Modeling, dynamics, and control of electrified vehicles

MODELING,

DYNAMICS, AND

CONTROL OF

ELECTRIFIED

VEHICLES

Related titles

Electric and Hybrid Vehicles: Power Sources, Models, Sustainability, Infrastructure

and the Market

(ISBN 978-0-444-53565-8)

Scrosati, Garche and Tillmetz, Advances in Battery Technologies for Electric

Vehicles

(ISBN 978-1-78242-377-5)

MODELING,

DYNAMICS, AND

CONTROL OF

ELECTRIFIED

VEHICLES

Edited by

HUI ZHANG

Beihang University, Beijing, China

DONGPU CAO

Cranfield University, Bedford, United Kingdom

HAIPING DU

University of Wollongong, Wollongong, NSW, Australia

Woodhead Publishing is an imprint of Elsevier

The Officers’ Mess Business Centre, Royston Road, Duxford, CB22 4QH, United Kingdom

50 Hampshire Street, 5th Floor, Cambridge, MA 02139, United States

The Boulevard, Langford Lane, Kidlington, OX5 1GB, United Kingdom

Copyright r 2018 Elsevier Inc. All rights reserved.

No part of this publication may be reproduced or transmitted in any form or by any means, electronic or

mechanical, including photocopying, recording, or any information storage and retrieval system, without

permission in writing from the publisher. Details on how to seek permission, further information about the

Publisher’s permissions policies and our arrangements with organizations such as the Copyright Clearance

Center and the Copyright Licensing Agency, can be found at our website: www.elsevier.com/permissions.

This book and the individual contributions contained in it are protected under copyright by the Publisher

(other than as may be noted herein).

Notices

Knowledge and best practice in this field are constantly changing. As new research and experience broaden

our understanding, changes in research methods, professional practices, or medical treatment may become

necessary.

Practitioners and researchers must always rely on their own experience and knowledge in evaluating and

using any information, methods, compounds, or experiments described herein. In using such information or

methods they should be mindful of their own safety and the safety of others, including parties for whom they

have a professional responsibility.

To the fullest extent of the law, neither the Publisher nor the authors, contributors, or editors, assume any

liability for any injury and/or damage to persons or property as a matter of products liability, negligence or

otherwise, or from any use or operation of any methods, products, instructions, or ideas contained in the

material herein.

British Library Cataloguing-in-Publication Data

A catalogue record for this book is available from the British Library

Library of Congress Cataloging-in-Publication Data

A catalog record for this book is available from the Library of Congress

ISBN: 978-0-12-812786-5 (print)

ISBN: 978-0-12-813109-1 (online)

