Advanced Battery Management Technologies for Electric Vehicles E-Book

Rui Xiong

• Herausgeber: John Wiley & Sons
• Kategorie: Wissenschaft und neue Technologien
• Serie: Automotive Series
• Sprache: Englisch

Seitenzahl: 416

Veröffentlichungsjahr: 2018

Beschreibung

A comprehensive examination of advanced battery management technologies and practices in modern electric vehicles Policies surrounding energy sustainability and environmental impact have become of increasing interest to governments, industries, and the general public worldwide. Policies embracing strategies that reduce fossil fuel dependency and greenhouse gas emissions have driven the widespread adoption of electric vehicles (EVs), including hybrid electric vehicles (HEVs), pure electric vehicles (PEVs) and plug-in electric vehicles (PHEVs). Battery management systems (BMSs) are crucial components of such vehicles, protecting a battery system from operating outside its Safe Operating Area (SOA), monitoring its working conditions, calculating and reporting its states, and charging and balancing the battery system. Advanced Battery Management Technologies for Electric Vehicles is a compilation of contemporary model-based state estimation methods and battery charging and balancing techniques, providing readers with practical knowledge of both fundamental concepts and practical applications. This timely and highly-relevant text covers essential areas such as battery modeling and battery state of charge, energy, health and power estimation methods. Clear and accurate background information, relevant case studies, chapter summaries, and reference citations help readers to fully comprehend each topic in a practical context. * Offers up-to-date coverage of modern battery management technology and practice * Provides case studies of real-world engineering applications * Guides readers from electric vehicle fundamentals to advanced battery management topics * Includes chapter introductions and summaries, case studies, and color charts, graphs, and illustrations Suitable for advanced undergraduate and graduate coursework, Advanced Battery Management Technologies for Electric Vehicles is equally valuable as a reference for professional researchers and engineers.

Leseprobe

Table of Contents

Cover

Biographies

Foreword by Professor Sun

Foreword by Professor Ouyang

Series Preface

Preface

Acknowledgments

1 Introduction

1.1 Background

1.2 Electric Vehicle Fundamentals

1.3 Requirements for Battery Systems in Electric Vehicles

1.4 Battery Systems

1.5 Key Battery Management Technologies

1.6 Battery Management Systems

1.7 Summary

References

2 Battery Modeling

2.1 Background

2.2 Electrochemical Models

2.3 Black Box Models

2.4 Equivalent Circuit Models

2.5 Experiments

2.6 Parameter Identification Methods

2.7 Case Study

2.8 Model Uncertainties

2.9 Other Battery Models

2.10 Summary

References

3 Battery State of Charge and State of Energy Estimation

3.1 Background

3.2 Classification

3.3 Model‐Based SOC Estimation Method with Constant Model Parameters

3.4 Model‐Based SOC Estimation Method with Identified Model Parameters in Real‐Time

3.5 Model‐Based SOE Estimation Method with Identified Model Parameters in Real‐Time

3.6 Summary

References

4 Battery State of Health Estimation

4.1 Background

4.2 Experimental Methods

4.3 Model‐Based Methods

4.4 Joint Estimation Method

4.5 Dual Estimation Method

4.6 Summary

References

5 Battery State of Power Estimation

5.1 Background

5.2 Instantaneous SOP Estimation Methods

5.3 Continuous SOP Estimation Method

5.4 Summary

References

6 Battery Charging

6.1 Background

6.2 Basic Terms for Evaluating Charging Performances

6.3 Charging Algorithms for Li‐Ion Batteries

6.4 Optimal Charging Current Profiles for Lithium‐Ion Batteries

6.5 Lithium Titanate Oxide Battery with Extreme Fast Charging Capability

6.6 Summary

References

7 Battery Balancing

7.1 Background

7.2 Battery Sorting

7.3 Battery Passive Balancing

7.4 Battery Active Balancing

7.5 Battery Active Balancing Systems

7.6 Summary

References

8 Battery Management Systems in Electric Vehicles

8.1 Background

8.2 Battery Management Systems

8.3 Typical Structure of BMSs

8.4 Representative Products

8.5 Key Points of BMSs in Future Generation

8.6 Summary

References

Index

End User License Agreement

List of Tables

Chapter 1

Table 1.1 Parameters of a typical PEV.

Table 1.2 Energy demands for five standard driving cycles.

Table 1.3 Driving distances for repetition of five standard driving cycles.

Table 1.4 Comparison of four types of batteries in EVs.

Chapter 2

Table 2.1 Specifications of three types of battery cells.

Table 2.2 Comparison of ECMs at different orders of RC networks under SHPPC test...

Table 2.3 AICs for the NMC cell using the OFFPIM.

Table 2.4 Statistical results of voltage estimation error with the UDDS testing ...

Table 2.5 Statistical results of voltage estimation error under SHPPC testing da...

Table 2.6 Statistical results of voltage estimation error under UDDS testing dat...

Table 2.7 Statistical results of voltage predication errors for the aged cell un...

Table 2.8 Voltage prediction errors for the aging cell under UDDS testing data u...

Table 2.9 Statistical results for the LMO cell using the OFFPIM.

Table 2.10 Statistical results for the LMO cell using the ONPIM.

Table 2.11 Statistical results of voltage prediction error using the OFFPIM at 1...

Table 2.12 Statistical results of voltage prediction error using the ONPIM at 10...

Chapter 3

Table 3.1 Procedure for SOC calculation based on EKF.

Table 3.2 Procedure to calculate SOC based on HIF.

Table 3.3 SOC estimation statistics under UDDS testing data at 25 °C.

