is also studied in the context

of a light weight electric vehicle. The proposed PWM speed controller is applied

to the in-wheel BLDC motors of the concept four wheel drive electric vehicle.

The in-wheel BLDC motor set-up and the concept light weight four wheel drive

EV are shown in Figure 5.17.

88


Figure 5.16: Line voltage and commutation signal of the phase C of BLDC motor

Figure 5.17: The in-wheel BLDC motor set-up in a light weight EV

89


Four similar three phase in-wheel BLDC motors designed for electric motor

cycle application are used as the drive train of the concept EV. Specifications of

the in-wheel BLDC motors are given in Table 3.3 on the page 46. Permanent

magnet rotor position detection of the in-wheel motors are based on inbuilt Hall

Effect sensors. Each in-wheel motor has its own individual control drive; however

all of the motors operate with the same speed controlled by the acceleration pedal

of the vehicle. Acceleration pedal provide a control analogue voltage signal to

the speed controller drive of in-wheel BLDC motors. The reference speed control

input voltage varies from 0.7 volts for zero speed to 3.6 volts for the full speed

rotation. In-built drum brakes of the motor hubs are used as mechanical brake of

the electric vehicle. Four 12 volts lead-acid batteries are used to supply inverters

of the in-wheel motors in the vehicle.

The concept light weight four in-wheel drive EV is tested for three different

speed operating condition of the in-wheel BLDC motors on a flat road. Line volt-

age and corresponding commutation signal of one of the in-wheel BLDC motors

at different operating condition are shown in Figures 5.18.

Levels of the reference input voltage to the speed controllers provided by the

acceleration pedal are 1.7 volts at low speed, 2.8 volts at moderate speed and 3.6

volts at full load speed operating conditions. In-wheel BLDC motors drive 6 Amps

at full speed operating condition. Therefore according to the manufacturer data-

sheet, the in-wheel motors are producing 2.93 N.m torque at 546.7 RPM. The

BLDC motor line voltages are measured with respect to the negative terminal

of the inverter DC power supply. As can be seen in the figure, duty cycle of

the PWM signal increases based on the input reference speed of the controller.

At full speed operating condition duty cycle of the PWM signal is 100% and

the maximum possible voltage is applied to the in-wheel motors. Experimental

results confirm the correct and stable performance of the proposed digital PWM

speed controller of the BLDC motor.

90


Figure 5.18: Line voltage and commutation signal of the in-wheel BLDC motorat different operating condition of the light weight EV

91


5.7 Conclusion

Sensorless commutation techniques of the BLDC motors have been widely used

in industrial applications in recent years. An optimized sensorless commutation

technique based on zero crossing detection of back-emf voltage of only one phase

of the BLDC motor is discussed in this chapter. In this method back-EMF zero

crossing points are detected through the measured line voltage of one phase of

the motor with respect to the negative terminal of the VSI DC link. Measuring

only one phase voltage of the BLDC motor instead of all three phase voltages,

remarkably reduce the cost, noise susceptibility, sensing components and wiring of

the motor drive. The proposed sensorless method is simulated and tested through

the experiment. Simulation and experimental results prove correct and stable

performance of the proposed sensorless drive of the BLDC motor. Simplicity, low

cost, low noise susceptibility and ease of implementation of the control technique

on a single chip microcontroller or a digital signal processor are advantages of the

proposed sensorless commutation method of the BLDC motor as the drive train

of high performance electric vehicles.

A digital PWM switching technique is implemented to control speed of the

proposed sensorless BLDC motor drive in a closed loop scheme. A PI controller

is utilized to select the duty cycle of the PWM controller instead of setting two

predefined duty cycle values. Stability of the proposed sensorless drive of the

BLDC motor using digital PWM speed controller is analysed through Lyapunov

second method. Stability analysis results in deriving a novel condition for duty

cycle of the PWM signal based on the motor parameters and operating condition

of the motor such as speed and load torque. Validity of the presented stabil-

ity analysis condition is verified through simulation and experiment. Effective

performance of the digital PWM speed controller is also tested in a light weight

electric vehicle using four in-wheel BLDC motors. Experimental results show the

stable performance of the electric vehicle using digital PWM speed controllers in

different operating condition.

92

Chapter 6

Fault Diagnosis of the BLDC

Motor Drive for EV Application


Safe operation of electric vehicles is of the prime concerns in automotive in-

dustry. Various Fault Tolerant Control Systems have been developed for electric

motors to diagnose and handle the motor faults and maintain the motor per-

formance in post-fault condition in the last decades. Implementing FTCS’s in

control drives of the in-wheel BLDC motors increase reliability, robustness and

safety of the electric vehicles. In this chapter, two fault tolerant control systems

are proposed to handle inverter switch faults and position sensors (Hall Effect

sensors) failure in the BLDC motor drives. Fault diagnosis in both proposed

FTCS’s are based on Discrete Fourier Transform (DFT) analysis of line voltages

of the BLDC motor. A four wheel drive EV using in-wheel BLDC motors is mod-

elled to study and analyse the EV performance under various drive train fault

conditions. Simulation results show instability of the electric vehicle immediately

after inverter open circuit switch fault occurrence.

A BLDC motor drive is modelled in Simulink to study the motor performance

under fault conditions. The BLDC motor model was validated by experimental

data under no fault condition. Various VSI switch faults and Hall Effect sensor

faults are applied to the validated BLDC motor model. Expert systems are de-

93

6. Fault Diagnosis of the BLDC Motor Drive for EV Application

signed to detect and identify various VSI switch faults and Hall Effect position

sensors failure. Multidimensional fault diagnosis knowledge based tables are de-

veloped by analysing simulation results of the BLDC motor model under various

fault conditions. The proposed fault diagnosis systems are capable of detecting

fault occurrence and identify faulty switch or faulty sensor. Simple strategies are

recommended to remove the faults and keep the BLDC motor drive operation in

post-fault condition. Simulation results and developed knowledge based tables

are validated through experimental data. The proposed fault tolerant control sys-

tems are simple, do not need excessive computations and can be executed with

the main control program of the BLDC motor.

6.2 Introduction

BLDC motors are popular as drive train in traction applications such as hybrid

electric vehicles and pure electric vehicles. Stable motor operation is important on

the overall EV drive train performance and directly effects on safety of the vehicle.

Control of the BLDC motor mainly depends on the accurate detection of the

permanent magnet rotor position that leads to choose the correct voltage space

vectors to switch voltage source inverter and supply the BLDC motor. Therefore

any malfunction of the position detecting sensors or the VSI switches degrade the

BLDC motor performance. Three Hall Effect sensors with 120 electrical degree

phase difference are used to detect rotor position of the BLDC motor in simulation

model and experimental test rig in this chapter.

This chapter presents two novel fault tolerant control systems for inverter

switch faults and position sensors failure in the BLDC motor drives. The pre-

sented FTCS’s are focused on the three phase star connected BLDC motors. Fault

tolerant control systems for the four-phase, five-phase and six-phase BLDC mo-

tors are the prospective potential research studies. Overall model of a 3 phases,

4 poles star connected BLDC motor and its VSI drive are shown in Figure 6.1

[79].

As shown in the figure, control system and variable voltage source inverter

are main sections of the BLDC motor drives. Control system is responsible for

decoding and choosing the three phase inverter switching signals to commutate

94


Figure 6.1: Overall BLDC motor drive model

95


the BLDC motor. Six step conducting algorithm is used to switch inverter and

supply voltage to the BLDC motor. Commutation signals, phase currents, phase

back-EMF and line voltages of the BLDC motor and on state switches of VSI

with respect to the electrical degree of the rotor are shown in Figure 6.2 [80].

Accurate rotor positioning and correct performance of the inverter switches are

the key factors on the BLDC motor performance [12].

Figure 6.2: BLDC motor output characteristics and VSI switching steps

96


A digital PWM controller is implemented to control speed of the BLDC motor.

A duty cycle controlled PWM signal is multiplied by the switching signals of the

inverter to control the average output voltage of the VSI. Behaviour of the BLDC

motor are compared for various PWM switching modes under normal and critical

condition (refer to Appendix A, Section E). The BLDC motor shows the most

robust performance when PWM signals are applied to all inverter switches [5].

In this chapter duty cycle controlled PWM signal is applied to all switches of the

inverter in simulation and experimental set-up.

In applications where safety is critical such as an electric vehicle, any fault

or failure in propulsion system results in an accident or a hazard. Therefore

implementing various electric motor fault tolerant control systems is necessary in

electric vehicles [81]. Generally a FTCS is responsible to do the following tasks,

• Fault diagnosis (fault detection and identification);

• Fault isolation;

• Remedial action.

Fault diagnosis systems are responsible to detect and identify the fault. The

faulty section must be isolated immediately to avoid any further damage to the

system after fault detection. Finally, appropriate remedial actions should be

taken to keep the system working with the maximum possible efficiency in the

post-fault condition [12].

Various faults may occur in stator, rotor, position sensors or voltage source

inverter of the BLDC motor drives. Possible common faults in each section of

the BLDC motor are summarized in Table 6.1 [79]. Faults have various effects on

the BLDC motor performance; some faults degrade the motor performance and

cause severe damage if they last longer and some others cause the motor failure

and stop operation in few seconds after fault occurrence. Therefore various fault

diagnosis algorithms must be implemented in the BLDC motor drive, however

there is a priority on applying fault isolation and remedial strategies if number

of successive faults occur at the short time intervals [79].

97


Table 6.1: Common Faults in the BLDC Motor DriveSection Fault type Description

Stator Short circuit of Three phase

windings Three phase to ground

Two phase

Two phase to ground

One phase to ground

Turn to turn fault

Open circuit of It may happen by some

windings inverter faults

Change of resistance Overheating

Overloading

Rotor Eccentricity

Asymmetry

Rotor unbalanced

Rotor magnet damage

Misalignment

Bearing fault

Inverter Switch faults Open circuit fault

Short circuit fault

DC link fault Short circuit to ground

Capacitor bank fault

Position Sensors breakdown Flaws in the core

sensors Change in the bias current

Change in core magnetic property

Change in induced magnetic field

Unbalanced positioning

98


Fault diagnostic algorithms of the BLDC motor are classified by employing

signal analysis, model based and knowledge based methods [13]. In the signal

analysis based methods fault is detected through comparison of the extracted

features of the motor signals with the ideal signals at normal operating condition.

