quantitative evaluation for reliability of hybrid electric vehicle powertrain

8/12/2019 Quantitative Evaluation for Reliability of Hybrid Electric Vehicle Powertrain

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Quantitative Evaluation for Reliability of Hybrid

Electric Vehicle Powertrain

Yantao Song and Bingsen Wang

Department of Electrical and Computer EngineeringMichigan State University

2120 Engineering Building

East Lansing, MI 48824, USA

[email protected]; [email protected]

Abstract—The reliability prediction of hybrid vehicles is of paramount importance for planning, design, control and opera-tion management of vehicles, since it can provide an objectivecriterion for comparative evaluation of various structures andtopologies and can be used as an effective tool to improvethe design and control of vehicles. This paper presents a load-dependent simulation model based on MATALAB for quanti-tatively assessing the reliability of hybrid electric vehicles. Themodel takes into consideration the variation of driving scenarios,dormant mode, electrical stresses, thermal stress and thermalcycling. Therefore the more reliable and accurate reliabilityprediction can be obtained. The model is demonstrated in detailand the result of reliability assessment based on a series hybridelectric vehicle is presented and analyzed.

I. INTRODUCTION

Hybrid electric vehicles (HEVs) with their superior fuel

economy have been considered as a pivotal technology to

mitigate concerns over the rapid rising of petroleum cost,

increasingly worsening air pollution and global warming asso-ciated with greenhouse gas emission [1]. However, inclusion of

a great number of power electronic devices into drive systems

of vehicles deteriorates reliability of the overall system [2].

The reduced reliability of HEVs not only discounts fuel-

saving premium, but also increases operation cost. Therefore

the reliability of HEVs powertrain has increasingly attracted

research attention from both the academia and the industry.

Research activities on the reliability of components, power

electronic converters and the whole drivetrain for HEVs from

the probabilistic and deterministic perspectives have been

reported in literature [3]. From system point of view, battery

is the most important and also the least reliable component

in HEVs, which has a crucial effect on the reliability and

cost of HEVs. The authors of [4] study the influence of the

operating temperature on the cycle life of lead-acid, lithium-

ion and NiMH batteries for HEVs based on simulation and

numeric analysis. The reliability of power electronic converters

in HEVs is also widely investigated. In [5] the reliability of a

bidirectional dc/dc converter for the energy storage system of

HEVs is assessed. In this paper the driving behaviors are taken

into account and the failure rate models of the components are

obtained by using Monte Carlo Simulation. But the reliability

models introduced by the authors do not include effects of

thermal cycling on component failures, which will lead to

the results that may substantially deviate from reality. A test

bench implemented with various driving cycles to verify the

reliability of new prototypes of inverters for electric motors

in hybrid vehicles is presented in [6]. Authors of [7] presents

a simulation concept that is used to assess the lifetime of the

inverter for HEVs in terms of the crack propagation speed of

bond and solder joint connections. Hirschmann, et al. present

a simulation model to predict the reliability of inverters for

HEVs [8]. This reliability model focuses on the effects of

temperature and thermal cycle on the failure rates of key

power components of inverters. A reliability model based on a

sequence tree is adopted to analyze various reliability indices

and related maintenance cost of the traction train within a

fuel cell car [9]. The authors of [10] evaluate and compare the

availability of pure electric vehicle, hybrid electric vehicle, and

conventional vehicles based on part-count reliability model.

This method does not consider the practical driving scenariosand operating conditions of vehicles. In order to overcome

the limitations of the existing methods, this paper presents

a reliability model for hybrid electric vehicles. The model

includes power electronic converters and energy storage unit in

SHEVs. The practical scenarios, thermal stress and electrical

stresses are considered in the model. The accurate reliability

analysis provides an important guideline for planning, design

and operation management of HEVs. The SHEV drive system

is reviewed in Section II. The reliability model is illustrated

in detail in Section III. In Section IV the results of reliabil-

ity assessment and a brief analysis are presented. Finally a

summary in Section V concludes the paper.