Table 3.4 SOC estimation statistics under UDDS testing data with an initial SOC ...

Table 3.5 SOC estimation statistics under UDDS testing data with an initial SOC ...

Table 3.6 SOC estimation statistics under UDDS testing data at 45 °C.

Table 3.7 SOC estimation statistics under NEDC testing data at 45 °C.

Table 3.8 SOC estimation statistics under NEDC testing data at 25 °C.

Table 3.9 SOC estimation statistics under NEDC testing data at 0 °C.

Table 3.10 SOC estimation statistics under UDDS testing data at 0 °C.

Table 3.11 SOE estimation statistics under CTCDC testing data at 0 °C.

Table 3.12 SOE estimation statistics under CTCDC testing data with an initial SO...

Table 3.13 SOE estimation statistics under NEDC testing data at 25 °C.

Table 3.14 SOE estimation statistics under CTCDC testing data at 25 °C.

Chapter 4

Table 4.1 Results of fitting function.

Table 4.2 Comparison between experimental SOH and model SOH.

Table 4.3 Equations to predict capacity fade at a given discharge rate.

Table 4.4 Identified parameters by GA.

Table 4.5 SOC and capacity estimation and their estimation errors.

Chapter 5

Table 5.1 Design limits for a LiB pack.

Table 5.2 Design limits on SOC, terminal voltage, current, and power for continu...

Table 5.3 Identified model parameters.

Table 5.4 Design limits for power capability estimation (30 seconds).

Chapter 6

Table 6.1 Major characteristics of charging algorithms for Li‐ion batteries.

Table 6.2 Main characteristics of a LTO battery.

Chapter 7

Table 7.1 OCVs (V) of five cells at SOCs of 30%, 50%, and 70%.

Table 7.2 DC internal resistances (Ω) of five cells at SOCs of 30%, 50%, and 70%...

Table 7.3 Coefficients for relationship between SOC and OCV of a battery cell.

Table 7.4 Parameters for equivalent circuit model of a battery cell.

Table 7.5 Specification of three LFP battery cells in the pack.

Table 7.6 Comparison of active balancing systems using different balancing crite...

Table 7.7 Rule base of FL controller for linguistic variables.

Table 7.8 Specifications of two types of battery cells.

Table 7.9 Balancing results for a LFP battery pack.

Table 7.10 Balancing results for a NCA battery pack.

Chapter 8

Table 8.1 CAN communications in EVs.

Table 8.2 Technical specifications of E‐power BMS.

Table 8.3 Typical battery management ICs.

List of Illustrations

Chapter 1

Figure 1.1 Forces applied on a vehicle.

Figure 1.2 Speed versus time under UDDS.

Figure 1.3 Speed versus time under FHDS.

Figure 1.4 Speed versus time under ECE‐15.

Figure 1.5 Speed versus time under EUDC.

Figure 1.6 Speed versus time under NEDC.

Figure 1.7 Speed versus time under Japanese 10.15 Mode.

Figure 1.8 Traction power versus time under UDDS.

Figure 1.9 Traction power versus time under FHDS.

Figure 1.10Figure 1.10 Traction power versus time under ECE‐15.

Figure 1.11Figure 1.11 Traction power versus time under NEDC.

Figure 1.12Figure 1.12 Traction power versus time under Japanese 10.15 Mode.

Figure 1.13 Schematic diagram of an electrochemical cell.

Figure 1.14 Schematic diagram of a lead–acid battery.

Figure 1.15 Schematic diagram of a NiCd battery.

Figure 1.16 Schematic diagram of a NiMH battery.

Figure 1.17 Schematic diagram of a Li‐ion battery.

Figure 1.18 Battery management system for EVs.

Chapter 2

Figure 2.1 Internal structure of a LiB for P2D.

Figure 2.2 NN model for the relationship of SOC to terminal voltage and current...

Figure 2.3

n

‐RC model for a LiB.

Figure 2.4 OCV–SOC relationships for four types of LiB materials: (a) LMO, LTO,...

Figure 2.5 OCV–SOC relationships at different (a) aging status and (b) operatin...

Figure 2.6 OCV–SOC relationships during charging and discharging processes at d...

Figure 2.7 Battery test bench.

Figure 2.8 Flow chart of test schedules.

Figure 2.9 Experimental results of a SHPPC test for the NMC cell at 25 °C: (a) ...

Figure 2.10 UDDS cycles for the NMC cell at 25 °C: (a) current; (b) voltage; (c...

Figure 2.11 FUDS cycles for the LMO cell at 25 °C: (a) current; (b) voltage; (c...

Figure 2.12 Flow chart of the offline parameter identification method.

Figure 2.13 Flowchart of online parameter identification method.

Figure 2.14 OCVs at different orders of ECMs identified by the OFFPIM for the N...

Figure 2.15 Comparison of OCV errors for different orders of ECM for the NMC ce...

Figure 2.16 Results of voltage estimation error considering the error compensat...

Figure 2.17 OCVs at different orders of ECMs identified by ONPIM for the NMC ce...

Figure 2.18 Estimated OCV errors for ECMs with different orders.

Figure 2.19 Voltage predication errors under UDDS testing data of the aged cell...

Figure 2.20 Voltage prediction errors for the aged cell under UDDS testing data...

Figure 2.21 OCVs for different orders of ECMs using the OFFPIM for the LMO cell...

Figure 2.22 Voltage prediction errors under FUDS testing data using the OFFPIM ...

Figure 2.23 OCVs for different orders of ECMs using the ONPIM for the LMO cell ...