The main advantage of this method is that it does not need the BLDC motor

model for fault detection, however fault diagnosis is not fast compared to the

other two methods [81]. Parameter estimation techniques are normally used

to diagnose the fault in the model based techniques. Fault diagnosis in this

method is quite fast and can be used for online fault monitoring, however an

exact dynamic model of the BLDC motor is needed. Reported research works

by Liu et al. [13] and Moseler et al. [82] are good examples of a model-based

fault diagnosis system for the BLDC motors. Expert systems using fuzzy logic or

neural network are developed for fault diagnosis in the knowledge based methods

[13]. The knowledge can be gathered either through an experienced engineer with

a thorough understanding of the BLDC motor system, or via a comprehensive

study of the BLDC motor dynamics through simulation model of the motor [81].

Fault diagnosis systems presented in this chapter are knowledge based expert

systems that use signal analysis methods.

A fault tolerant control system for inverter open circuit switch faults of the

BLDC motor for EV application is discussed in the next section. Inverter short

circuit switch faults are removed by six fast acting fuses that are connected in

series with inverter switches. Therefore a short circuit fault is treated as an

open circuit fault by the proposed FTCS [79]. Dynamic parameters of a four

wheel drive EV using in-wheel BLDC motors are analysed under inverter open

switch fault of the motor. Fault diagnosis technique and remedial strategies are

discussed. A fault tolerant control system for position sensors breakdown in

the BLDC motors is also presented in the fourth section of this chapter. The

BLDC motor behaviour is analysed under various position sensor faults. Position

sensor fault diagnosis algorithm and the remedial strategy to rectify the fault

are discussed. Effectiveness of the both proposed fault diagnosis techniques are

investigated through experimental results.

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6.3 Inverter Open Circuit Switch Faults

A fault tolerant control system of the BLDC motor drive for open circuit fault

of the inverter switches is discussed in this section. It is assumed that short circuit

faults of the inverter switches are removed by series fast acting fuses. Therefore

a short circuit fault effects on the BLDC motor as an open circuit fault.

Precious research works are published on FTCS’s for VSI switch faults in

the BLDC motor drives [81-85]. A fault diagnosis algorithm based on wavelet

analysis of the inverter DC link current are introduced for the BLDC motors [83].

Wavelet analysis needs massive computational efforts and increase complexity

of the FTCS. Open circuit fault diagnosis of the inverter switches based on the

BLDC motor stator current is reported by Park et al. [84]. The proposed method

is simple and does not need massive computations. However current based fault

diagnosis methods are not capable of distinguishing the fault occurrence either is

inside the motor or the inverter [85].

Four various VSI switch faults diagnosis methods based on different voltage

sensing points of the BLDC motor are discussed [85]. Voltage errors are used

for fault detection in the presented techniques. Fault diagnosis time is signifi-

cantly reduced; however the proposed methods have major limitations. Neutral

point voltage of the BLDC motor is required for two of the presented techniques.

Since neutral point of the BLDC motor is not stable during high frequency PWM

switching therefore the proposed fault diagnosis techniques based on neutral volt-

age sensing are not consistent in a closed loop control scheme. Pattern of the

BLDC motor line voltages change continuously in applications such as electric

vehicles with the frequent changes of the speed and load torque. Therefore the

ideal reference voltages should also change dynamically to find the correct voltage

errors of the BLDC motor [81]. A fault diagnosis system based on the voltage

of lower switches in each phase of the VSI is proposed for voltage fed PWM

inverter systems by Yu et al. [86]. Noise susceptibility of sensors used inside

the inverter due to high frequency PWM switching signals is the main limitation

of the proposed method [79]. A fault diagnosis technique based on the neural

network system is proposed to detect the most of common inverter faults in in-

duction motor drives for EV and hybrid EV applications [87]. Features used for

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fault detection in neural network are extracted from torque, voltage and current

signals of the induction motor. The proposed fault diagnosis technique is fast and

accurate. Complex algorithm, high number of used sensors and need of sensing

the neutral voltage point of the BLDC motor are main drawbacks of the proposed

method [81].

Effect of the inverter switch faults on dynamic parameters of a four in-wheel

drive EV are analysed in this section. A knowledge based fault diagnosis tech-

nique is proposed to detect and identify the inverter open circuit fault in the

following. The proposed fault diagnosis algorithm is validated through experi-

mental data. Some of the presented simulation and experimental results in this

section have been published by Tashakori et al. [79][81].

6.3.1 EV Dynamics Analysis under Inverter Open Circuit

Switch Fault

Simulation models are mainly developed to decrease the cost and length of

the design process of advanced systems. They can be used to study behaviour of

the systems under abnormal condition. Modelling of the hybrid electric vehicles

has been grown since 1970s [88]. Simulation models are used to study various

aspects of the vehicle; for instance vibration, vehicle handling, noise, vehicle

performance, safety, stability, component testing and etc. [89]. However there are

few simulation models of pure electric vehicle to study effect of the in-wheel motor

faults on the EV performance. A four in-wheel drive EV using four BLDC motors

is modelled in Simulink using Simscape library to analyse the EV performance

during inverter open circuit switch faults in the in-wheel motors. Schematic

diagram of the four in-wheel drive EV model is shown in Figure 6.3 [79].

Four BLDC motors using digital PWM speed controllers are modelled as drive

train of the EV. Specifications of the BLDC motor model BLK423S manufactured

by Anaheim Automation Company are used in the EV model (refer to Table 3.2

on the page 43). The duty cycle PWM control signal is applied to all six switches

of the VSI. A vehicle body in longitudinal motion from SimDriveline library is

used as vehicle body model. Specifications of the vehicle’s body used in the EV

model are given in Table 6.2.

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Figure 6.3: Schematic diagram of the four in-wheel drive EV model

Table 6.2: Specification of the Vehicle’s Body Used in the EV Model


Mass 1200 Kg

Number of in-wheel motors 4 -

Horizontal distance from 1.4 m

CG to front wheels

Horizontal distance from 1.6 m

CG to rear wheels

CG height above ground 0.5 m

Frontal area 3 m2

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6.3.1.1 No Fault Condition

The electric vehicle model is tested to run from stall position up to 2000 RPM

reference speed of the in-wheel BLDC motors on a flat road (zero inclination)

under no fault condition. Speed response of the electric vehicle is shown in Km/h

in Figure 6.4 [79]. Speed of the vehicle is increased gradually up to 113 Km/h in

50 seconds after start moving.

Figure 6.4: EV speed under no fault condition

Normal forces applied to the tires from the electric vehicle’s body are shown

in Figure 6.5 [79]. As can be seen in the figure, force on tires changes fast during

the first few seconds when the EV has the highest acceleration rate. The force

on the rear tires are more than the front tires at starting time that is due to

central gravity (CG) position of the vehicle’s body. As the vehicle approaches to

the constant speed region, tire forces are almost constant but forces on the front

tires are more than those on the rear tires.

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Figure 6.5: Normal tire forces under no fault condition

In-wheel BLDC motors start from stall position up to 2000 RPM reference

speed of digital PWM controllers. Speed responses of the in-wheel BLDC motors

are shown in Figure 6.6 [79]. As can be seen the in-wheel electric motors operate at

the same speed to keep the EV moving stable in a straight line. Speed fluctuation

of the front wheels at starting point is due to the slip of front tires.

Electric torques produced by in-wheel motors are shown in Figure 6.7 [79]. As

can be seen the in-wheel motors deliver the same electric torques to the wheels

that keep the electric vehicle moving stable. Effect of the front tires slip can also

be seen in the produced torques of the in-wheel BLDC motors A and B. In-wheel

motors are produced the initial torque of 350 N.m to overcome the high inertia

of the vehicle’s body at stall position. Produced electric toques by the in-wheel

motors are reduced to 30 N.m as the speed of the vehicle reaches to 113 km/h.

104


Figure 6.6: Speed responses of the BLDC motors under no fault condition

Figure 6.7: Torque responses of the BLDC motors under no fault condition

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6.3.1.2 VSI Open Circuit Fault

The electric vehicle model is run from stall position up to 2000 RPM refer-

ence speed of the in-wheel BLDC motors. Open circuit fault switch S1 (refer to

Figure 6.1) of the inverter is applied to the in-wheel BLDC motor A at t = 40 s

while the EV speed is reached to 110 km/h. Other three in-wheel motors were

operating under no fault condition. No fault protection system is implemented

to the in-wheel BLDC motor drive to see the maximum fault effect on the EV

performance. Speed characteristic of the electric vehicle under open circuit fault

of switch S1 is shown in Figure 6.8 [79]. As can be seen in the figure speed of the

EV is suddenly decreased and the vehicle is unstable after fault occurrence.

Figure 6.8: EV speed under open circuit fault of switch S1

Normal forces applied to the tires from the electric vehicle body under open

circuit fault of the inverter switch S1 in the BLDC motor A are shown in Figure 6.9

[79]. Applied Vertical forces to the tires oscillate with high amplitude ripples after

the fault occurrence that is due to the unbalanced load torques on the wheels in

post-fault condition.

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Figure 6.9: Normal tire forces under open circuit fault of switch S1

Speed and torque responses of the in-wheel BLDC motors under open circuit

fault of the inverter switch S1 in the BLDC motor A are shown in Figure 6.10 and

Figure 6.11 respectively [79]. High amplitude notches can be seen in speed and

torque responses of the faulty in-wheel motor (the BLDC motor A). The faulty

in-wheel motor is totally unstable and speed and torque responses of other three

in-wheel BLDC motors are also deteriorated after fault. Other three motors are

effected due to unbalanced distribution of the vehicle force on tires, load torques

on the motors, after fault occurrence (refer to the Figure 6.9).

The four wheel drive EV model is also tested for inverter open circuit fault

of switch S2 of the BLDC motor A. Simulation results (refer to the Appendix A)

show unstable performance of the EV and almost are same as the inverter open

circuit fault of switch S1. Therefore performance of the electric vehicle is not safe

under inverter open circuit faults. Sudden VSI switch fault occurrence put life

of the passengers at risk and makes the electric vehicle a hazard to other nearby

vehicles or people on the road.

107


Figure 6.10: Torque responses of the BLDC motors under open circuit fault ofswitch S1

Figure 6.11: Speed responses of the BLDC motors under open circuit fault ofswitch S1

108


Comparison of the EV performance under faulty condition with no fault con-

dition demonstrates the need of FTCS’s for the in-wheel motors to improve safety

of the electric vehicles.

6.3.2 Fault Diagnosis

In this section fault detection and identification algorithms of the inverter open

circuit switch faults of the three phase BLDC motors are presented. Any switch

fault of the VSI effects directly on the applied voltages to the BLDC motor.

Therefore pattern change of the BLDC motor line voltages are used for fault

diagnosis. Line voltages of the BLDC motor are measured with respect to the

negative terminal of the VSI DC link.

