MODELLING AND CONTROL OF AN

AFTERMARKET PARALLEL HYBRID

ELECTRIC VEHICLE

AUTHOR: WISDOM ENANG

RESEARCH

BACKGROUND

INFORMATION

DEFINITION OF HYBRID ELECTRIC VEHICLE

Hybrid electric vehicles (HEV) combine the internal combustion engine of

normal vehicles with battery and electric motor.

HEV ADVANTAGES

Greater operating efficiency because HEVs use regenerative braking, which

helps to minimize energy loss and recover the energy used to slow down or

stop a vehicle.

Greater fuel efficiency because hybrids consume significantly less fuel than

vehicles powered by ICE alone

Cleaner operation because HEVs can run on alternative fuels: electricity

(which have lower emissions), thereby decreasing the dependency on

fossil fuels.

HEV CONFIGURATION USED IN THIS RESEARCH

UNIQUENESS OF THIS MODEL

AFTERMARKET

PARALLEL

HYRBRID

ELECTRIC VEHICLE

Hybrid electric system added to conventional diesel engine vehicle.

Hybrid system only controls electric motor thus preserving the original

vehicle warranty.

Hybridization kits (electric motor and electric battery) are small in size and

affordable.

POWER FLOW POSSIBLE IN THE AFTERMARKET PARALLEL HEV

Motor only mode Power assist mode

Engine only mode

Recharge control mode Regenerative braking mode

RESEARCH

QUESTIONS

RESEARCH

QUESTIONS

RESEARCH AIM

To produce a robust real time controller for a parallel aftermarket HEV.

RESEARCH OBJECTIVES

To produce a parallel HEV model capable of accurately predicting fuel

consumption in real world driving scenarios.

To identify the interactions between human driver behaviour and fuel

consumption using the validated HEV model

Computation of a rule based control for the HEV

Optimal control of the HEV using Dynamic programming

Intelligent control of the HEV using GPS information

Intelligent control of the HEV using information from on-board driving

pattern learning algorithm taking in to consideration driver behaviour.

WHAT DATA IS NEEDED AND HOW IT CAN BE COLLECTED?

Chassis Dynamometer

Electric motor test rig

Engine fuel consumption map at each torque and speed operating point.

Engine transient testing of real world drive cycle for model validation.

Motor efficiency map at each torque and speed operating point.

WHAT HEV CONTROL OPTIONS ARE THERE?

WHAT HEV MODELLING OPTIONS ARE THERE?

This approach makes the assumption that the vehicle

meets the target performance, so that the vehicle speed is

supposed known a priori; thus enjoying the advantage

simplicity and low computational cost. Backward or Kinematic Approach

This approach makes use of a driver model typically a PID which compares that target vehicle speed (drive cycle

speed) with the actual speed profile, and then generates a power demand profile which is needed to follow the target

vehicle speed profile by solving the differential motion

equation of the vehicle. Quasi Static Approach

RESEARCH

PROGRESS

OVERALL RESEARCH PROGRESS

Experimental testing of Engine for fuel consumption map

and model validation

Electric motor test for motor efficiency

map

Testing

HEV modelling

Modelling

HEV model validation

Rules based HEV control

Control

Optimal HEV control using dynamic control

Intelligent HEV control using GPS

Intelligent HEV control using driver style

learning algorithm

Real time implementation of

HEV controllers

RESEARCH

RESULTS

RESULTS FROM EXPERIMENTAL TESTING

Motor testing results Engine testing results

HEV MODELLING STRUCTURE – QUASI STATIC APPROACH

Use orange switch inside to include or exclude

Engine idling when cycle speed demand is 0

Note: Time delay factor added

to the Hybrid

Controller to make the system results

more useful in real l ife

wheel torque (Nm)

shif t f lag (-)

speed_signal

Motor Torque (Nm)

Current_mode

v ehicle v elocity (m/s)

Wheel Tractiv e Force - Engine (N)