Figure 2.24 Voltage prediction errors under FUDS testing data using the ONPIM f...

Figure 2.25 Voltage prediction errors at 10 °C when the model parameters are id...

Figure 2.26 Some battery models.

Chapter 3

Figure 3.1 Battery SOC estimation methods.

Figure 3.2 OCV versus SOC curve for a LiB cell.

Figure 3.3 A general framework of model‐based SOC estimation methods.

Figure 3.4 One RC network – Thevenin model.

Figure 3.5 Flowchart of EKF‐based estimation methods for battery SOC.

Figure 3.6 Thevenin model parameters obtained by hybrid pulse test for a LiB ce...

Figure 3.7 The change of Thevenin model parameters with SOC variation: (a)

d

OCV...

Figure 3.8 Flowchart of the HIF‐based SOC estimation method.

Figure 3.9 SOC estimation results under UDDS testing data at 25 °C: (a) voltage...

Figure 3.10 SOC estimation results under UDDS testing data at 25 °C with assump...

Figure 3.11 SOC estimation results under UDDS testing data at 25 °C with assump...

Figure 3.12 Estimation results of terminal voltage and SOC under UDDS testing d...

Figure 3.13 Estimation results of terminal voltage and SOC under NEDC testing d...

Figure 3.14 Estimation of terminal voltage and SOC under NEDC testing data at 2...

Figure 3.15 Estimation of terminal voltage and SOC under NEDC testing data at 0...

Figure 3.16 Battery SOC estimation method based on real‐time experimental data ...

Figure 3.17 Estimation of terminal voltage and SOC under UDDS testing data at 0...

Figure 3.18 CTCDC profile: (a) speed versus time; (b) current versus time; (c) ...

Figure 3.19 SOE estimation results under CTCDC at 0 °C: (a) voltage; (b) voltag...

Figure 3.20 SOE estimation results under CTCDC at 25 °C with initial SOE values...

Figure 3.21 SOE estimation results under NEDC testing data at 25 °C: (a) voltag...

Figure 3.22 SOE estimation results under CTCDC at 25 °C: (a) voltage; (b) volta...

Chapter 4

Figure 4.1 Classification of SOH estimation methods.

Figure 4.2 Current and voltage profile in a discharge and charge pulse.

Figure 4.3 EIS results under different aging status.

Figure 4.4 SEM images of fresh and aged battery cells: (a) fresh cell cathode; ...

Figure 4.5 Current and voltage profiles of the CC/CV charging method.

Figure 4.6 Charging times for CC, CV and CC/CV for a NMC LiB from Kokam.

Figure 4.7 (a) Voltage–capacity curves and (b) IC of the charge and discharge r...

Figure 4.8 Evolution of IC curves for cells cycling at DOD of 50% at (a) 40 °C,...

Figure 4.9 Voltage–capacity curve and DVA curve at CC charging regime at C/3 an...

Figure 4.10 Evolution of DVA curves for cells cycling at DOD of 50% at (a) 40 °...

Figure 4.11 Evolution of

Q

A

,

Q

B

, and

Q

C

for cells cycling at DOD of 50% at (a) ...

Figure 4.12 General operation framework to identify battery model parameters.

Figure 4.13 Frameworks for (a) joint estimation and (b) dual estimation of batt...

Figure 4.14 Double RC network‐based impedance model.

Figure 4.15 Identified SEI resistance.

Figure 4.16 Capacity degradation of a LiB cell.

Figure 4.17 Correlation between SEI resistance and available capacity.

Figure 4.18 Exponential fitting curves and fitting errors.

Figure 4.19 A three‐dimensional response surface‐based battery OCV model.

Figure 4.20 Flowchart for available capacity estimation based on SOC definition...

Figure 4.21 Flowchart of capacity estimation algorithm by joint estimator.

Figure 4.22 (a) Estimated battery SOC and (b) the error with the H infinity obs...

Figure 4.23 (a) Capacity estimation of battery and (b) estimation error.

Figure 4.24 Schematic diagram of multi‐time scale AEKF.

Figure 4.25 Flowchart of multi‐time scale AEKF for estimation of battery states...

Figure 4.26 Capacity estimation with a single‐time scale AEKF (

L

z

 = 1 second): ...

Figure 4.27 Capacity estimation with a multi‐time scale AEKF (

L

z

 = 60 seconds):...

Chapter 5

Figure 5.1 Derivative of OCV as a function of SOC.

Figure 5.2 Improved Thevenin model.

Figure 5.3 Peak currents estimation results: (a) peak discharge current with HP...

Figure 5.4 Peak power real‐time results of estimation: (a) peak discharge power...

Figure 5.5 Flowchart of joint estimation of SOC and SOP using the AEKF‐based me...

Figure 5.6 AEKF‐based SOC estimation results by setting a correct initial SOC: ...

Figure 5.7 AEKF‐based SOP estimation by setting a correct initial SOC: (a) peak...

Figure 5.8 SOC estimation obtained by the AEKF‐based joint approach: (a) SOC es...

Figure 5.9 Peak current estimation results under two inaccurate initial SOCs: (...

Figure 5.10 Flowchart of SOC and SOP joint estimation based on RLS and AEKF alg...

Figure 5.11 Estimation results with correct initial SOC: (a) voltage; (b) volta...

Figure 5.12 Estimation results with erroneous initial SOC of 60%: (a) voltage; ...

Figure 5.13 Estimation results with accurate initial SOC (30 seconds): (a) char...

Figure 5.14 SOP estimation with accurate initial SOC (30 seconds): (a) charge p...