6.3.2.1 Fault Detection

Any pattern change of the line voltages of the BLDC motor for constant speed

and torque load condition is the signature of fault occurrence. Variations of the

motor speed and load torque should be considered in fault diagnosis. Discrete

Fourier transform analysis is used to detect pattern changes of the BLDC motor

line voltages. Frequency spectrum of the measured voltages are extracted from

equation (6.1) for the specific intervals of time. Power Spectral Density (PSD)

of the calculated frequency spectrum of the measured voltages of each phase

of the BLDC motor are calculated from equation (6.2) for each time interval.

Successive PSD values are compared to find the PSD errors of each phase from

equation (6.3).

V (f) =N−1∑n=0

Vne−j2πk n

N k = 0, 1, ..., N (6.1)

Em(f) = |V (f)|2 (6.2)

εm = Em(f)− Em−1(f) (6.3)

In practice, line voltages of the BLDC motor are sensed for specific intervals

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of time continuously while the motor is operating in constant speed and torque

load condition. The minimum time interval for correct fault detection is one

electrical rotation of the BLDC motor. The time of one electrical degree rotation

is inversely proportional to the BLDC motor speed. Fault occurrence is detected

if calculated PSD errors of the BLDC motor line voltages exceeds the predefined

limits. Five percent of the calculated PSD value of the BLDC motor line voltages

under no fault condition are set as the limit to avoid short term disturbance

detections.

6.3.2.2 Fault Identification

An expert system is developed to identify the faulty switch of inverter based on

studying the BLDC motor performance under faulty condition through the sim-

ulation model. The BLDC motor model is validated through the experimental

set-up. A three phase low voltage BLDC motor is used as an experimental test

motor. A Low voltage (LV) development board of microchip using PIC18F4231

micro-controller is programmed to control the experimental BLDC motor. Ex-

perimental test rig of the BLDC motor and specifications of the experimental test

motor used in simulation model are given in Chapter 4 (refer to Figure 4.8 and

Table 4.3).

Digital PWM controller is implemented to control speed of the BLDC motor.

An embedded code is written in Simulink model to produce a duty cycle controlled

PWM signal. High frequency PWM signal is applied to all switches of the inverter.

The experimental test BLDC motor and its simulation model are tested at 2000

RPM under 0.1 N.m load torque. Line voltage and Hall Effect signal of phase A of

the experimental set-up and the simulation model of the BLDC motor are shown

in Figure 6.12 [12]. Agreements between simulation and experimental results

validate the BLDC motor model. The validated BLDC motor model is also used

to develop fault diagnosis system for Hall Effect sensors failure in Section Five.

Inverter open circuit switch faults are applied to the validated BLDC motor

model and results are analysed to develop the fault diagnosis system. Power spec-

tral density errors of the line voltages of the BLDC motor model are calculated

under healthy operating condition and after fault occurrence. A knowledge based

110


Figure 6.12: Line voltage and Hall Effect signal of phase A of BLDC motor

table is developed to identify the fault by analysing the calculated PSD errors of

the motor line voltages in various fault conditions.

Open Circuit Fault of Switch S1 is applied at t = 0.5 to the BLDC

motor model. Line voltages of the BLDC motor during the open circuit fault of

switch S1 are shown in Figure 6.13 [79]. Positive amplitude spikes can be seen

in line voltage of the faulty phase of the motor. The line voltage of phase A has

totally distorted after the fault occurrence. Line voltages of phase B and phase C

111


are also changed, though the voltage of phase A has the most variations. There-

fore the PSD error of the phase A voltage should be the maximum. Calculated

Power spectral density errors of the BLDC motor line voltages for open circuit

fault of switch S1 are given in Table 6.3 [79].

Figure 6.13: Line voltages of BLDC motor during open circuit fault of switch S1

Table 6.3: Simulation PSD Values for Open Circuit of S1

Description Phase A Phase B Phase C

PSD before fault [Em−1(f)] 963.95 958.56 954.67

PSD after fault [Em(f)] 1.0862e+05 1019.76 932.06

PSD error [εm] 107656.05 61.2 -22.61

Open Circuit Fault of Switch S2 is applied at t = 0.5 to the BLDC

motor model. Line voltages of the BLDC motor during the open circuit fault

of switch S2 are shown in Figure 6.13 [79]. As can be seen the line voltage of

112


phase A, the faulty phase, has negative amplitude spikes. Voltage of phase B

and C are also significantly distorted, however variations of the phase A voltage

is much more than other two phases. Calculated power spectral density errors

of the BLDC motor line voltages for open circuit fault of switch S2 are given in

Table 6.4 [79].

Figure 6.14: Line voltages of BLDC motor during open circuit fault of switch S2

Table 6.4: Simulation PSD Values for Open Circuit of S2



PSD after fault [Em(f)] 1.0912e+05 1076 1115.5

PSD error [εm] 108156.05 117.44 160.83

Line voltages of the BLDC motor are also studied under open circuit faults of

other inverter switches through the validated simulation model. The calculated

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PSD errors for open circuit faults of phase A switches of the inverter can be

generalized for the other two phases due to symmetry of the BLDC motor [12].

Two flags are defined for fault diagnosis; Switch Fault Flag (SFF) for each phase

of the motor to identify the faulty switch and Fault Phase Flag (FPF) to identify

the faulty phase. Numeric values are assigned to SFF of each phase and FPF

according to the linguistic variables based on the calculated PSD errors as below

[79],

• SFF is ‘-1’ if PSD error is over the limits and negative;

• SFF is ‘0’ if PSD error is in the limits;

• SFF is ‘1’ if PSD error is over the limits and positive;

• FPF is ‘0’ if no fault is detected;

• FPF is ‘1’ if maximum PSD error related to phase A;

• FPF is ‘2’ if maximum PSD error related to phase B;

• FPF is ‘3’ if maximum PSD error related to phase C.

A multidimensional knowledge based table are developed to identify the faulty

switch based on simple quasi-fuzzy if-then rules according to the assigned numeric

values of the flags. Developed knowledge based if-then rules for inverter switches

faults diagnosis are shown in Table 6.5 [79].

Table 6.5: Proposed Knowledge Based Table for Inverter Switches Faults Diag-nosis

Fault type SFF SFF SFF FPF

phase A phase B phase C

No fault 0 0 0 0

Open circuit S1 1 1 0 1






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As the fault diagnosis is based on the PSD errors of line voltages, there is no

need to know the exact patterns of the BLDC motor line voltages in advance.

This is the most advantage of the proposed fault diagnosis system compared to

the previously reported systems for the EV application.

6.3.3 Experimental Results

The low voltage development board of the microchip is modified to test the

open circuit fault of the inverter switches and Hall Effect sensors. The modified

control board of the BLDC motor is shown in Figure 6.15 [79]. There is an in-built

over current protection circuit in the control board that avoids phase currents to

exceed a predefined limit.

Figure 6.15: The modified LV development board control drive of BLDC motor

Open circuit faults of phase A switches of the VSI is applied to the BLDC

motor while it is running at 2000 RPM under 0.1 N.m load torque. Line voltages

of the motor under open circuit faults of switches S1 and S2 are shown in Fig-

ure 6.16 and Figure 6.17 respectively [79]. As can be seen the voltage of faulty

phase, phase A, is totally deteriorated for both open circuit faults of switches of

115


S1 and S2. Voltages of phase B and phase C are also distorted, though the phase

A voltage has always the most variations same as the simulation results.

Figure 6.16: Line voltages of BLDC motor under open circuit fault of switch S1

116


Figure 6.17: Line voltages of BLDC motor under open circuit fault of switch S2

117


Power spectral density errors of the BLDC motor line voltages for experi-

mental open circuit faults of switches S1 and S2 are given in Tables 6.6 and 6.7

respectively. AS can be seen PSD errors of the line voltage of phase A is maxi-

mum for both open circuit faults of switches of S1 and S2 compared to the other

two phases. However the PSD errors of experimental results are not as large as

the PSD errors of simulation results due to the effect of over current protection

circuit of the control drive.

Table 6.6: Experimental PSD Values for Open Circuit of S1



PSD after fault [Em(f)] 348.34 339.26 319.36

PSD error [εm] 33.36 17.09 -2.99

Table 6.7: Experimental PSD Values for Open Circuit of S2




PSD error [εm] 37.99 22.15 17.77

The experimental BLDC motor drive is also tested under open circuit switch

faults of other legs of the inverter. Experimental results are similar to the open

circuit switch faults of leg A of inverter. The most pattern changes always belong

to the line voltage of faulty phase. Discussed experimental results validate the

fault diagnosis algorithm developed by simulation results for inverter switches

open circuit faults.

118


6.3.4 Remedial Strategy

EV drive train faults must be isolated and rectified in a mean time to main-

tain the maximum possible vehicle performance and safety of the passengers.

Therefore after the faulty switch is identified, the corresponding faulty leg is dis-

connected from the inverter by implemented controlled switches in each leg of the

VSI.

There are various inverter reconfiguration topologies to maintain the BLDC

motor operation in post-fault condition. Inverter topologies are not in the scope

of this chapter; however two simple inverter topologies are discussed for post-

fault condition with respect to the electric vehicle application. Reconfiguration

of the three phase voltage source inverter to the four switches topology inverter

for the BLDC motors in post-fault condition is recommended by Lee et al. [90].

Schematic diagram of the proposed four switches topology inverter is shown in

Figure 6.18 [81].

Figure 6.18: Schematic diagram of the proposed four switches topology inverter

After isolating the faulty leg of the inverter, the corresponding phase of the

BLDC motor is connected to the midpoint of DC link of the VSI through con-

trol switches Sa; Sb; Sc. Reconfiguring to the four switch mode operation of the

119


inverter avoids further major faults inside the in-wheel BLDC motor drive. How-

ever performance of the in-wheel BLDC motor is degraded. This method can be

used to increase the reliability of the in-wheel BLDC motors just for a short time

in post-fault condition until the vehicle gets the proper service [81].

A modular and easy controlled fault tolerant VSI using a redundant leg is

proposed for the BLDC motor drives by Errabelli et al. [91]. The faulty leg of

the inverter is replaced by the redundant leg in post-fault condition. Schematic

diagram of the proposed fault tolerant inverter of the BLDC motor with a re-

dundant leg is shown in Figure 6.19 [81]. The corresponding faulty phase of the

BLDC motor is connected to the redundant leg of the inverter through indepen-

dent control switches Sra; Srb; Src. In this technique performance of the in-wheel

BLDC motor is not degraded compared to the four switches topology inverter;

however its manufacturing cost is due to the redundant leg. Since the reliability

and safety of the in-wheel motor drive is more important than the cost in EV

application, therefore the proposed fault tolerant VSI with a redundant leg is

recommended for in-wheel motors.