II . SERIES H YBRID E ELECTRIC V EHICLE P OWERTRAIN

As shown in Fig. 1, an SHEV power system consists of

three power electronic converters, a three-phase PWM rectifier,

a three-phase inverter and a bidirectional buck/boost dc/dc

converter, and energy storage unit that is composed of battery

cells connected in parallel and series manners. Since there

are two energy sources, the traction power will be divided

between the engine and the battery bank in accordance with the

specific energy management strategy, driving conditions, and

state of charge of the battery pack. Correspondingly there are



0 1 2 1 34 5 6 53 7 2 1 38 6 9 5 3 2 5 3 5 2 5 3 1 1 2 1 6 9 5 3 2 5 3 7

7 6 6 6

6

Fig. 1. Schematic of the SHEV powertrain.0 1 2 3 2 4 5 7 8 7 9 2 7 9 9 1 9 9 1 9 9 9 2 2 9 2 8 9

1 3 2 7 9 3 2 7 9 7 7 9 1 2 4 1 1 3 9 5 4 7 1 1 4 4 7 2 4 1 1 4 1 1 3 1 2 2 4Fig. 2. The diagram of the reliability simulation model.

five operating modes for SHEVs, and the operating conditions

of the power converters and the battery bank are different

from each mode to others. As a result, the electrical and

thermal stresses of components in SHEVs greatly vary during

a driving cycle, which will be considered in the reliabilityanalysis model presented in the paper.

III . RELIABILITY S IMULATION M ODEL OF SHEVS

The structure of the reliability simulation model is shown

in Fig. 2. In this model, the input data are the operating

conditions of vehicles. Herein, various standard driving cycles

are used to simulate the driving scenarios. The failure rates and

mean time to failure (MTTF), lifetime and other reliability

indices of the components, converter and the whole system

can be obtained from the model. Each functional block will

be introduced as follows.

A. Driving Cycle

The torque-speed characteristics of vehicles versus time

determine the operating conditions of the power electronic

converters in the drive system, which finally affect the elec-

trical and thermal stresses of the key power components.

However, the torque-speed profiles of vehicles depend on the

behaviors of drivers and the road conditions. Uncertainty of

driving patterns challenges the reliability prediction of HEVs.

Fortunately, various driving cycles that are temporal sequences

of speeds, such as NEDC, FTP-72, FTP-75, US06, and so

on, have been developed in different countries to provide

TABLE IPARAMETERS A SSUMED OF V EHICLE

Parameter Value

Vehicle weight 1243 kg

Front area 1.746 m2

Rolling resistance coefficient 0.01

Aerodynamic drag coefficient 0.26

Diameter of tire 0.62 m

Transmission efficiency 0.9

a test benchmark for evaluating efficiency and emission of

vehicles. Since these driving cycles have been accepted by the

industry and widely used to assess performance of vehicles,

herein they are employed to emulate the operating patterns of

HEVs. The driving cycle provides the instantaneous speed and

acceleration information to HEVs model, as shown in Fig. 2.

B. Vehicle Model

The driving cycle emulates vehicles’ instantaneous speed

and acceleration that cannot exclusively determine the instan-

taneous electrical stresses of vehicles’ powertrain. The specific

electrical stresses also depend on the parameters of vehicles

and the road conditions, such as wind speed, gradient and

roughness of the road surface. The parameters of HEV and

assumed road conditions not only determine the power ratings

of energy sources and power electronic converters, but also

determine their instantaneous powers [1]. The parameters of

the vehicle used in this paper are obtained from the commer-

cial vehicles and literature [11], and have been tabulated in

TABLE I. In HEV model, the vehicle speed and acceleration

are used as inputs to obtain the instantaneous torque of the

electric motor.

C. Motor Model

The design of the traction motor in SHEVs is based on

performance requirements of vehicle that mainly include max-

imum speed, acceleration and gradiability, vehicle’ parameters

and the road conditions. The specific design process and

methodology is detailed in [1]. The motor’s power rating is

illustrated in TABLE II. Herein the interior permanent magnet

motor (IPM) is utilized as the traction motor and its model

is developed to calculate the instantaneous stator current and

voltage by using known torque and speed. The simulation

model is based on the steady-state model of IPMs [12].

The IPMs operating modes, such as maximum torque per

ampere, fluxing weakening, are also considered in this model

to simulate practical operating conditions. The stator voltage

and current from the motor model directly determine the

operating conditions of power converters in HEVs driving

system.