Wheel Braking f orce (N)

Vehicle Dynamics

Terminator

Scope3

Scope2

Scope1

Scope

Motor_power

Power_demand

Engine_power

Plots

Engine Power

Plot Analysis

Manual Switch

Initialize Model Parameters

SOC

Hy brid Switch

Pdemand

motor_speed (RPM)

Engine_speed (RPM)

Current Mode

Motor Power

Hybrid Control System

[shift_flag]

Goto9

[Engine_torque]

Goto8

[engine_speed]

Goto7

[current_mode]

Goto6

[Engine_Power]

Goto5

[Motor_Torque]

Goto4

-T-

Goto3

[Motor_Power]

Goto20

[wheel_torque]

Goto2

[SOC]

Goto18

[Power_demand]

Goto15

[motor_speed]

Goto14

[speed_signal]

Goto13

[idle_flag]

Goto10

[gear_demand]

Goto1[vehicle_velocity]

Goto

Fuel Savings %

Fuel Consumption g

[Engine_Power]

From9

[Power_demand]

From8

[Motor_Torque]

From7

[Motor_Power]

From6

-T-

From5

[wheel_torque]

From4

[gear_demand]

From3

[current_mode]

From22

[Motor_Power]

From20

[vehicle_velocity]

From2

[engine_speed]

From19

[Engine_torque]

From18

[motor_speed]

From17

[Power_demand]

From16

[idle_flag]

[SOC]

From14

[speed_signal]

From13

[shift_flag]

From12

[shift_flag]

From11

[engine_speed]

From10

[vehicle_velocity]

From1

chassis_dyno_speed_dmd

cycle_gear_demand

cycle_speed_demand

[vehicle_velocity]

From

idle f lag (-)

Engine_torque (Nm)

Enginespeed (rpm)

Fuel Consumption (g)

Fuel Sav ings (%)

Engine

cy cle_gear_demand

cy cle_speed_demand (km/h)

v ehicle_v elocity (m/s)

gear_demand

wheel torque (Nm)

Shif t_f lag [-]

Speed Signal

Power demand (w)

Driver Subsystem

gear_demand

v ehicle v elocity (m/s)

Wheel tractiv e f orce - Engine (N)

Shif t f lag [-]

Engine Torque (Nm)

engine speed (RPM)

idle f lag [-]

Engine Power (KW)

Drive Train

0

Constant2

1

Constant

v ehicle v elocity (m/s)

Motor Power demand (W)

Motor Torque (Nm)

SOC (%)

Motor Speed (RPM)

Battery and Electric Motor Subsystem

Hy brid_Switch

This approach makes use of a driver model typically a PID which compares that target vehicle speed (drive cycle speed) with the actual speed profile, and then generates a power demand profile which is needed to follow the target vehicle speed profile by solving the differential motion equation of the vehicle.

HEV MODEL VALIDATION

Model validation carried out over the NEDC (New European Drive Cycle)

NEDC testing results proves it to be highly repeatable and hence why it has been chosen for the model validation

Level of accuracy achieved: 99% model accuracy

HIGHLIGHTS FROM MODEL VALIDATION

RULE BASED CONTROL STRUCTURE

Overview of the control structure Traction mode control structure

Braking mode control structure

RULE BASED CONTROL RESULTS

Drive cycle speed time profile Power split profile Instantaneous Fuel consumption

profile comparison

Engine operating point Battery state of Charge profile Cumulative fuel consumption

profile comparison

State of charge boundaries: Highest allowable (80%) and lowest allowable (20%)

Fuel savings achieved over the NEDC 12.58%

Lowest state of charge encountered 27%

Future Work

Optimal HEV control using

dynamic control

Intelligent HEV control using

GPS

Intelligent HEV control using driver

style learning algorithm

Real time implementation

of HEV controllers

PhD RESEARCH PROJECT GANTT CHART

QUESTIONS

PLEASE?

THANK YOU FOR

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