Figure 6.19: Schematic diagram of the proposed fault tolerant inverter with aredundant leg

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6.4 Position Detection Sensors Failure

In this section a fault tolerant control system for position sensors failure of

a three phase BLDC motor are discussed. Commutation of the BLDC motor is

done with respect to the position sensor signals. Therefore failure or malfunction

of the position sensors effects directly on the motor performance. On the other

hand faults in position sensor may result in immediate over current of the BLDC

motor drive under high load torques [92]. Unbalanced positioning of the Hall

Effect sensors by manufacturer is not in the sensor failure categories and scope

of this study. However it increases low-frequency harmonics in the torque ripples

and degrades performance of the BLDC motor [93]. The main faults that may

result in failure of a Hall Effect sensor in BLDC motors are [94]:

1. Flaws in the sensor’s core due to corrosion, cracks, residual magnetic fields

and core breakage;

2. Effect of temperature variations on the magnetic properties of the ferrite

core;

3. Effect of mechanical shocks on the orientation of the induced magnetic field

in the sensor;

4. Changes in the bias current of the sensor.

There are few reported research works on fault tolerant control system of

position sensors failure in the BLDC motors. Major sensor faults of an Interior

Permanent Magnet Motor (IPMM) as the propulsion system of an electric vehicle

are discussed [92]. Position sensor faults are detected through difference between

the calculated rotor angle and the actual rotor angle. Permanent magnet rotor

position angle is calculated by a sensorless algorithm based on extended EMF in

rotating reference frame [95]. Sensorless mode control of the permanent magnet

motor is recommended as a remedial strategy. Complexity of the BLDC motor

sensorless control drives and transition algorithms to the sensorless mode are the

main drawbacks of the proposed method [12]. Performance of the BLDC motor

is analysed under Hall Effect sensors faults for lunar rover wheel application

[96]. Effects of the position sensor faults on inverter switching signals and phase

currents are shown only through a simulation model; however simulation results

121


are not discussed. There is also no discussion on fault diagnosis techniques and

remedial strategies to rectify the fault in the paper [80].

Fault diagnosis technique and a novel remedial strategy for Hall Effect sensor

failure in the BLDC motors is discussed in this section. Performance of the BLDC

motor under position sensors fault are analysed through a validated simulation

model. A knowledge based fault diagnosis technique is developed to detect the

faulty position sensor based on simulation results analysis. Finally effectiveness

of the proposed fault diagnosis algorithm is proved through experimental data.

Some of the presented simulation and experimental results in this section have

been published by Tashakori et al. [12][80].

6.4.1 Performance of the BLDC Motor under Position

Sensor Faults

Behaviour of the BLDC motor drive under various position sensor faults are

studied through a validated simulation model (refer to Figure 6.12 on the page 111)

to develop the fault diagnosis algorithm. The BLDC motor experimental set-up

is the same as the one that is used and explained for inverter switch faults in the

previous section.

Position sensor faults are applied to the validated simulation model of the

BLDC motor while the motor is running under stable and healthy condition.

Then output characteristics of the motor such as line voltages, phase currents,

speed and torque characteristics are analysed. Hall Effect sensor failure is divided

into two categories based on the output signal of the sensors. Output signal of

the sensor is constant high (logic ‘1’, Ha = 1), sensor is short circuit to it power

supply, or it is constant low (logic ‘0’, Ha = 0), sensor is open circuit [12].

Behaviour of the BLDC motor is discussed under both fault conditions of the

corresponding Hall Effect position sensor of phase A.

6.4.1.1 Hall Effect Signal is Constant Zero

Hall Effect sensor fault Ha = 0 is applied to the validated BLDC motor model

at t = 0.5 s while the motor is ruining at 2000 RPM under 0.1 N.m load torque.

Speed and torque responses of the BLDC motor under Ha = 0 fault condition are

122


shown in Figure 6.20 [12]. As can be seen in the figure, speed and torque of the

BLDC motor is unstable and out of the control under Ha = 0 fault condition.

Figure 6.20: Speed and torque responses of BLDC motor under Ha = 0 faultcondition

Line voltages of the BLDC motor under Ha = 0 fault condition are shown in

Figure 6.21 [12]. Line voltages of the BLDC motor are measured with respect

to the negative terminal of DC link of VSI. As shown in figure line voltages of

all phases are changed due to the direct effect of Hall Effect sensor faults on the

switching signal of the inverter. Hall Effect sensor faults also increase the BLDC

motor torque ripples and degrade its performance.

Effect of the various position sensor faults on the switching signals of the

VSI are summarized in Table 6.8 [80]. Each position sensor fault changes two

switching signals of the inverter to constant zero. This effect can be considered as

an open circuit fault of two inverter switches at the same time [80]. As given in

Table 6.8 switching signals of the switches S1 and S6 are constant zero (switches

S1 and S6 are open circuit) under Ha = 0 fault condition.

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Figure 6.21: Line voltages of BLDC motor under Ha = 0 fault condition

6.4.1.2 Hall Effect Signal is Constant One

Hall Effect sensor fault Ha = 1 is applied to the validated BLDC motor model

at t = 0.5 s while the motor is ruining at 2000 RPM under 0.1 N.m load torque.

Speed and torque responses of the BLDC motor under Ha = 1 fault condition

are exactly similar to the Ha = 0 fault condition. Torque ripples of the BLDC

motor is increased and the motor is not stable after fault occurrence. However

switching signals of the switches S2 and S5 remain open circuit after Ha = 1 fault

occurrence that is not the same as Ha = 0 fault condition. Therefore line voltages

of the BLDC motor should be different in Ha = 1 fault condition compared to

Ha = 0 fault condition. Line voltages of the BLDC motor under Ha = 1 fault

condition are shown in Figure 6.21 [12].

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Table 6.8: Effect of the Various Sensor Faults on the Switching Signals of the VSI

Fault type Effected switches of VSI Switches status

Ha = 0 S1 , S6 Open

Ha = 1 S5 , S2 Open

Hb = 0 S3 , S2 Open

Hb = 1 S1 , S4 Open

Hc = 0 S5 , S4 Open

Hc = 1 S3 , S6 Open

6.4.2 Fault Diagnosis

Correct space voltage vectors are chosen according to the electrical position

of the permanent magnet rotor by decoding the commutation signals (refer to

Table 3.1). As can be seen there is no electrical position of the rotor that all

sensor signals are one or zero. Therefore addition of the commutation signals,

Hf , are either one or two under healthy operating condition of the BLDC motor.

Hf = Ha +Hb +Hc (6.4)

Hall Effect sensor faults change the value of Hf during one electrical rotation

of the rotor. If Hf = 3, it shows that one of the position sensor signals is constant

one and if Hf = 0, it means that one of the position sensor signals is constant

zero. The minimum required time for fault detection is time of one electrical

rotation of the rotor that is quite fast. Hall Effect sensors Fault detection Flag

(HFF) is introduced to detect the fault occurrence as below [80],

• HFF is set ‘0’ if Hf = 1 or Hf = 2;

• HFF is set ‘1’ if Hf = 3;

• HFF is set ‘-1’ if Hf = 0.

Position sensors faults are detected if the Fault Detection Flag is not zero.

Detection of the fault and fault type are possible through HFF; however faulty

sensor cannot be identified through fault detection flag. DFT analysis of the

line voltages of the BLDC motor is used to identify the faulty sensor. Same as

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Figure 6.22: Line voltages of BLDC motor under Ha = 1 fault condition

discussed for the inverter switch faults, line voltages of the BLDC motor are mea-

sured and saved for specific intervals of time continuously. Frequency spectrum

of the measured line voltages are calculated from equation (6.1). Power spec-

tral density of the line voltages frequency spectrum are calculated from equation

(6.2). PSD errors of the sensed line voltages between successive time intervals

under constant speed and torque load are signature to identify the faulty sensor

[12].

Single-sided amplitude spectrum of line voltage of phase A of the BLDC motor

under no fault and Hall Effect sensor faults of phase A (Ha = 0 and Ha = 1) are

shown in Figure 6.23 [80]. As can be seen High amplitude harmonics are added

to frequency spectrum of the phase A line voltage of the BLDC motor under

position sensor faults condition. Therefore energy density of the line voltage fre-

quency spectrum under position sensor fault is not same as the healthy operating

condition of the BLDC motor. PSD errors of all three phase line voltages of the

BLDC motor under Hall Effect sensor faults of Ha = 0 and Ha = 1 are calculated

126


and given in Tables 6.9 and 6.10 respectively [80].

Figure 6.23: Amplitude spectrum of the phase A line voltage of BLDC motor

Table 6.9: PSD Values for Ha = 0 Fault ConditionDescription Phase A Phase B Phase C

PSD before fault [Em−1(f)] 957 942 938

PSD after fault [Em(f)] 807 1044 1083

PSD error [εm] -150 102 145

Table 6.10: PSD Values for Ha = 1 Fault ConditionDescription Phase A Phase B Phase C

PSD before fault [Em−1(f)] 957 942 938

PSD after fault [Em(f)] 1109 925 802

PSD error [εm] 152 -17 -135

127


Hall Effect sensors fault Identification Flag (HIF) is introduced for each phase

of the BLDC motor. Numeric values are assigned to the fault identification flag

of each phase based on the sign of the calculated PSD errors. FIF numeric values

are assigned as below [80],

• HIF is set ‘-1’ if PSD error is negative;

• HIF is set ‘1’ if PSD error is positive.

A multidimensional table is developed to diagnose the position sensor faults of

the BLDC motor based on fault detection and identification flags. Fault diagnosis

algorithm is developed based on the gathered knowledge from characteristics of

the BLDC motor model under various Hall Effect sensor faults. Simplicity and no

need of knowing the exact pattern of the BLDC motor line voltages for different

speed and torque loads are the main advantages of the proposed fault diagnosis

method. The proposed multidimensional knowledge based table for Hall Effect

sensors fault diagnosis in the BLDC motors is shown in Table 6.11 [12].

Table 6.11: Proposed Knowledge Based Table for Position Sensor Faults Diagnosis

Fault type HIF HIF HIF HFF

phase A phase B phase C

No fault X X X 0

Ha = 0 -1 1 1 -1

Ha = 1 1 -1 -1 1

Hb = 0 1 -1 1 -1

Hb = 1 -1 1 -1 1

Hc = 0 1 1 -1 -1

Hc = 1 -1 -1 1 1

Hall Effect sensor fault occurrence and fault type are detected through the

fault detection flag, when HFF is not zero, and faulty sensor is identified based

on the fault identification flag of each phase of the BLDC motor according to the

Table 6.11.

128


6.4.3 Experimental Results

Correctness of the proposed fault diagnosis system for Hall Effect sensors fail-

ure of the BLDC motor is investigated through experiment. Low voltage devel-

opment board of microchip has been modified to test Hall Effect sensors faults of

the experimental BLDC motor. Three half-bridge gate drivers using MOSFETs

as shown in Figure 6.24 are used as the VSI drive of BLDC motor [97]. Signal

PWM0 is applied switching signal of the switch Q0 that is equivalent of the switch

S1 in Figure 6.1.