D. Loss Model

The basic design rules of three power electronic converters

are explained as the following. The inverter is utilized to

control the traction motor, therefore its voltage and current



determined by

λIGBT = λdie + λ package + λoverstress

λdie = πs ∗ λ0 ∗y

i=1(πt)i∗τ iτ on+τ off

λ package = 2.75 ∗ 10−3 ∗ λb ∗zi=1 (πn)i ∗ (T )

0.68

λoverstress = πI ∗ λEOS (1)

where, the first term λdie, which is mainly determined bythe junction temperature in the mission profile, represents

the failure-rate component related to the die of IGBTs; and

the second term λ package denotes IGBT package failures that

are caused by the number and magnitudes of thermal cycles

that devices undergo. The last term λoverstress , which reflects

contribution of the over-current and over-voltage stresses to the

total component failure rate, can be neglected since in practical

applications the over-stress operating conditions should not

occur in normal operating conditions. The unit of the failure

rate in the above equation is the number of failures per 109

hours.

The parameters in (1) will be further explained as the

following. The parameter πs represents the influence of thevoltage stresses on the failure of the IGBT’s die, and is

determined by the ratios of the applied collector-to-emitter

and gate-to-emitter voltages to the corresponding ratings. λ0and λb are base failure rates of the die and the package,

respectively. (πt)i represents the effect of the real junction

temperature on the failure of the die in the ith phase of

the IGBT’s mission profile and is determined by the junction

temperature. The parameters τ i is the working time ratio of

the IGBT in the ith phase of the mission profile. τ on and τ off respectively correspond to the total working time ratio and

the total dormant time ratio. These three parameters account

for the effect of the dormant mode on the failure of IGBTs.(T )i represents the amplitude of the thermal variation that

the device undergoes in the ith phase of its mission profile.

(πn)i is the influence factor that is related to the annual

number of the thermal cycle experienced by the package

with the amplitude of (T )i. The failure rate model further

demonstrates that the junction temperature and the temperature

cycle have a significant influence on failure of IGBTs.

Failure rate models of diodes and capacitors have the same

form as that of IGBTs. However, the reliability handbooks

RDF2000 and MIL-217F do not contain the failure rate model

of battery. The reliability prediction procedure Bellcore TR-

332 [16] published by Bell Communication Research, Inc

provides a simple failure model of battery cell, which is

λbattery = λ0 ∗ 10−9/hour (2)

From this model the failure of the battery is independent of

all stresses, but only depends on the base failure rate λ0 that

is related with the type of the battery cell. Therefore it is a

very rough model.

IV. RELIABILITY A SSESSMENT A ND A NALYSIS

The reliability of SHEVs powertrain is evaluated based on

the presented simulation model. The driving cycles FTP-75

and US06 are utilized, which represent driving conditions on

the urban route and on the high way, respectively. The ambient

temperature is set to be 45 °C. The average total running time

of a vehicle is about 500 hours per year [8]. The thermal cycles

of the magnitude of lower than 3 °C have little influence on

the failure of components and therefore can be neglected.

The reliability of the powertrain depends on the type of driv-

ing cycles, the energy management strategy and initial condi-tions of the battery pack. In order to evaluate the effects of the

various driving cycles on the reliability of SHEVs’ powertrain,

the simulations based on FTP-75 and US06 are implemented

and analyzed. The energy management strategy determines the

power distributions between two energy sources, the battery

pack and the engine, and therefore determines electrical and

further thermal stresses of the rectifier and the dc/dc converter.

Herein, the engine on/off control is utilized. In this strategy, the

battery pack is used as the main energy source and it provides

total drive power to the inverter/motor while the engine is

turned off if the state of charge (SOC) of the battery pack is

within the set range. Once the SOC of the battery pack drops

to its lower threshold, the engine is turned on and charges the

battery pack with the full power. The benefit of the engine

on/off control lies in the fact that the engine always works

in the high-efficiency range. However, the battery pack has

to undergo deep charge/discharge cycles and the buck/boost

converter consequently has to experience higher electrical and

thermal stresses. In order to avoid unfairness of the simulation

results caused by the initial condition of the battery pack,

simulations respectively based on five consecutive US06 cycles

and two consecutive FTP-75 cycles are implemented.

Fig. 5 and Fig. 6 illustrate the junction temperatures of the

inverter IGBT and diode in the last cycle of five consecutive

US06 driving cycles. Compared with Fig. 3, it can be observedthat the junction temperatures of devices follow the profiles of

their power losses and that the junction temperatures fluctuate

dramatically in one driving cycle although their absolute

values are not much high. Fig. 7, 8, 9 and 10 demonstrate

the numbers and corresponding amplitudes of thermal cycles

that the semiconductors in the inverter and DC/DC converter

undergo in five consecutive US06 cycles. It is shown that the

amplitudes of the thermal cycles are mainly under 35 °C, and

that the IGBTs of the inverter and DC/DC converter experience

more thermal cycles of higher amplitudes since the IGBTs

have higher losses and correspondingly have higher junction

temperatures. The failure rates of components are related to

the temperature cycles.