Figure 5.15 Peak current estimation for different continuous times: (a) charge ...

Figure 5.16 Current and power capability estimation results with correct and er...

Figure 5.17 Power capability estimation for the cells with different aging leve...

Chapter 6

Figure 6.1 Typical terminal voltage and current profiles of CC/CV charging for...

Figure 6.2 Voltage profiles for the CC/CV charging algorithm with a CC of 1C at...

Figure 6.3 Charging process of a CC/CV charger.

Figure 6.4 Block diagram of a double‐loop control charger.

Figure 6.5 Charging current profiles of FL‐CC/CV, GP‐CC/CV and CC/CV.

Figure 6.6 Block diagram of PLL‐CC/CV.

Figure 6.7 Flow chart for the charging process of PLL‐CC/CV.

Figure 6.8 Charging profile of CPPL‐CC/CV.

Figure 6.9 Charging profile of MSCC with five steps.

Figure 6.10 Charging profile of MSCC with four steps.

Figure 6.11 Experimental results of charging profiles of MSCC with four steps a...

Figure 6.12 Flowchart to implement MSCC with various approaches for charging pr...

Figure 6.13 Charging profile of TSCC/CV.

Figure 6.14 Charging profile of CVCC/CV.

Figure 6.15 Flow chart of FCV‐PC.

Figure 6.16 Charging profile of CC‐PC.

Figure 6.17 ECM of a Li‐ion battery for energy loss modeling.

Figure 6.18 Relationship between OCV and SOC.

Figure 6.19

R

p

and

R

o

versus SOC.

Figure 6.20

C

p

versus SOC.

Figure 6.21 Optimal CCP versus the variation of battery resistance at different...

Figure 6.22 SOC versus charging time at a CC charging rate of 8C.

Figure 6.23 Voltages versus cell charge capacity at CC charging rates of 1C, 5C...

Chapter 7

Figure 7.1 Capacities of 20 Li‐ion cells for capacity screening process.

Figure 7.2 Battery equivalent circuit model in charging process.

Figure 7.3 Voltage responses at a specific SOC for pulse current discharging an...

Figure 7.4 SOM for battery sorting.

Figure 7.5 Average values of available capacity, temperature variation, and int...

Figure 7.6 Clustering results of 12 cells.

Figure 7.7 OCVs versus SOCs of cells in a sorted battery pack.

Figure 7.8 Fixed shunt resistor balancing circuit.

Figure 7.9 Switched shunt resistor balancing circuit.

Figure 7.10 Shunt transistor balancing circuit.

Figure 7.11 Relationship between SOC and terminal voltage for a LFP battery.

Figure 7.12 Switched capacitor balancing circuit.

Figure 7.13 Double‐tiered switched capacitor balancing circuit.

Figure 7.14 Chain structure switched capacitor balancing circuit.

Figure 7.15 Shunt inductor balancing circuit.

Figure 7.16 Cell to pack boost shunting balancing circuit.

Figure 7.17 Cell to pack multiple‐transformer balancing circuit.

Figure 7.18 Cell to pack multi‐secondary winding transformer balancing circuit.

Figure 7.19 Cell to pack switched transformer balancing circuit.

Figure 7.20 Pack to cell multiple‐transformer balancing circuit.

Figure 7.21 Pack to cell multi‐secondary winding transformer balancing circuit.

Figure 7.22 Pack to cell switched transformer balancing circuit.

Figure 7.23 Cell to tank to cell single switched inductor balancing circuit.

Figure 7.24 Single switched capacitor balancing circuit.

Figure 7.25 Cell to pack to cell bidirectional switched transformer balancing c...

Figure 7.26 Cell to pack to cell bidirectional multiple transformers balancing ...

Figure 7.27 Cell to pack to cell bidirectional multi‐secondary windings transfo...

Figure 7.28 Schematic diagram of battery equivalent circuit model in charging p...

Figure 7.29 Experimental results of the PCC test: (a) pulse charge currents; (b...

Figure 7.30 OCV–SOC relationship for a LFP battery from the PCC test.

Figure 7.31 Flyback converter balancing circuit with battery pack.

Figure 7.32 Pack balancing performance in the charging process with the SOC as ...

Figure 7.33 Pack balancing performance in the charging process with terminal vo...

Figure 7.34 MSI balancing system with FL controller.

Figure 7.35 Control signals of MOSFET. DT,

dead‐time

.

Figure 7.36 Operation modes of MSI balancing circuit in one cycle.

Figure 7.37 Simulation results for MSI balancing circuit with constant equal mo...

Figure 7.38 Block diagram of FL controller of MSI balancing circuit.

Figure 7.39 Membership functions of OCVs.

Figure 7.40 Membership function of OCV differences.

Figure 7.41 Membership function of inductor currents.

Figure 7.42 Equivalent gain for desired inductor current with input

V

d

at OCV o...

Figure 7.43 Inductor current with input

V

d

at OCV of 3.3 V.

Figure 7.44 Inductor currents with two inputs of

V

d

and

V

oc

.

Figure 7.45 Experimental results for (a) OCV and (b) average balancing current ...

Figure 7.46 Experimental results for (a) OCV and (b) average balancing current ...

Figure 7.47 Experimental results for (a) OCV and (b) average balancing current ...

Figure 7.48 Experimental results for (a) OCV and (b) average balancing current ...

Chapter 8

Figure 8.1 Basic BMS hardware structure for EVs.

Figure 8.2 Basic BMS hardware circuits.

Figure 8.3 Discrete circuit of Multi‐channel AD conversion for sampling battery...