Figure 6.24: Half-bridge gate driver and inverter of LV development board

Open circuit, Ha = 0, and short circuit, Ha = 1, faults of the corresponding

Hall Effect sensors of phase A are applied to the experimental BLDC motor while

it is running at 2000 RPM under 0.1 N.m torque load. Speed oscillations and

high acoustic noise are the first observations in the BLDC motor drive after fault

occurrence. Six LED lights are provided on the control board that are on when

the corresponding switching signals are logic high. Status of the LED lights

for open circuit and short circuit faults the corresponding Hall Effect sensors of

phase A are shown in Figure 6.25 [80]. Equivalent switching signals of S1 and S6

(PWM1 and PWM4) of VSI drive of the experimental BLDC motor are constant

129


zero for Ha = 0 fault. Equivalent switching signals of S5 and S2 (PWM5 and

PWM0) of inverter are also constant zero for Ha = 1 fault.

Figure 6.25: Corresponding switching LED lights on the control board underposition sensor faults of phase A: (a) Open circuit fault (b) Short circuit fault

Therefore simulation results presented in Table 6.8 on the page 125 are val-

idated through experimental results. Line voltages of the experimental BLDC

motor under Ha = 0 and Ha = 1 fault conditions are shown in Figure 6.26 and

Figure 6.27 respectively [80].

As is shown in the figures, line voltages of the experimental BLDC motor are

changed during faults compared to the healthy operating condition. Single-sided

amplitude spectrum of the experimental BLDC motor line voltage of phase A

under no fault and Hall Effect sensor faults of phase A are shown in Figure 3.3

[80]. High amplitude harmonics can be seen on the phase A line voltage of

the experimental BLDC motor under position sensor fault conditions. However

amplitude and frequency of the added harmonics are different for Ha = 0 fault

compared to Ha = 1 fault. Therefore power spectral density of the line voltages

are different according the fault types in post-fault condition.

130


Figure 6.26: Line voltages of the experimental BLDC motor under Ha = 0 fault

131


Figure 6.27: Line voltages of the experimental BLDC motor under Ha = 1 fault

132


Figure 6.28: Amplitude spectrum of the phase A line voltage of experimentalBLDC motor

Power spectral density (PSD) errors of the experimental BLDC motor line

voltages under Ha = 0 and Ha = 1 fault conditions are calculated and given

in Table 6.12 and Table 6.13. Calculated PSD errors of the experimental line

voltages under Hall Effect sensor fault condition validate the calculated PSD

errors of the simulation line voltages. However experimental PSD errors are not

as large as simulation ones that is due to the inbuilt over current protection circuit

of the controller board.

The experimental BLDC motor is also tested under the corresponding Hall

Effect sensors faults of other phases. Calculated experimental PSD errors of the

line voltages under Hall Effect sensors faults of other two phases are similar to the

presented experimental results for phase A of the BLDC motor. Experimental

results validate the proposed knowledge based fault diagnosis table for position

133


Table 6.12: PSD Values for Experimental Ha = 0 Fault Condition




PSD error [εm] -13.8576 7.4703 46.9078

Table 6.13: PSD Values for Experimental Ha = 1 Fault Condition




PSD error [εm] 15.2162 -2.6486 -12.074

sensors breakdown of the BLDC motors.

6.4.4 Remedial Strategy

The signal of the faulty Hall Effect sensor should be disconnected from the

BLDC motor drive after fault detection. Signal of the faulty sensor is generated

by the controller by implementing 120 electrical degree delay to one of the other

available Hall Effect signals. Electrical degree delays are calculated in time as

it is discussed in Chapter 5. If the BLDC motor speed does not change during

commutation intervals, the time for one full electrical rotation of the permanent

magnet rotor of the BLDC motor can be calculated from equation (6.5).

Tone electrical degree =60

P2

(360× ωref )(6.5)

where P is number of the motor poles and ωref is there reference speed of the

controller. Effectiveness of the proposed equation to calculate the correct time

of electrical delays of the BLDC motors is proved through simulation and exper-

imental results in the previous chapter [34].

An embedded Matlab code is implemented in the BLDC motor simulation

model to test performance of the proposed FTCS for Hall Effect sensors fault.

Ha = 0 fault is applied to the BLDC motor model at t = 0.5 s while the motor

134


Figure 6.29: Speed response of the fault tolerant controlled BLDC motor drive

is running at 2000 RPM under 0.1 N.m torque load. Speed characteristics of the

fault tolerant controlled BLDC motor drive is shown in Figure 6.29 [12].

As can be seen in the figure, fault tolerant control drive of the BLDC motor

detect, identifies and maintains performance of the motor in post-fault condition.

The commutation signal of phase A is generated correctly by implementing the

calculated the time delays. Fault diagnosis time in simulation model is about

0.113 second which is fast and acceptable for the BLDC motor drives.

6.5 Conclusion

In this chapter two fault tolerant control systems are presented for inverter

switch faults and Hall Effect position sensors failure of the BLDC motor drives.

Performance of a four in-wheel drive EV are studied under open circuit faults of

inverter switches S1 and S2 in one of the front in-wheel BLDC motors through

simulation. Results show that EV becomes unstable immediately after VSI switch

faults occurrence. Behaviour of the BLDC motor is analysed under various in-

verter switches and position sensor faults through a validated simulation model.

135


Results show that these faults effect directly on the applied voltages and cause

the BLDC motor to be unstable. Therefore necessities of implementing FTCS’s

for the in-wheel BLDC motors are inevitable to increase safety and reliability of

the electric vehicles.

The proposed fault diagnosis systems are based on the DFT analysis of the line

voltages of the BLDC motor. Knowledge based tables are developed to diagnose

the faulty switch of inverter and the faulty sensor by analysing power spectral

density errors of the BLDC motor line voltages. Since the PSD errors of the

line voltages are signature of fault detection and identification in both proposed

fault diagnosis systems; the exact pre-knowledge of the line voltages pattern for

various reference speed or torque loads is not needed.

Fault tolerant three phase VSI with a redundant leg is recommended to isolate

and rectify the inverter switch faults. A simple method is also introduced to

generate the signal of faulty position sensor by implementing the time delays

between Hall Effect signals in post fault condition. Effectiveness of the proposed

fault diagnosis algorithms and the knowledge based tables are validated through

the experiment results.

136

Chapter 7

Conclusion

Electric vehicles have been considered for the green transportation since 1980’s.

Electric motors are the main propulsion system in the electric vehicles. Different

electric motors are used as drive train of electric vehicles. Induction and BLDC

motors are the most popular drive trains used in the electric vehicles available in

the world market by the manufacturers. In-wheel motor technology has been one

of the research interests in automotive industry in the last decade. Important

technical requirements of an in-wheel motor are: high torque at low speeds; high

torque/power to size ratio; high efficiency; high dynamic response; robustness;

low noise susceptibility. Performance of the brushed DC, induction, BLDC and

switched reluctance motors are compared according to the in-wheel technology

requirements through simulation.

Simulation models of the motors are tested under the same normal and crit-

ical conditions. Simulation results show better torque/speed characteristics and

faster dynamic response of the BLDC motor compared to the other motors in

the normal condition. Brushed DC motor also has high torque at low speeds and

fast dynamic response in the normal condition. Low efficiency, low speed ranges

and periodic need of maintenance are the main disadvantages of the brushed DC

motors that are not suitable for the EV application. On the other hand, the

induction motor has better performance compared to the other motors in the

critical condition. Low efficiency at high speeds, slow dynamic response and slip

are the major drawbacks of the induction motor for the in-wheel motor applica-

tion. The switched reluctance motor has also a good performance in the critical

137

7. Conclusion

condition, however high amplitude torque ripples and noise susceptibility are its

main limitations. The BLDC motor performance is not suitable for in-wheel mo-

tor application specifically during electrical faults. Implementing fault tolerant

control systems improve reliability and robustness of the BLDC motors. Com-

parison results and discussions of the motors presented in Chapter 2 conclude to

the point that the BLDC motor is the most suitable motor for the in-wheel motor

electric vehicles.

Precise electronic control of the in-wheel motors improves safety, efficiency and

overall performance of the electric vehicles. An accurate simulation model of the

in-wheel motors are needed to study performance of the motor for different control

algorithms. Therefore a three phase star connected BLDC motor model with an

ideal back-EMF has been modelled in the Chapter 3. The presented model is

based on Laplace transform of the mathematical equations of the BLDC motor.

Simulation results of the motor model are validated through the experimental

data of a three phase in-wheel BLDC motor hub designed for electric motor cycle

application. Simplicity and the ideal back-EMF waveforms of the proposed model

make it useful for performance analysis of the various BLDC motor drives.

Mechanical output power of the electric motors is dependant on speed and

produced electric torque. Therefore, precise torque control of the in-wheel mo-

tors improves performance of the electric vehicles. Direct torque control switching

technique of the BLDC motor is discussed in Chapter 4. DTC drive of the BLDC

motor is simulated in Simulink and results are compared with those of the con-

ventional switching technique of the motor. Comparison results show effective

control of the BLDC motor torque and lower torque ripples by the DTC switch-

ing technique compared to the conventional switching technique. The proposed

DTC drive is implemented to test the experimental low voltage BLDC motor.

Experimental results show capability of the DTC drive in effective control of the

torque in various conditions.

Sensorless drives of the BLDC motor are widely used in various applications

in recent years. The novel back-EMF based sensorless drive of the BLDC motor

presented in Chapter 5, is based on back-EMF zero crossing detection of only

one phase of the BLDC motor. Commutation signal of one phase is generated

by 30 electrical degree delays from ZCD points of the back-EMF voltage. Com-

138

7. Conclusion

mutation signals of the other phases are generated based on 120 electrical degree

phase delays from the first commutation signal. Cost, sensing circuitry and noise

susceptibility of the BLDC motor sensorless drives are decreased by the proposed

method. The proposed sensorless technique of the BLDC motor is simulated,

built practically and tested experimentally. Good agreements between simula-

tion and experimental results justify the correct performance of the proposed

sensorless BLDC motor drive.

A digital PWM speed controller using a PI controller to select the duty cycle

of the PWM signal is implemented in the proposed sensorless drive of the BLDC

motor. Stability of the BLDC motor drive using digital PWM speed controller is

analysed through Lyapunov method. A novel condition is presented to calculate

the ideal duty cycle of PWM signal based on the motor parameters to keep

the BLDC motor stable at the reference speed. Correctness of the introduced

stability analysis condition is investigated through simulation and experiment.

Experimental and simulation results validate the introduced stability analysis

condition for PWM speed controller of the BLDC motor.