For information on all Woodhead Publishing publications

visit our website at https://www.elsevier.com/books-and-journals

Publisher: Joe Hayton

Acquisition Editor: Sonnini R. Yura

Editorial Project Manager: Ana Claudia Garcia

Production Project Manager: Omer Mukthar

Cover Designer: Victoria Pearson

Typeset by MPS Limited, Chennai, India

CONTENTS

List of Contributors ix

1. Modeling, Evaluation, and State Estimation for Batteries 1

Hao Mu and Rui Xiong

1.1 Introduction 1

1.2 Battery Modeling 2

1.3 Evaluation of Model Accuracy 8

1.4 State Estimation 25

1.5 Conclusions 34

References 35

2. High-Power Energy Storage: Ultracapacitors 39

Lei Zhang

2.1 Introduction 39

2.2 Modeling 45

2.3 UC State Estimation 66

2.4 Conclusions 69

Further Reading 70

3. HESS and Its Application in Series Hybrid Electric Vehicles 77

Shuo Zhang and Rui Xiong

3.1 Introduction 77

3.2 Modeling and Application of HESS 80

3.3 Conclusion 115

References 117

4. Transmission Architecture and Topology Design of EVs and HEVs 121

Jibin Hu, Jun Ni and Zengxiong Peng

4.1 Introduction 121

4.2 EV and HEV Architecture Representation 125

4.3 Topology Design of Power-Split HEV 129

4.4 Topology Design of Transmission for Parallel Hybrid EVs 143

4.5 Conclusion 157

Reference 157

v

5. Energy Management of Hybrid Electric Vehicles 159

Hong Wang, Yanjun Huang, Hongwen He, Chen Lv, Wei Liu

and Amir Khajepour

5.1 Introduction 159

5.2 Energy Management of HEVs 161

5.3 Case Study 182

5.4 Model Predictive Control Strategy 192

5.5 Results 195

5.6 Conclusions 198

References 198

6. Structure Optimization and Generalized Dynamics Control

of Hybrid Electric Vehicles 207

Liang Li, Sixiong You, Xiangyu Wang and Chao Yang

6.1 Introduction 207

6.2 Generalized Dynamics Models 208

6.3 Extended High-Efficiency Area Model 212

6.4 Typicals Applications 215

6.5 Conclusions 241

References 243

7. Transmission Design and Control of EVs 245

Xiaoyuan Zhu and Fei Meng

7.1 Introduction 245

7.2 EVs Equipped with IMT Powertrain System 248

7.3 Problem Formulation 253

7.4 Oscillation Damping Controller Design 259

7.5 Simulation Results 265

7.6 Conclusion 271

Funding 272

References 272

Further Reading 274

8. Brake-Blending Control of EVs 275

Chen Lv, Hong Wang and Dongpu Cao

8.1 Introduction 275

8.2 Brake-Blending System Modeling 278

8.3 Regenerative Braking Energy-Management Strategy 283

8.4 Dynamic Brake-Blending Control Algorithm 292

8.5 Conclusion 306

vi Contents

References 306

Further Reading 308

9. Dynamics Control for EVs 309

Yafei Wang and Hiroshi Fujimoto

9.1 Introduction 309

9.2 Modeling and Control of EVs 315

9.3 Sensing and Estimation 321

9.4 Active Safety Control 326

9.5 Riding and Energy Efficiency Control 332

9.6 Conclusions 336

References 336

10. Robust Gain-Scheduling Control of Vehicle Lateral Dynamics

Through AFS/DYC 339

Hui Zhang and Junmin Wang

10.1 Introduction 339

10.2 Development of Uncertain Vehicle Dynamics Model 342

10.3 Main Results 355

10.4 Simulation Results 359

10.5 Conclusions 364

Acknowledgments 365

References 365

11. State and Parameter Estimation of EVs 369

Brett McAulay, Boyuan Li, Philip Commins and Haiping Du

11.1 Introduction 369

11.2 Velocity Estimation (Longitudinal, and Total, Preferred Method and

Alternatives) 372

11.3 Slip-Angle Estimation 374

11.4 Tire-Force and Tire
Road Friction Coefficient Estimation 381

11.5 Vehicle Mass- and Road Slope-Estimation Method 395

11.6 Conclusions 405

References 406

Further Reading 407

12. Modeling and Fault-Tolerant-Control of Four-Wheel-Independent￾Drive EVs 409

Rongrong Wang and Junmin Wang

12.1 Introduction 409

Contents vii

12.2 System Modeling and Problem Formulation 411

12.3 Fault-Tolerant Tracking Controller Design 418

12.4 Simulation Investigations 437

12.5 Conclusions 448

References 448

13. Integrated System Design and Energy Management of Plug-In

Hybrid Electric Vehicles 451

Xiaosong Hu

13.1 Introduction 451

13.2 Powertrain Modeling 453

13.3 Heuristic Scenarios 455

13.4 Emission Mitigation via Renewable Energy Integration 463

13.5 Optimal Scenario With Integrated System Design and Energy

Management 465

13.6 Battery-Health Implication 468

13.7 Conclusions 471

References 473

Appendix 474

14. Integration of EVs With a Smart Grid 475

Xiaosong Hu

14.1 Introduction 475

14.2 Powertrain Modeling 477

14.3 Formulation of Cost-Optimal Control Problem 483

14.4 Results and Discussion 485

14.5 Conclusions 494

References 495

Index 497

viii Contents

LIST OF CONTRIBUTORS

Dongpu Cao

Cranfield University, Bedford, United Kingdom

Philip Commins

University of Wollongong, Wollongong, NSW, Australia

Haiping Du

University of Wollongong, Wollongong, NSW, Australia

Hiroshi Fujimoto

The University of Tokyo, Tokyo, Japan

Hongwen He

Beijing Institute of Technology, Beijing, China

Jibin Hu

Beijing Institute of Technology, Beijing, China

Xiaosong Hu

Chongqing University, Chongqing, China

Yanjun Huang

University of Waterloo, ON, Canada

Amir Khajepour

University of Waterloo, ON, Canada

Boyuan Li

University of Wollongong, Wollongong, NSW, Australia

Liang Li

Tsinghua University, Beijing, China

Wei Liu

Beijing Institute of Technology, Beijing, China