Figure 8.4 Voltage measurement using LT6803‐3.

Figure 8.5 Battery passive balancing circuit with switching shunt resistor.

Figure 8.6 Passive balancing circuit.

Figure 8.7 Actual passive balancing circuit.

Figure 8.8 Transformer‐based active balancing circuit.

Figure 8.9 Active balancing circuit with the IC of LTC3300.

Figure 8.10 Actual active balancing circuit.

Figure 8.11 Architecture of centralized BMS.

Figure 8.12 Architecture of distributed BMS.

Figure 8.13 E‐Power BMS architecture.

Figure 8.14 Klclear BMS architecture.

Figure 8.15 Schematic of nickel foil‐based self‐heating.

Figure 8.16 Architecture of cloud computing for BMSs in EVs.

Figure 8.17 Flow chart of RUL prediction based on deep learning.

Guide

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Advanced Battery Management Technologies for Electric Vehicles

Rui Xiong

Beijing Institute of Technology China

Weixiang Shen

Swinburne University of Technology Australia

Copyright

This edition first published 2019

© 2019 John Wiley & Sons Ltd

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The right of Rui Xiong and Weixiang Shen to be identified as the authors of this work has been asserted in accordance with law.

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Library of Congress Cataloging-in-Publication Data

Names: Xiong, Rui, author. | Shen, Weixiang, author.

Title: Advanced battery management technologies for electric vehicles / Rui

   Xiong, Beijing Institute of Technology, China, Weixiang Shen, Swinburne

   University of Technology, Australia.

Description: Hoboken, NJ : John Wiley & Sons, Inc., [2019] | Series:

   Automotive Series | Includes bibliographical references and index. |

   Identifiers: LCCN 2018044881 (print) | LCCN 2018046237 (ebook) | ISBN

   9781119481676 (Adobe PDF) | ISBN 9781119481683 (ePub) | ISBN 9781119481645

   (hardcover)

Subjects: LCSH: Electric vehicles-Batteries.

Classification: LCC TL220 (ebook) | LCC TL220 .X56 2018 (print) | DDC

   629.25/024-dc23

LC record available at https://lccn.loc.gov/2018044881

Cover Design: Wiley

Cover Images: © solarseven/Shutterstock, © 3DMI/Shutterstock, © Ungor/Shutterstock, © buffaloboy/Shutterstock

Automotive Series

Series Editor: Thomas Kurfess

Advanced Battery Management Technologies for Electric Vehicles

Xiong and Shen

January 2019

Automotive Power Transmission Systems

Zhang

September 2018

Hybrid Electric Vehicles: Principles and Applications with Practical Perspectives, 2nd Edition

Mi and Masrur

October 2017

Hybrid Electric Vehicle System Modeling and Control, 2nd Edition

Liu

April 2017

Thermal Management of Electric Vehicle Battery Systems

Dincer, Hamut, and Javani

March 2017

Automotive Aerodynamics

Katz

April 2016

The Global Automotive Industry

Nieuwenhuis and Wells

September 2015

Vehicle Dynamics

Meywerk

May 2015

Vehicle Gearbox Noise and Vibration: Measurement, Signal Analysis, Signal Processing and Noise Reduction Measures

Tůma

April 2014

Modeling and Control of Engines and Drivelines

Eriksson and Nielsen

April 2014

Modelling, Simulation and Control of Two‐Wheeled Vehicles

Tanelli, Corno, and Savaresi

March 2014

Advanced Composite Materials for Automotive Applications: Structural Integrity and Crashworthiness

Elmarakbi

December 2013

Guide to Load Analysis for Durability in Vehicle Engineering

Johannesson and Speckert

November 2013

Biographies

Rui Xiong received his MSc degree in vehicle engineering and PhD degree in mechanical engineering from Beijing Institute of Technology, Beijing, China, in 2010 and 2014, respectively. He conducted scientific research as a joint PhD student in the DOE GATE Centre for Electric Drive Transportation at the University of Michigan, Dearborn, MI, USA, between 2012 and 2014.

Since 2014, he has been an Associate Professor in the Department of Vehicle Engineering, School of Mechanical Engineering, Beijing Institute of Technology, Beijing, China. Since 2017, he has been an Adjunct Professor at the Faculty of Science, Engineering and Technology, Swinburne University of Technology, Melbourne, Australia. He has conducted extensive research on electric vehicles and hybrid electric vehicles, energy storage and battery management systems, and authored more than 100 peer‐reviewed articles and held ten patents in the relevant research fields.

Dr Xiong was a recipient of the Excellent Doctoral Dissertation from Beijing Institute of Technology in 2014, and the first prize of the Chinese Automobile Industry Science and Technology Invention Award in 2018. He received the 2018 Best Vehicular Electronics Paper Award recognizing his paper as the best paper in Vehicular Electronics that had been published in the IEEE Transactions on Vehicular Technology over the past 5 years, and the Best Paper Awards from Energies. He is as an Associate Editor of IEEE Access, and an Associate Editor of SAE International Journal of Alternative Powertrains. He is also serving on the Editorial Board of Applied Energy, Energies, Sustainability, and Batteries. He was a conference chair of the International Symposium on Electric Vehicles (ISEV2017) held in Stockholm, Sweden, 2017, and a conference chair of the International Conference on Electric and Intelligent Vehicles (ICEIV 2018) held in Melbourne, Australia, 2018.