Fault tolerant control systems effectively improve performance of the electric

motors under fault condition. Performance of the electric motors in post-fault

condition in applications such as drive train of electric vehicles where safety is the

most concern is critical. Fault tolerant control systems of the BLDC motors used

in EV application are discussed in Chapter 6. A four in-wheel drive EV using

BLDC motors are modelled in Simulink dynamic parameters of the modelled EV

is studied under inverter open circuit switches faults in one of the front in-wheel

BLDC motors. Results show immediate instability of the electric vehicle after

faults occurrence. Results of this study show that using fault tolerant control

systems for the in-wheel motors is inevitable.

Performance of the BLDC motor is studied under various inverter switches

and position sensors faults through a validated simulation model. Two fault

diagnosis systems are proposed for inverter open circuit switch faults and Hall

Effect position sensors failure based on DFT analysis of line voltages of the BLDC

motor. Multidimensional knowledge based tables are introduced to diagnose the

faulty switch of inverter and the faulty position sensor through spectral energy

density errors of the motor line voltages. One of the advantages of these fault

139

7. Conclusion

diagnosis systems is that the exact line voltages pattern of the BLDC motor for

various reference speed or torque loads is not needed. Fault tolerant inverters

with a redundant leg are recommended to isolate and rectify the inverter switch

faults of the in-wheel BLDC motor drives. A simple and reliable method, same

as the method introduced for sensorless drive of the BLDC motor in Chapter 5,

is also recommended to generate the signal of faulty sensor by implementing the

time delays from the other healthy sensors. Inverter open circuit switch faults

and position sensor faults are tested on a low voltage BLDC motor through

experiment. The proposed fault diagnosis algorithms and the knowledge based

tables are validated through the experimental results too.

140

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Industrial Electronics, vol. 59, no. 1, pp. 343–351, 2012. 70, 76, 77

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IEEE Transactions on Industrial Electronics, vol. 56, no. 8, pp. 3040–3049,

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151

Appendix A

A Reference Links of Table 2.1

Reference links of Table 2.1 Drive train specification of the electric vehicles avail-

able in the world market, on page 11 are given in order of vehicle numbers as

below. All links are accessed on 10 August 2013.

1. http://evworld.com

2. www.buddyelectric.no

3. www.byd.com/na/auto/e6.html

4. www.theelectriccarcorporation.co.uk

5. http://blade.id.au

6. www.lightningcarcompany.co.uk/Lightning/home.html

7. www.mitsubishi-motors.com.au/vehicles/i-miev

8. www.morgan-motor.co.uk/mmc/researchanddev/pluse.html

9. www.wmgta.com/en/products/mycar-nev.aspx

10. http://ecomove.dk/qbeak-presentation

11. www.mahindrareva.com/Reva-Home.html

12. www.mercedes-amg.com

152

Appendix A

13. www.smartaustralia.com.au

14. www.teslamotors.com

15. http://thinkev.leftbankcompanies.com

16. www.stevensvehicles.co.uk

B Details of the motor models in Chapter 2

All motors are simulated using machine blocks of the SimPowerSystems library

in Matlab/Simulink. Block diagram of the motor drive models are shown below,

Figure B1: Block diagram of the induction motor drive model

153

Appendix A

Figure B2: Block diagram of the DC motor drive model

Figure B3: Block diagram of the switched reluctance motor drive model

154

Appendix A

Figure B4: Block diagram of the BLDC motor drive model

C State Space Equation of BLDC Motor

Phase voltage difference state space equations of BLDC motor can be derived

as below from equations (3.1), (3.2), (3.3), (3.7) on page 35.

Vab = R(ia − ib) + (L−M)d

dt(ia − ib) + Eab (C1)

Vbc = R(ib − ic) + (L−M)d

dt(ib − ic) + Ebc (C2)

where ia + ib + ic = 0, therefore by neglecting mutual inductance and rearranging

equations (C1) and (C2),

diadt

= −RLia +

2

3L(Vab − Eab) +

1

3L(Vbc − Ebc) (C3)

dibdt

= −RLib +

2

3L(Vab − Eab) +

1

3L(Vbc − Ebc) (C4)

155

Appendix A

Then state space equations of BLDC motor are:

iaibωm

=

−RL

0 0

0 −RL

0

0 0 −RL

iaibωm

+

23L

13L

0

− 13L

13L

0

0 0 1J

Vab − EabVbc − Ebc

Te − Tl

(C5)

ia

ib

ic

ωm

=

1 0 0

0 1 0

−1 −1 0

0 0 1

iaibωm

(C6)

D Clarke Transformation

Clarke transformation is a mathematical transformation employed to simplify

the analysis of three-phase circuits.

iαiβiγ

=2

3

1 −1

2−1

2

0√

32−√

32

12

12

12

iaibic

(D1)

In a balanced system where ia + ib + ic = 0, iγ = 0. Therefore two of currents

are enough to calculate α and β components. The transform simplifies to,[iα

iβ

]=

2

3

[1 01√3

2√3

][ia

ib

](D2)

E Lyapunov’s Second Method for Stability

Alexandr Mikhailovich Liapunov, in his PhD thesis titled as “The general prob-

lem of stability of motion” in 1982, proposed two methods for stability analysis

[98]. The second stability analysis method, that is universally used nowadays, is

introduced as follow:

156

Appendix A

For a system having a point of equilibrium at x = 0, consider a Lyapunov

candidate function V (x) which has an analogy to the potential function of classical

dynamics. Therefore considering the function V (x) : Rn → R, System is stable

in the sense of Lyapunov,

if V (x) ≥ 0 if and only if x = 0 (positive definite), then V (x) = dV (x)dt≤ 0 if

and only if x = 0 (negative definite).

F Comaprison of Different PWM Switching Tech-

niques of The BLDC Motor

A BLDC motor drive using a digital PWM speed controller is modelled in

Simulink. A PI controller dynamically chooses the duty cycle of the PWM signal

based on the speed error of the motor. Specifications of the BLDC motor used

in simulation model are given in Table 3.3 on the page 46. In this section the

BLDC motor drive performance for various PWM switching modes is compared

under healthy and inverter switch faults conditions. To distinguish various PWM

switching modes numbers are assigned to them as below,

1. Mode one : PWM signal is applied to upper side switches the inverter;

2. Mode Two : PWM signal is applied to lower side switches of the inverter;

3. Mode Three : PWM signal is applied to all of the inverter switches.

F.1 Normal Condition

BLDC motor model is tested for 1500 RPM reference speed and 10 N.m load

torque under no fault condition. Speed characteristics of the BLDC motor for

various PWM switching modes are shown in Figure F1 [5]. As can be seen

speed response of the BLDC motor for switching mode three has the lowest peak

overshoot where the highest one belongs to the mode one. High peak overshoot

speed response is not suitable for the in-wheel motors. Speed oscillation of the

157

Appendix A

BLDC motor around the reference speed of the controller is almost same for all

switching modes in the steady state condition.

Figure F1: Speed responses of BLDC motor for different PWM switching modes

Torque characteristics of the BLDC motor for various PWM switching modes

are shown in Figure F3 [5]. The BLDC motor produces same initial peak torque

for all switching modes. Torque ripples amplitude of the BLDC motor for mode

three is higher than other modes in the steady state situation. High amplitude

torque ripples is not desirable for the in-wheel motors.

Phase A terminal voltage of the BLDC motor for various PWM switching

modes are shown in Figure F4 [5]. Terminal voltage of the motor is measured

with respect to the negative terminal of the DC link of VSI. As is shown the

DC voltage of the inverter is chopped by PWM signal during the upper switch

conduction period and is zero during conduction of the lower switch for switching

mode one. As can be seen the conducting condition of switches in the mode Two

is exactly opposite of the mode one. DC voltage of the inverter is chopped by

PWM signal during both conduction periods of the upper and lower switches in

the mode Three. The line voltage pattern of the BLDC motor is a good signature

to recognise the applied PWM switching mode of the speed controller.

158

Appendix A

Figure F2: Torque responses of BLDC motor for different PWM switching modes

Figure F3: Torque responses of BLDC motor for different PWM switching modes

159

Appendix A

Figure F4: Line voltage of BLDC motor for different PWM switching modes

Duty cycle values chosen by the PI controller for various PWM switching

modes are shown in Figure F5 [5]. As can be seen the PI controller is chosen

various duty cycle values for each PWM switching mode. Duty cycle changes

limit in the PWM switching mode three is smaller compared to the other two

modes in the steady state condition.

F.2 Critical Condition

Critical condition is considered as mechanical shocks and inverter switch faults.

Mechanical faults are implemented as 30% sudden changes of load torque of the

BLDC motor. The speed control algorithm is same and the only difference is that

various switches of inverter are chosen for PWM switching in different modes. In

this section behaviour of the BLDC motor for open and short circuit faults of the

upper side switch of phase A in inverter is analysed.

160

Appendix A

Figure F5: Duty cycle chosen by PI controller for different PWM switching modes

F.2.1 Mechanical Shocks

Thirty percent changes of the load torque are applied at t = 0.4 s (increase of the

load) , t = 0.5 s (decrease of the load) and t = 0.6 s (decrease of the load) while

the BLDC motor is running at 1500 RPM under initial 10 N.m load toque. Digital

PWM speed controller keeps the BLDC motor stable during mechanical shocks

for all PWM switching modes. Torque characteristics of the BLDC motor under

mechanical shocks for various PWM switching modes are shown in Figure F6 [5].

Torque ripples amplitude of the BLDC motor in the PWM switching mode three

is almost same as other two methods for the higher load torque, however at the

low load torque it is remarkable more than the other two modes.

Duty cycle values chosen by the PI controller under mechanical shocks for

various PWM switching modes are shown in Figure F7 [5]. Changes limit of duty

cycles chosen by the controller for the switching modes one and two are much

more than the mode three under higher load torque and it is vice versa under

the lower load torque. Since the in-wheel motors should perform under high load

161

Appendix A

Figure F6: Torque responses of the BLDC motor under mechanical shocks fordifferent PWM switching modes

Figure F7: Duty cycle chosen by PI controller under mechanical shocks for dif-ferent PWM switching modes

torque, therefore behaviour of the BLDC motor and its speed controller are more

robust during mechanical shocks for the PWM switching mode three.

162

Appendix A

F.2.2 Inverter Switch Faults

Open and short circuit faults of the upper side switch of phase A of the inverter

is applied to the BLDC motor at t = 0.2 s while the motor is running at 1500

RPM under 10 N.m load torque. Speed responses of the BLDC motor for various

PWM switching modes under inverter switch faults are shown in Figure F8 [5].

Speed response of the motor are almost similar for all the PWM switching modes

during open circuit fault. The BLDC motor is lost the operating point and its

speed starts oscillating. Speed of the BLDC motor for the PWM switching mode

one during short circuit fault is much higher that the other two modes.