Chen Lv

Cranfield University, Cranfield, United Kingdom; Cranfield University, Bedford,

United Kingdom

Brett McAulay

University of Wollongong, Wollongong, NSW, Australia

Fei Meng

Shanghai Maritime University, Shanghai, China

Hao Mu

Beijing Institute of Technology, Beijing, China

Jun Ni

Beijing Institute of Technology, Beijing, China

ix

Zengxiong Peng

Beijing Institute of Technology, Beijing, China

Hong Wang

University of Waterloo, ON, Canada

Junmin Wang

The Ohio State University, Columbus, OH, United States

Rongrong Wang

Shanghai Jiao Tong University, Minhang, China

Xiangyu Wang

Tsinghua University, Beijing, China

Yafei Wang

Shanghai Jiao Tong University, Shanghai, China

Rui Xiong

Beijing Institute of Technology, Beijing, China

Chao Yang

Tsinghua University, Beijing, China

Sixiong You

Tsinghua University, Beijing, China

Hui Zhang

Beihang University, Beijing, China

Lei Zhang

Beijing Institute of Technology, Beijing, China

Shuo Zhang

Beijing Institute of Technology, Beijing, China

Xiaoyuan Zhu

Shanghai Maritime University, Shanghai, China

x List of Contributors

CHAPTER 1

Modeling, Evaluation, and State

Estimation for Batteries

Hao Mu and Rui Xiong

Beijing Institute of Technology, Beijing, China

1.1 INTRODUCTION

Currently, hybrid electric vehicles (HEVs) and electric vehicles (EVs)

promise a future of green travel in which fuel-consuming engines are

replaced with electric motors, thus reducing our dependence on fossil

energy and ultimately producing less harmful emissions. Such vehicles can

be plugged in at home overnight or at the office or in a parking space

during the day, using electricity that is generated at a centralized power

station or even by renewable sources. The key component to the achieve￾ment of these electrical systems is the energy storage system, namely, the

battery technology.

The lithium-ion (Li-ion) battery, as depicted in Fig. 1.1, is the most

common choice for phone communication and portable appliances

because of its many advantages, such as high energy-to-weight and

power-to-weight ratios (180 Wh/kg and 1500 W/kg, respectively) and

low self-discharge rate (Linden and Reddy, 2002; Capasso and Veneri,

2014). In addition, among all rechargeable electrochemical systems,

Li-ion technology is the first-choice candidate as a power source for

HEVs/EVs. However, this technology is still delicate and affected by

numerous limitations, such as issues of safety (Doughty and Roth, 2012),

cost (Lajunen and Suomela, 2012), recycling (Gaines, 2011), and charging

infrastructure (Veneri et al., 2012).

To ensure the power battery works safely and reliably, which is a func￾tion of the battery management system (BMS), the temperature, voltage,

and current of the batteries should be monitored and the states of the bat￾teries should be estimated precisely in real time (Junping et al., 2009; He

et al., 2010; Camus et al., 2011). However, it is hard to measure the states

of batteries, like the state of charge (SoC), state of health (SoH), and state

of function (SoF) directly due to the complicated electrochemical process

1

Modeling, Dynamics, and Control of Electrified Vehicles

DOI: http://dx.doi.org/10.1016/B978-0-12-812786-5.00001-X

Copyright © 2018 Elsevier Inc.

All rights reserved.

and various factors in practical applications. Thus estimation methods

based on battery models are developed broadly.

The remainder of this chapter is organized as follows: Section 1.2 intro￾duces several kinds of modeling approaches for Li-ion batteries, such as

physical-based models, equivalent circuit models (ECMs), etc. In

Section 1.3, some regular battery tests are presented, which are indispens￾able for battery research. Then, considering the popularity of different

models, the ECMs are selected to illustrate parameter identification meth￾ods, which can be divided into offline and online ones according to real￾time capability. Due to the balance problem between model accuracy and

the computation burden of the BMS, an evaluation criterion is introduced

to determine the optimal number of RC networks in the models.

Section 1.4 is the core part of this chapter and covers state estimation of

batteries, in particular about SoC estimation. Many SoC estimation meth￾ods will be classified systematically and the multiscale adaptive extended

Kalman filter (MAEKF) algorithm for state and parameter collaborative

estimation will be elaborated on since it is not only provides satisfactory

estimation accuracy, but also low computation burden. Some conclusions

are drawn in Section 1.5 and references are listed in references section.

1.2 BATTERY MODELING

Many battery models, which are lumped models with relatively few para￾meters, have been put forward especially for the purpose of vehicle power

management control and BMS development. The most commonly used

models can be categorized as electrochemical models and ECMs (Plett,

2004a; He et al., 2011a, 2011b; Vasebi et al., 2007; Zhu et al., 2011;

Hussein and Batarseh, 2011; Hu et al., 2012). Electrochemical models

utilize a set of coupled nonlinear differential equations to describe the

Figure 1.1 Different types of Li-ion batteries.