Weixiang Shen received his BEng, MEng and PhD degrees in electrical engineering. Dr Shen is an Associate Professor at the Faculty of Science, Engineering and Technology, Swinburne University of Technology, Melbourne, Australia. From 1993 to 1994, he was a Visiting Scholar at The University of Stuttgart, Stuttgart, Germany, where he worked on battery management systems for photovoltaic systems. From 2003 to 2007, he was a Lecturer and Senior Lecturer at Monash University, Malaysia. From 2008 to 2009, he was a research fellow with Nanyang Technological University, Singapore, focusing on battery management systems for purifying wastewater based on membrane technology using solar photovoltaic systems. His research interests include battery charging, battery capacity estimation, battery management systems and integration of electric vehicles and renewable energy sources into power grids. He has authored or co‐authored more than 80 papers published by peer‐reviewed journals in the relevant research areas.

Dr Shen is an editor for the journal Vehicles, a guest editor for a special issue on “Advanced Energy Storage Techniques towards Sustainable Transportation” for the journal Sustainability, and a guest editor for a special issue on “Advanced Energy Storage Technologies and Their Applications” for the journal IEEE Access. He is an active reviewer for leading journals such as IEEE Transaction on Vehicular Technology, IEEE Transaction on Power Electronics, IEEE Transaction on Industrial Electronics, IEEE Transaction on Energy Conversion, and Journal of Power Sources. He also served as a conference organizing committee member and session chair for a few international conferences, such as The IEEE International Conference on Industrial Electronics and Applications (ICIEA) in 2008–2018. He was a general chair of the International Conference on Energy, Ecology and Environment (ICEEE2018) held in Melbourne, Australia, 2018.

Foreword by Professor Sun

The promotion of electric vehicles is part of a national strategy to reduce oil consumption and air pollution in many countries around the world. In China, the electric vehicle industry has been designated by the central government as a strategic emerging industry that represents what is considered to be the future of automobiles. It is well known that the advancement of battery technologies is crucial to the safe and efficient operation of electric vehicles. The two fundamental characteristics about battery technologies that affect the cost of operation, performance, and durability are power density and energy density. For vehicle applications, it is desirable that batteries have both high power density and high energy density, but there is generally a trade‐off between these two characteristics, resulting in higher power density with a correspondingly lower energy density, or higher energy density with a lower power density.

In recent years, lithium‐ion batteries have been widely accepted for electric vehicle applications due to their superiority in high energy density, high power density, and long cycle life. They exhibit strong coupling effects among electric, thermal and mechanical behaviors in electric vehicle applications, leading to strong time‐varying, ambient temperature dependent and nonlinear characteristics. However, there are few measurable parameters for controlling and monitoring batteries. Currently, the most popular parameters are battery terminal voltage, charge/discharge current, and surface temperature. The lack of effective measurable parameters further complicates and challenges the development of lithium‐ion battery management systems for electric vehicles. The key technologies involved in battery management systems include battery modeling, battery state estimation, battery charging and battery balancing. The fundamental solution to achieving active management of battery systems is to improve battery model accuracy, develop robust multi‐scale and multi‐state estimation approaches, and optimize charging and balancing processes. Research on any of these topics would contribute to the improvement of battery management systems that could reduce the risk of fire or explosion, caused by overcurrent, overvoltage, or overcharge/discharge.

The book Advanced Battery Management Technologies for Electric Vehicles is the culmination of more than a decade of research by Associate Professors Rui Xiong and Weixiang Shen on all important aspects of battery management systems for electric vehicles, including battery system modeling, state of charge estimation, state of energy estimation, state of health estimation, state of power estimation, battery charging and battery balancing, and the implementation of battery management systems. In particular, the book has a comprehensive coverage of the technical details of the core algorithms, which can realize the main functions of battery management systems in electric vehicles. Therefore, this book is not only a valuable reference for professionals, researchers and practicing engineers in battery management systems in electric vehicles and energy storage, but it can also be used as a course book for undergraduate as well as graduate students in engineering, particularly in automotive and electrical engineering.

Associate Professor Xiong is a former PhD student of mine. He started his master and doctoral programs at Beijing Institute of Technology (BIT) in 2008 and 2010, respectively. Immediately after receiving his PhD in 2014, he joined the research team in the National Engineering Laboratory for Electric Vehicles at BIT. Since then, he has been focusing on the research and development of battery management technologies in electric vehicles and carrying out systematic and in‐depth investigations in cutting edge electric vehicle technologies on battery testing, battery modeling, battery states estimation, durability, safety and battery system integration and management, yielding fruitful achievements. This book is the representation of his persistent efforts in the development of advanced battery management technologies. It has also resulted from his close collaboration with Associate Professor Shen, an internationally renowned expert in battery state estimation, battery charging and battery balancing for electric vehicles.

I highly recommend this book not only because it is the first book exclusively devoted to advanced battery management technologies but also because it is the brainchild of Associate Professor Xiong who has made outstanding contributions to the development of battery management algorithms and has played a unique role in promoting advanced and intelligent battery management systems for all‐climate electric vehicles. The results reported in this book are based on the technological achievements of the National Engineering Laboratory for Electric Vehicles at BIT and attributed to his extensive cooperation with top electric vehicle makers in China such as BAIC BJEV, ZhengZhou Yutong Bus, Huawei, and United Automotive Electronics. In summary, this book is a must‐read for anyone who wants to understand the core algorithms and relevant technologies of battery management systems for electric vehicles.