Duty cycle values chosen by the PI controller for various PWM switching

modes under inverter switch faults are shown in Figure F9 [5]. Duty cycle values

of the mode two and three are constant 100% during open circuit fault where in

the mode one it toggle between 0 and 100%. The PI controller for the PWM

switching mode one does not work during short circuit fault and the duty cycle

value is zero where in the other two modes duty cycle values changes from 0 to

100%. Although the BLDC motor is not stable during the inverter switch faults

for all the PWM switching modes but speed controller in the PWM switching

mode three shows more robust response during short circuit switch fault.

G EV Model Simulation Results under Inverter

Open Circuit Switch Fault

The four wheel drive electric vehicle model is run from stall position up to

2000 RPM reference speed of the in-wheel BLDC motors. Open circuit fault

switch S2 (refer to Figure 6.1 on page 95) of the inverter is applied to the in-

wheel BLDC motor A at t = 40 s while the EV speed is reached to 110 km/h.

Simulation results and output characteristics of the electric vehicle and in-wheel

motors under inverter open circuit fault condition are shown in Figures G1, G2,

G3 and G4.

163

Appendix A

Figure F8: Speed responses of the BLDC motor under inverter switch faults fordifferent PWM switching modes

164

Appendix A

Figure F9: Duty cycle chosen by PI controller under inverter switch faults fordifferent PWM switching modes

165

Appendix A

Figure G1: EV speed characteristics under open circuit fault of switch S2

Figure G2: Normal tire forces under open circuit fault of switch S2

166

Appendix A

Figure G3: Torque characteristics of the BLDC motors under open circuit faultof switch S2

Figure G4: Speed characteristics of the BLDC motors under open circuit fault ofswitch S2

167

Appendix B

168

1999

EV AMERICA

TECHNICAL

SPECIFICATIONS

Effective

October 1, 1999

Prepared byElectric Transportation Applications

1999 EV AMERICA TECHNICAL

SPECIFICATIONS

2

MINIMUM VEHICLE REQUIREMENTS

For a vehicle to be considered qualified as an EV America-USDOE “Production” level

vehicle, it must meet the minimum criteria defined by “shall” terminology utilized in the

Specification. [For clarity, the use of the word “Shall” defines minimum requirements,

whereas the use of the word “Should” defines design and performance objectives.]

Vehicles which cannot meet all of the “Shall” requirements will be considered Prototypes,

and will not be considered as having “passed” EV America. The following requirements

shall be met by any vehicle before it can receive EV America “Production” level status:

(1) Vehicles shall have a minimum payload of 400 pounds.

(2) For Conversion vehicles, OEM GVWR shall not be increased. Suppliers shall

provide the OEMs Gross Vehicle Weight Rating (GVWR).

(3) For conversion vehicles, OEM Gross Vehicle Axle Weight Ratings (GAWR) shall

not be increased. Suppliers shall provide axle weights for the vehicle as delivered,

and at full rated payload.

(4) Seating capacity shall be a minimum of 2, (one driver and at least one passenger).

Suppliers shall provide seating capacity (available seat belt positions) for their

vehicle.

(5) Suppliers shall provide information on their selected battery manufacturer’s

recycling plan, including how it has been implemented.

(6) For conversion vehicles, the OEM passenger space shall not be intruded upon by

the battery, battery box or other conversion materials.

(7) Vehicles shall comply with the requirements of 49 CFR 571.105.S5.2.1, or

alternatively, 49 CFR 571.105.S5.2.2 for parking mechanisms.

(8) Vehicles shall have a minimum range between charges of at least 50 miles when

loaded with two 166-pound occupants and operated at a constant 45 mph.

(9) Vehicles shall comply with Federal Motor Vehicle Safety Standards applicable on

the date of manufacture and such compliance shall be certified by the

manufacturer in accordance with 49 CFR 567. Suppliers shall provide a

completed copy of Appendix B with their submittal, indicating the method of

compliance with each section of 49 CFR 571. If certification includes exemption,

the exemption number issued by the National Highway Transportation Safety

Administration (NHTSA), the date of it’s publication in the Federal Register and

the page number(s) of the Federal Register acknowledging issuance of the

exemption shall be provided along with Appendix B. Only exemptions for non-

applicable requirements shall be allowed.


SPECIFICATIONS

3

(10) Batteries and/or battery enclosures shall be designed and constructed in

accordance with the requirements of SAE J1766 FEB96. Further, batteries and

electrolyte will not intrude into the passenger compartment during or following

FMVSS frontal barrier, rear barrier and side impact collisions, and roll-over

requirements of 49 CFR 571.301. Suppliers shall provide verification of

conformance to this requirement.

(11) Batteries shall comply with the requirements of SAE J1718 APR97, and at a

minimum shall meet the requirements of NEC 625 for charging in enclosed spaces

without a vent fan.

(12) Concentrations of explosive gases shall not be allowed to exceed 25% of the LEL

(Lower Explosive Limit) in the battery enclosure. Suppliers shall describe how

battery boxes will be vented, to ensure any battery gases escape safely to

atmosphere during and following normal or abnormal charging and operation of

the vehicle.

(13) The battery charger shall be capable of recharging the main propulsion battery to

a state of full charge from any possible state of discharge in less than 12 hours, at

temperatures noted in Section 5.6.

(14) Chargers shall have the capability of accepting input voltages of 208V and 240V

single phase 60 Hertz alternating current service, with a tolerance of ±10% of

rated voltage. Charger input current shall be compatible with the requirements for

Level II chargers, and shall comply with the requirements of SAE J1772 OCT96

and/or SAE J1773 JAN95. Personnel protection systems shall be in accordance

with the requirements of UL Standard 2202, Published 1998.

(15) Chargers shall have a true power factor of .95 or greater and a harmonic

distortion rated at ≤ 20% (current at rated load).

(16) The charger shall be fully automatic, determining when “end of charge”

conditions are met and transitioning into a mode that maintains the main

propulsion battery at a full state of charge while not overcharging it, if

continuously left on charge.

(17) Vehicles shall not contain exposed conductors, terminals, contact blocks or

devices of any type that create the potential for personnel to be exposed to 50

volts or greater (the distinction between low-voltage and high voltage, as

specified in SAE J1127 JAN95, J1128 JAN95, et al.).

(18) Vehicles being tested shall be accompanied by non-proprietary manuals for parts,

service, operation and maintenance, interconnection wiring diagrams and

schematics, (with pricing for optional manuals). These documents shall either be

provided or available to the end user.


SPECIFICATIONS

4

(19) The vehicle shall include a state of charge indicator for the main propulsion

batteries.

20) Propulsion power shall be isolated from the vehicle chassis such that battery

leakage current is less than 0.5 MIU in accordance with UL Standard 2202,

Published 1998.

(21) Charging circuits shall be isolated from the vehicle chassis such that ground

current from the grounded chassis at any time while the vehicle is on charge or

the charger is connected to an off-board power supply does not exceed 5 mA, in

accordance with UL Standards 2202, Published 1998.

(22) Replacement tires shall be commercially available to the end user in sufficient

quantities to support the purchaser’s needs,

(23) The vehicle shall prevented from being driven with the key turned on and the

drive selector in the DRIVE or REVERSE position while the vehicle’s charge

cord is attached. Additionally, the following interlocks shall be present:

• The controller shall not initially energize to move the vehicle with the gear

selector in any position other than “PARK” or “NEUTRAL;”

• The start key shall be removable only when the “ignition switch” is in the

“Off” position, with the drive selector in “PARK;”

• With a pre-existing accelerator input, the controller shall not energize or

excite such that the vehicle can move under its own power from this

condition.

(24) All vehicles shall comply with the FCC requirements for unintentional emitted

electromagnetic radiation, as identified in 47 CFR 15, Subpart B, “Unintentional

Radiators.”

(25) Failure of a battery or battery pack shall be determined through a discharge test.

The discharge test shall be performed with the discharge current regulated to

achieve a C/1 discharge rate based on the ampere hour capacity of the battery

specified by the Supplier as required in Section 6.1 and with a battery

temperature of at least 77º F. Subsequent to receiving a full charge and

equalization, the battery shall be discharged at such current and temperature until

the terminal voltage of any cell in the battery drops below the voltage specified by

the Supplier as required in Section 6.3. The ampere hours delivered by the battery

to that point shall be calculated and shall become the actual battery capacity.

Failure of the battery shall be deemed to have occurred if the actual battery

capacity is not at least 80% of the nominal ampere hour capacity specified by the

Supplier as required by Section 6.1.

(26) Vehicles shall be equipped with an automatic disconnect for the main propulsion

batteries. They shall also have a manual service disconnect. These disconnects

shall be clearly labeled. [See Section 7.3]


SPECIFICATIONS

5

(27) Any conductive or inductive type charging systems shall be compatible with the

Personnel Protection requirements of SAE J1772 or J1773, as appropriate.

(28) Suppliers shall provide Material Safety Data Sheets (MSDS) for all batteries.

(29) Suppliers shall indicate the level of charge below which the batteries should not

be discharged and how the controller automatically limits battery discharge below

this level.

(30) Suppliers shall verify that the method(s) of charging the propulsion batteries and

the charging algorithm have been reviewed and approved by the battery

manufacturer.

(31) Regardless of the charger type used, the charger shall be capable of meeting the

requirements of Section 625 of the National Electric Code (NEC).

(32) If the vehicle is equipped with fuel fired heaters, the vehicle shall comply with the

requirements of 49 CFR 571.301.

(33) The vehicle shall have an on-board Battery Management System (BMS).

The following sections constitute the Technical Requirements of the Specification. Information

has been categorized according to component and/or function. These sections provide an

overview of the requirements and recommendations for Suppliers to use. This Technical

Specification establishes the minimum requirements for Production level electric vehicles, as

well as identifying design and performance objectives. Suppliers shall clearly describe the

vehicle they are proposing by completing a copy of Appendix A. Drawings should be provided

showing the installation, location and layout of the conversion components including the

batteries, motor and controller, and powered accessories. The drive line should also be

described, i.e., direct drive transmission, reduction gear ratio, etc.. Suppliers should include

any other information required to describe the vehicle.

No inference should be drawn by Suppliers or any other person that the measures listed in this

specification are sufficient to make the vehicle safe, and each Supplier shall acknowledge in

writing that 1) it is solely responsible for determining whether each vehicle offered for sale is

safe, and 2) it is not relying on EV America, Electric Vehicle Market Development Group

(EVMDG), the Procurement Management Board (PMB), or any of the EV America

participants, their Consultants, or the U.S. Government as having, by this specification and its

requirements, established minimally sufficient safety standards. This written statement shall be

provided in the Supplier’s proposal.