2 Modeling, Dynamics, and Control of Electrified Vehicles

pertinent transport, thermodynamic, and kinetic phenomena occurring in

the cell. They can translate the distributions into easily measurable quanti￾ties such as cell current and voltage and build a relationship between the

microscopic quantities, such as electrode and interfacial microstructure

and the fundamental electrochemical studies and cell performance.

However, they typically deploy partial differential equations (PDEs) with

a large number of unknown parameters, which often leads to large mem￾ory requirements and heavy computation burdens, so the electrochemical

battery models are not desirable for BMSs (Smith et al., 2010). The sim￾plified electrochemical models, which ignore the thermodynamic and

quantum effects, are proposed to simulate the electrochemical and voltage

performance. The Shepherd model, the Unnewehr universal model, the

Nernst model, and the combined model are the typical choices. The

equivalent circuit battery models are developed by using resistors, capaci￾tors, and voltage sources to form a circuit network. Typically, a big capac￾itor or an ideal voltage source is selected to describe the open-circuit

voltage (OCV); the remainder of the circuit simulates the battery’s inter￾nal resistance and relaxation effects such as dynamic terminal voltage. The

Rint model, the Thevenin model, the DP model, and their revisions are

widely used.

1.2.1 Physical-Based Models

Electrochemical models usually use coupled nonlinear PDEs to describe

ion transport phenomena and electrochemical reactions to achieve high

accuracy, but incur heavy computation load. For instance, a pseudo two￾dimensional (P2D) model, developed by Doyle et al. (1993), is one of the

most popular variants and can take seconds to minutes to simulate

(Ramadesigan et al., 2012). For simplicity, Atlung et al. (1979) developed

a single particle model (SPM) that assumes electrodes are represented by

two single spherical particles. To improve the accuracy of the SPM under

high C-rate, several extended single particle models (E-SPMs) have been

proposed (Luo et al., 2013; Schmidt et al., 2010; Khaleghi Rahimian

et al., 2013), where Li-ion concentration and potential distribution in

electrolyte are taken into account. In general, electrochemical models

such as P2Ds, SPMs, and E-SPMs are more accurate than ECMs, but

require a large number of immeasurable parameters, leading to overfitting

in parametric identification. Therefore the pursuit for battery models

with high accuracy and computational efficiency still remains a challenge.

Modeling, Evaluation, and State Estimation for Batteries 3

Although electrochemical battery models are suitable for understand￾ing the electrochemical reactions inside the battery, their complexity often

leads to the need for more memory and computational effort. Thus they

may not be practical in the fast computation and real-time implementa￾tions needed for EV BMS. This problem has been addressed by many

researchers by investigating reduced-order models (ROMs) that predict

the battery behavior with varying degrees of fidelity (Smith et al., 2008,

2010). To reduce the order of an electrochemical battery model, discreti￾zation techniques can be applied to retain only the most significant

dynamics of the full-order model (Tanim et al., 2015). Various discretiza￾tion techniques are utilized to simplify the full model’s PDEs into a set of

ODEs of the ROM while keeping the fundamental governing electro￾chemical equations. In Shi et al., 2011, six different discretization meth￾ods (listed in Table 3) are addressed and compared for battery system

modeling.

1.2.1.1 Single Particle Model

The SPM assumes a single electrode particle in each electrode and negli￾gible electrolyte diffusion. Conservation of Li1 species in a single spheri￾cal active material particle is described by Fick’s law of diffusion:

@cs

@t 5 Ds

r2

@

@r

r

2 @cs

@r

for rAð0;RsÞ (1.1)

where rA(0,Rs) is the radial coordinate, Rs is the particle radius, cs(r,t) is

the concentration of Li1 ions in the particle as a function of radial posi￾tion r and time t, and Ds is the solid-phase diffusion coefficient. We use

the subscripts s and s, e to indicate the solid-phase and solid/electrolyte,

interface, respectively. The boundary conditions are

@cs

@r

jr50 5 0 (1.2)

Ds

@cs

@r r5Rs 5 2 j

asF









(1.3)

where j(x, t) is the rate of electrochemical reaction at the particle surface

(with j . 0 indicating ion discharge), F is Faraday’s constant (96,487 C/mol),

and as is the specific interfacial surface area. For the spherical active material

4 Modeling, Dynamics, and Control of Electrified Vehicles