Fengchun Sun

Professor and Academician, Beijing Institute of Technology

Director, National Engineering Laboratory for Electric Vehicles

Director, Collaborative Innovation Center of Electric Vehicles in Beijing

Director, National New Energy Vehicle Monitoring and Management Center

Foreword by Professor Ouyang

Two great challenges facing the Chinese automotive industry are climate change and energy security. First, China is the largest country in automotive production and sales in the world. Chinese vehicle sales and production exceeded 28 million in 2017, accounting for one‐quarter of total world sales, and ranked first in the world for nine consecutive years. This has caused severe urban air pollution. Research shows that tailpipe emissions of current internal combustion engine vehicles are the main source of urban air pollution. They account for approximately 24% of pollution in some major cities, including Beijing, Tianjin, and Shanghai. Secondly, China's crude oil consumption is increasing greatly with the rapid growth of the ownership of internal combustion engine vehicles. China surpassed the United States for the first time to become the world's largest importer of crude oil in 2017. China's crude oil imports reached 8.43 million barrels a day in 2017, up 10% from 2016, compared with the US's 7.91 million barrels a day, leading to accelerating eastward movement of global oil trade.

These challenges require the development of new energy vehicles. The new energy vehicles change the propulsion system from engine to motor and thus essentially change energy sources from fossil fuels to electrochemical energy storage systems, where the stored energy can be derived from renewable energy sources such as wind or solar energy. Consequently, new energy vehicles can reduce urban air pollution and diversify energy sources. Furthermore, mass penetration of new energy vehicles can also lead to integrated sustainable transportation and power grids. Among all the new energy vehicles, electric vehicles such as pure electric vehicles and plug‐in electric vehicles are becoming very attractive for road transportation. By the end of 2015, China had become the world's largest electric vehicle market, and the development of electric vehicles has been determined to be a national strategy for China.

Electric vehicles are partially or wholly driven by a battery system which consists of a combination of series and parallel connections of many battery cells. Lithium‐ion battery cells are currently the most promising for the construction of battery systems due to their favorable performances in energy density, power density, energy efficiency, and life time. The battery system in electric vehicles experiences dynamic and complex operation conditions. Its performances vary strongly with many factors including battery temperature, charge and discharge rate, aging effect, depth of discharge and cell inconsistency in the battery pack for electric vehicles. Therefore, it is indispensable to develop advanced battery management technologies to monitor and control the battery system, thereby assuring its safe and reliable operation.

This book by Associate Professors Rui Xiong and Weixiang Shen presents their research results and contributions in advanced battery management technologies made over more than ten years. The book starts with the fundamental knowledge of battery electrochemistry and electric vehicle dynamics, driving cycles and requirements of battery management systems. It continues with the detailed provision of battery modeling techniques focusing on equivalent circuit models, model‐based estimation methods for state of charge, state of energy, state of health and state of power, and battery charging and balancing techniques. These techniques are all critical in making a safer and more reliable battery system. Finally, the book ends with the integration of all these techniques into battery management systems for electric vehicles. Thus, this book covers the necessary background and techniques for the development of advanced battery management systems for electric vehicles.

A scientific national strategy for 2016–2020 is expected to play a critical role in making China the global leader in the electric vehicle industry. This book is published at a particularly timely moment when the electric vehicle industry in China is in the process of transforming from one that is investment driven to one that is innovation driven. This transformation requires new knowledge and innovative techniques in one of the key technologies for electric vehicles: battery management technologies.

I recommend this book not only because of its solid technical content but also because of the important and unique role Associate Professor Xiong plays in the systematic and original research work for the development of advanced battery management systems. I believe this book can benefit senior undergraduate and postgraduate students who are going to enter the electric vehicle industry. Chemical, mechanical and electrical engineers who are already in the electric vehicle industry can also benefit by systematically learning advanced battery management technologies from this book. Researchers who are working in academia can use this book as an in‐depth source and comprehensive reference to develop new battery management technologies for electric vehicles.

Minggao Ouyang

Professor and Academician, Tsinghua University

Director, State Key Laboratory of Automotive Safety and Energy

Director, China–US Clean Energy Research Centre‐Clean Vehicle Consortium

Series Preface

Batteries have been used in vehicles for well over a century, and with the advent and rise of electric vehicle, the battery is arguably the single most important element of the electric vehicle. As newer generations of electric vehicles including hybrid, plug‐in, and extended range vehicles become available and more popular, the impact of fully exploiting the battery during its entire lifecycle is growing in significance. Squeezing the best performance out of a battery and maximizing its useful life are not only important to electric vehicle OEMs and consumers, but they are key to ensuring that future electric vehicle fleets are highly sustainable.

The Automotive Series publishes practical and topical books for researchers and practitioners in industry, and postgraduate/advanced undergraduates in automotive engineering. The series covers a wide range of topics, including design, manufacture and operation, and the intention is to provide a source of relevant information that will be of interest and benefit to people working in the field of automotive engineering. Advanced Battery Management Technologies for Electric Vehicles is an excellent addition to the series focusing on optimizing the performance and extending the life of one of the most critical elements of any electric vehicle, the battery. While a significant amount of literature available to the modern automotive engineer focuses on concepts such as battery design and specifications, there is a lack of information regarding the management of the battery including the battery's health, charging cycles, and performance. It is the only text currently available that discusses practical implementation of use strategies for batteries that are important to the battery's, and thus the vehicle's, performance. It also presents key concepts that are related to the overall life cycle of the battery. This wealth of information is not only critical to the performance of the electric vehicle, but it is also paramount to the value proposition of the vehicle. For example, choosing the correct charging and discharging cycles for the battery will have a direct effect on the life span and performance of the battery, directly affecting the marketability and sustainability of the vehicle.