910 East Orangefair Ln. Anaheim, CA 92801 Tel. (714) 992-6990 Fax. (714) 992-0471 www.anaheimautomation.com

FEAT

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ECIF

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The powerful BLK42 Series are NEMA 42 Brushless DC Motors that are IP65 rated which meet the splash-proof requirements for most applications. The BLK42 Series has complete protection from debris and dustparticles. The sealed shafts further protect the motor providing longer life cycles. The BLK42 Series aresquare-bodied motors including an aluminum square-mounting flange to allow for easy installation, includesClass F insulation for higher temperature operation and has a maximum rated torque of 840 oz-in. TheBLK42 Series is available in three different stack lengths with varying power levels, has a rated speed of3000 RPM and utilizes Hall Sensor Feedback.

• NEMA Size 42 BLDC Motors• IP65 Rating• Complete Protection from Dust Particles• Can be Subjected to Wet Enviornments• Long Life and Highly Reliable• Cost Effective Replacement for Brush DC Motors• Hall Sensor Feedback• Available in Three Different Stack Lengths

L010749

BLK42 Series - Brushless DC MotorsBLK42 Series - Brushless DC Motors

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0003-V061-S124KLB 24 061 0003 0621 665 0071 4.83 24.0 1.3 16 03 1280.0 31 60.6

0003-V013-S124KLB 24 013 0003 0621 665 0071 2.61 25.0 52.4 021 63 1280.0 31 60.6

0003-V071-S224KLB 24 071 0003 0751 807 4212 94.74 23.0 4.2 46 13 9101.0 12.51 37.6

0003-V013-S224KLB 24 013 0003 0751 807 4212 52.02 64.0 24.3 021 63 9101.0 12.51 37.6

0003-V013-S324KLB 24 013 0003 0881 048 0452 6.42 83.0 59.2 021 63 3021.0 24.71 04.7

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910 East Orangefair Ln. Anaheim, CA 92801 Tel. (714) 992-6990 Fax. (714) 992-0471 www.anaheimautomation.com

WIR

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INFO

RM

ATION

SPECIFIC

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*Note: All units are mm

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IRFR2407IRFU2407

HEXFET® Power MOSFET

Seventh Generation HEXFET® Power MOSFETs fromInternational Rectifier utilize advanced processingtechniques to achieve extremely low on-resistance persilicon area. This benefit, combined with the fastswitching speed and ruggedized device design thatHEXFET power MOSFETs are well known for, providesthe designer with an extremely efficient and reliabledevice for use in a wide variety of applications.

The D-Pak is designed for surface mounting usingvapor phase, infrared, or wave soldering techniques.The straight lead version (IRFU series) is for through-hole mounting applications. Power dissipation levelsup to 1.5 watts are possible in typical surface mountapplications.

S

D

G

Parameter Max. UnitsID @ TC = 25°C Continuous Drain Current, VGS @ 10V 42ID @ TC = 100°C Continuous Drain Current, VGS @ 10V 29 AIDM Pulsed Drain Current 170PD @TC = 25°C Power Dissipation 110 W

Linear Derating Factor 0.71 W/°CVGS Gate-to-Source Voltage ± 20 VEAS Single Pulse Avalanche Energy 130 mJIAR Avalanche Current 25 AEAR Repetitive Avalanche Energy 11 mJdv/dt Peak Diode Recovery dv/dt 5.0 V/nsTJ Operating Junction and -55 to + 175TSTG Storage Temperature Range

Soldering Temperature, for 10 seconds 300 (1.6mm from case )°C

Mounting Torque, 6-32 or M3 screw 10 lbf•in (1.1N•m)

Absolute Maximum Ratings

VDSS = 75V

RDS(on) = 0.026Ω

ID = 42ADescription

3/1/00

www.irf.com 1

l Surface Mount (IRFR2407)l Straight Lead (IRFU2407)l Advanced Process Technologyl Dynamic dv/dt Ratingl Fast Switchingl Fully Avalanche Rated

PD -93862

D-Pak I-PakIRFR2407 IRFU2407

Parameter Typ. Max. UnitsRθJC Junction-to-Case ––– 1.4RθJA Junction-to-Ambient (PCB mount)* ––– 50 °C/WRθJA Junction-to-Ambient ––– 110

Thermal Resistance

* When mounted on 1" square PCB (FR-4 or G-10 Material) . For recommended footprint and soldering techniques refer to application note #AN-994

2 www.irf.com

IRFR/U2407

Parameter Min. Typ. Max. Units ConditionsV(BR)DSS Drain-to-Source Breakdown Voltage 75 ––– ––– V VGS = 0V, ID = 250µA∆V(BR)DSS/∆TJ Breakdown Voltage Temp. Coefficient ––– 0.078 ––– V/°C Reference to 25°C, ID = 1mARDS(on) Static Drain-to-Source On-Resistance ––– 0.0218 0.026 Ω VGS = 10V, ID = 25A VGS(th) Gate Threshold Voltage 2.0 ––– 4.0 V VDS = 10V, ID = 250µAgfs Forward Transconductance 27 ––– ––– S VDS = 25V, ID = 25A

––– ––– 20 µA VDS = 75V, VGS = 0V––– ––– 250 VDS = 60V, VGS = 0V, TJ = 150°C

Gate-to-Source Forward Leakage ––– ––– 200 VGS = 20VGate-to-Source Reverse Leakage ––– ––– -200

nAVGS = -20V

Qg Total Gate Charge ––– 74 110 ID = 25AQgs Gate-to-Source Charge ––– 13 19 nC VDS = 60VQgd Gate-to-Drain ("Miller") Charge ––– 22 34 VGS = 10Vtd(on) Turn-On Delay Time ––– 16 ––– VDD = 38Vtr Rise Time ––– 90 ––– ID = 25Atd(off) Turn-Off Delay Time ––– 65 ––– RG = 6.8Ω

tf Fall Time ––– 66 ––– VGS = 10V Between lead,

––– –––6mm (0.25in.)from packageand center of die contact

Ciss Input Capacitance ––– 2400 ––– VGS = 0VCoss Output Capacitance ––– 340 ––– pF VDS = 25VCrss Reverse Transfer Capacitance ––– 77 ––– ƒ = 1.0MHz, See Fig. 5Coss Output Capacitance ––– 15700 ––– VGS = 0V, VDS = 1.0V, ƒ = 1.0MHzCoss Output Capacitance ––– 220 ––– VGS = 0V, VDS = 60V, ƒ = 1.0MHzCoss eff. Effective Output Capacitance ––– 220 ––– VGS = 0V, VDS = 0V to 60V

nH

Electrical Characteristics @ TJ = 25°C (unless otherwise specified)

LD Internal Drain Inductance

LS Internal Source Inductance ––– –––S

D

G

IGSS

ns

4.5

7.5

IDSS Drain-to-Source Leakage Current

Repetitive rating; pulse width limited by max. junction temperature.

ISD ≤ 25A, di/dt ≤ 290A/µs, VDD ≤ V(BR)DSS, TJ ≤ 175°C

Notes:

Starting TJ = 25°C, L = 0.42mH RG = 25Ω, IAS = 25A.

Pulse width ≤ 300µs; duty cycle ≤ 2%.

S

D

G

Parameter Min. Typ. Max. Units ConditionsIS Continuous Source Current MOSFET symbol

(Body Diode) ––– ––– showing theISM Pulsed Source Current integral reverse

(Body Diode) ––– ––– p-n junction diode.VSD Diode Forward Voltage ––– ––– 1.3 V TJ = 25°C, IS = 25A, VGS = 0V trr Reverse Recovery Time ––– 100 150 ns TJ = 25°C, IF = 25AQrr Reverse RecoveryCharge ––– 400 600 nC di/dt = 100A/µs ton Forward Turn-On Time Intrinsic turn-on time is negligible (turn-on is dominated by LS+LD)

Source-Drain Ratings and Characteristics

42

170A

Coss eff. is a fixed capacitance that gives the same charging time as Coss while VDS is rising from 0 to 80% VDSS

Calculated continuous current based on maximum allowable junction temperature. Package limitation current is 30A

Date:

Customer Model Number

Serial #

L-L Resistance (Rtm) Ohms : Electrical Time Constant (te) mSec. :

L-L Inductance (Ltm) mH at 1Khz : Mechanical Time Constant (tm) mSec. :

Torque Constant (Kt) oz.in./Amp : Thermal Resistance (Rth) °C/watt

Voltage Constant (Ke) Vpeak/KRPM : Thermal Time Constant (t th ) min. :

Amb. Temp. ( ºC ) : Rotor Inertia (Jr) oz-in-s² :

Stack Length:

Notes:

Speed / Torque Test Data -Control Input set at 100% duty cycle.

Load

Volts

(DC)

Amps

(DC)

Watts

(DC)

Speed

(RPM)

Torque

(oz.in.)

Output

(watts)

Output

(HP)

Eff.

(%)

1 24.01 0.26 6.11 3125 0.54 1.25 0.002 20.4

2 24.01 0.60 13.76 2683 4.00 7.94 0.011 57.7

3 24.01 0.98 22.29 2248 8.02 13.34 0.018 59.9

4 24.01 1.16 26.46 2054 10.00 15.20 0.020 57.4 Max Continuous Rating

5 24.02 1.74 39.56 1534 15.96 18.12 0.024 45.8

6 24.02 2.15 49.16 1222 19.88 17.98 0.024 36.6

7 24.03 2.59 59.55 913 23.94 16.18 0.022 27.2

8 24.03 3.04 70.70 621 27.86 12.80 0.017 18.1

9 24.04 3.51 82.69 319 31.98 7.55 0.010 9.1

10 24.04 3.91 93.72 0 31.48 0.00 0.000 0.0

Special Load Points

1

2

This motor is intended for sampling and customer approval only. No application fitness approval is implied, as that can only be determined by the customer. These data represent performance of a single sample motor. These values are not to be construed as guaranteed values.

Microchip

1.14

3.74

2/14/02

DMB0224C10002

12482

0.75

4.03

4.60

9.79

7.24

22.7 0.000628

4.78

16

Sample Motor Data Sheet

Torque (oz.in.)

0 5 10 15 20 25 30 35 40

MotorSpeed(RPM)

0

500

1000

1500

2000

2500

3000

3500

SystemAmps(DC)

0

1

2

3

4

SystemEff.( % )

0

10

20

30

40

50

60

70

80

90

100

SystemWatts(InPut)

0

20

40

60

80

100

RPM vs Tq. Amps vs Tq. Eff. vs Tq. Watts vs Tq.

Sample Motor Test Data

Hurst Mfg.

Company Confidential Motor Test Data 2/18/03

bldc motor drive controller for electric vehicles · bldc motor drive controller for electric...

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