The failure rates and MTTFs of the components are demon-

strated in TABLE III. It should be noted that failure rates

of components in TABLE III is that of a single device and

that the failure rate of the whole system does not include the

battery pack and the rectifier. Since the battery pack consists

of hundreds of cells connected in parallel and series manners,

the failure rate of the whole battery pack is relatively high

and dominant in the overall system. And the failure rate of

the battery back is independent of its operating conditions but

only depends on the number of the battery cells. As a result,



TABLE IIIFAILURE R ATES AND MTTFS OF SHEVS

Driving cycle Reliability index Inverter IGBT Inverter diode Boost IGBT Boost diode Dc-link capacitor System

FTP-75 Failure rate (/ 10

6 hours) 1.8974 1.173 2.2824 0.74447 6.5499 ∗ 10−4

21.463

MTTF (105 hours) 5.2705 8.5251 4.3813 13.432 1.5267 ∗ 10

40.46592

US06 Failure rate (/ 10

6 hours) 4.0965 2.4326 9.6035 5.7916 6.5557 ∗ 10−4

54.583

MTTF (105 hours) 2.4411 4.1108 1.0413 1.7266 1.5254 ∗ 10

40.18321

0 1 2 2 0 3 2 2 0 4 2 2 0 5 2 2 0 6 2 2 7 2 2 28 11 21 13 23 14 2

9 9

9

Fig. 5. The junction temperature of inverter IGBT in a US06 driving cycle.

0 1 2 2 0 3 2 2 0 4 2 2 0 5 2 2 0 6 2 2 7 2 2 28 11 21 13 2

9

Fig. 6. The junction temperature of inverter diode in a US06 driving cycle.

any improvement of design and control strategies has a little

effect on the failure rate and MTTF of the SHEV powertrain,

which does not match practical observations. Therefore the

failure rates and MTTF of the system in TABLE III exclude

the contribution from the battery pack. Our ongoing effort is

devoted to accurate lifetime model of battery cells. TABLE III

also shows that the IGBT of the boost converter features

the highest failure rate and therefore is the least reliable.

It is because the power loss dissipated in the switch of

boost converter is much higher than the losses of the other

components and correspondingly its thermal stress is the worst,

as shown in Fig. 7-10. Since US06 emulates the highway

driving behaviors and have higher speeds and accelerations

than the driving cycle FTP-75, in the US06 driving cycles,

0 1 2 3 4 5 6 7 8 9 8 8 8 0 8 1 8 2 8 3 8 4 8 5 8 6 8 7 0 998 90 91 92 93 94 9

Fig. 7. The numbers and amplitudes of inverter IGBT thermal cycles in fiveconsecutive US06 driving cycles.

0 1 2 3 4 5 6 7 6 6 6 8716 76 18 78 19 79 1

Fig. 8. The numbers and amplitudes of inverter diode thermal cycles in fiveconsecutive US06 driving cycles.

the electrical and thermal stresses of the power converters are

much worse and correspondingly much higher failure rates andlower reliability can be predicted, as shown in TABLE III.

V. CONCLUSION

A reliability prediction model for electric vehicles has

been presented. Compared with the part-count method that

determines the reliability of a system only based on the types

and numbers of components used in the system, the model

presented in this paper not only considers the thermal and

electrical stresses, but also includes the effects of load varia-

tions related to driving behaviors and road conditions on the

reliability of components since the model is based on the stan-



0 1 2 1 0 3 2 3 0 4 2 4 021 23 24 25 20 26 27 28 2

9

Fig. 9. The numbers and amplitudes of boost IGBT thermal cycles in fiveconsecutive US06 driving cycles.

0 1 2 0 2 1 3 0 3 102 03 04 05 01 06 0

7 8 9 8 9 889

Fig. 10. The numbers and amplitudes of boost diode thermal cycles in fiveconsecutive US06 driving cycles.

dard driving cycles. Although this model is developed based

on the series hybrid electric vehicles, it can be equally suited

for other types of EVs with minimal modification/extension.

On the basis of the accurate reliability analysis, improvement

in design of the powertrain, in control methods and in energy

management strategies can be realized to further enhance the

performances of vehicles and to reduce the operation and

maintenance cost.

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quantitative evaluation for reliability of hybrid electric vehicle powertrain

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