As is mentioned in the beginning of this preface, Advanced Battery Management Technologies for Electric Vehicles is part of the Automotive Series; however, batteries are found on a wide variety of other systems outside of the automotive sector. Thus, the concepts presented in this text are applicable across a wide variety of fields. In particular, most of our next generation renewable energy sources require the ability to store energy for use at a later time. For example, solar plants generate significant power during the day, but not at night. Energy storage units employing large battery banks are one means by which solar energy may be tapped when the sun goes down. Issues related to battery management technologies such as charging, balancing, charge and state estimation will be valuable in any technology sector that employs rechargeable batteries for high power applications. This makes the content of the text applicable across a wide variety of technology fields, and of significant use beyond the automotive sector.

Advanced Battery Management Technologies for Electric Vehicles provides a set of well‐focused and integrated topics that are critical to battery systems management. It presents these topics with a special focus on the automobile. The text employs a set of well thought out case studies, clearly demonstrating the utility and application of the fundamental concepts that are developed by the authors. It is state‐of‐the‐art text, written by recognized experts in the field and is a valuable resource for practitioners in the field. It is an excellent addition to the Automotive Series.

Thomas Kurfess

November 2018

Preface

Electric vehicles (EVs) have been widely recognized as the most environmentally friendly form of road transport. Over the past decade, there have been significant advancements in EV technologies. Such advancements have seen EVs gradually replace conventional internal combustion engine vehicles (ICEVs). Some experts foresee sale volumes of EVs will surpass those of ICEVs in the next 10–20 years. In the current global EV market, China has played a dominant role in EV manufacturing and sales. China's EV sales have topped the world for the third consecutive year since 2015 and are predicted to account for 57% of the world's EV sales by 2035.

With the rapid uptick in EV sales, EV technologies have been developed at an accelerated pace. Among these EV technologies, researchers have been conducting studies on battery management technologies for many years. Weixiang Shen is a leader of applied battery research for EVs and renewable energy systems at Swinburne University of Technology. He is a pioneer researcher on battery management technologies for EVs and has been working in the area for more than 20 years. Rui Xiong is a leader of advanced energy storage and application at Beijing Institute of Technology. He has been working on battery management technologies for about 10 years. They both have published numerous conference and journal papers, successfully completed many industrial projects and engaged in consultancy work on the topic of battery management technologies for EVs. While conducting research, the authors have found that although there is a wealth of information for battery management technologies in the public domain literature, there is not a comprehensive and specific book focusing on battery management technologies in EVs as yet. The aim of this book is to bridge this gap.

Lithium‐ion (Li‐ion) batteries have been widely used in EVs due to their high energy density, high power density, high voltage, low self‐discharge, and long cycle life in comparison with other secondary batteries. However, behaviors of Li‐ion batteries are greatly affected by their working environment. In particular, Li‐ion battery systems in EVs operate in a more dynamic working environment than those in portable electronic devices such as laptops and mobile phones. The charge and discharge currents and thus voltages of the battery systems fluctuate significantly when EVs are in regenerative braking and acceleration and their operation temperatures vary greatly when EVs are driving during different seasons in various locations. The battery systems under such large current and temperature variations along with rapid charge–discharge cycles require sophisticated battery management systems (BMSs). The purpose of BMSs is to regulate the operation of the battery systems within allowable voltage, current and temperature ranges and to estimate battery states for EV optimal operations. This leads to the development of advanced battery management technologies, which will be presented in this book.

To make this book self‐contained, vehicle dynamics and standard EV driving cycles are introduced to provide the basis to discuss power and energy requirements of EVs and to evaluate EV performances. Also included is an introduction to the electrochemistry of different battery systems applicable to EVs. The key battery management technologies and BMSs have also been introduced. Beyond the basics, the book focuses on battery modeling technique and estimation techniques of battery state of charge, state of energy, state of health and state of power. For each of these techniques, there is the detailed description of the techniques accompanied with step‐by‐step mathematical equations. Case studies are provided with experimental and simulation results to demonstrate the applications of these techniques in EV working conditions. Furthermore, this book discusses battery charging and balancing techniques. The application of all the above‐mentioned techniques to BMSs and the key technologies of BMSs in future generation are also discussed. In addition to referencing the relevant work of other researchers, a large portion of the materials presented in this book is the collection of many years of research and development by the authors.

This book consists of eight chapters. In Chapter 1, the fundamental knowledge about EVs, requirements of battery systems for EVs and battery systems applicable to EVs such as lead–acid, nickel–cadmium, nickel–metal hydride and Li‐ion batteries are introduced. The overview of the key battery management technologies, the BMS structures and BMS functions are presented.

In Chapter 2, the classification of battery modeling techniques is provided. Then, the chapter goes on to focus on the explanation of battery equivalent circuit models in terms of the model structure, open circuit voltage, polarization and hysteresis characteristics. Based on the equivalent circuit models, offline parameter identification methods and online parameter identification methods are introduced, followed by case studies showing the application of each method. Furthermore, the influences of battery aging, battery type and battery temperature on accuracies of equivalent circuit models are discussed in detail.

In Chapter 3, the classification of battery state of charge estimation methods is introduced. This includes look‐up table methods, an ampere‐hour integral method, data‐driven estimation methods, and model‐based estimation methods. Following this, a detailed explanation is given to model‐based state of charge estimation methods with constant model parameters using Kalman filter and an H infinity filter. The influences of the uncertainties on the state of charge estimation methods are discussed. For real EV applications, model‐based state of charge and state of energy estimation methods with identified model parameters in real‐time are emphasized. The MATLAB codes and Simulink models used in the case studies to implement these estimation methods are provided to users.

In Chapter 4