Battery Management Algorithm for Electric Vehicles

310 Pages·2020·16.54 MB·English

by  Rui Xiong

Rui Xiong
Battery
Management
Algorithm
for Electric
Vehicles
Battery Management Algorithm for Electric
Vehicles
Rui Xiong
Battery Management
Algorithm for Electric
Vehicles
123
RuiXiong
NationalEngineeringLaboratoryforElectric
Vehicles,SchoolofMechanicalEngineering
Beijing Institute of Technology
Beijing,China
ISBN978-981-15-0247-7 ISBN978-981-15-0248-4 (eBook)
https://doi.org/10.1007/978-981-15-0248-4
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Foreword
The promotion of new energy vehicles represented by electric vehicles, fuel cell
vehicles, and plug-in hybrid vehicles is a worldwide strategy to alleviate climate
change and enhance energy security in many countries. In China, vigorously
developing the electric vehicle industry has become a strategic development
direction for governments, institutes, and automobile enterprises. By the end of
2018, China had been the world’s largest producer of electric vehicles in terms of
sales volume for four consecutive years. An unprecedented revolution in the
automotive industry is happening in China and around the world.
The battery system, which consists of a combination of series and parallel
connections of battery cells, can be the ideal power source for the electric vehicle.
For the past few years, lithium-ion battery cells have been the most promising
energy storage candidates for electric vehicles due to their excellent performances
in high energy density, high power density, high energy efficiency, and long cycle
life. However, they exhibit strong coupling effects among electric, thermal, and
mechanical behaviors in electric vehicle applications, resulting in strong
time-varying, ambient temperature- dependent, and nonlinear characteristics. As a
complexelectrochemicalsystem,thereareonlyafewparameters,includingbattery
terminal voltage, charge or discharge current, and surface temperature that can be
measured or monitored from battery cells. In addition, the performance of battery
system in an electric vehicle is strongly correlated to a range offactors including
ambient temperature, aging status, depth of discharge, dynamic and complicated
operation conditions, charge and discharge current rate, and cell inconsistency in
the battery pack. These difficulties and challenges have restricted the development
ofelectricvehicles.Therefore,moreresearchintoadvancedbatterytechnologiesis
crucial for the safe and efficient operation of electric vehicles.
The bookcovers the research results ofProf. RuiXiong inbattery management
techniquesforelectricvehicles.Thisbookstartswithplansfornewenergyvehicle
development in China as well as around the world and an overview of battery
managementsystem(BMS)technologies.Itcontinuesbysystematicallydescribing
methods of battery experiments and basic theories of battery modeling including
electrochemical models, equivalent circuit models, and fractional-order models.
v
vi Foreword
After that, it presents detailed algorithm theories and implementation processes of
battery states estimation, remaining useful life prediction, low-temperature heating
strategies, and optimized charging methods. Finally, the book ends with the
research and development process of the battery management system algorithms.
Thus, this book covers the necessary background and techniques for the develop-
ment of core algorithms in the battery management system for electric vehicles.
Professor Xiong has been engaged in extensive research and development on
electric vehicles and hybrid electric vehicles, energy storages, and BMS for more
thanonedecade.AfterreceivinghisPh.D.degreein2014,hestartedresearchwork
as an associate professor in the National Engineering Laboratory for Electric
Vehicles, School of Mechanical Engineering, Beijing Institute of Technology,
Beijing, China. Since 2019, he was promoted to professor at Beijing Institute of
Technology. He has also been an Adjunct Professor at the Faculty of Science,
Engineering and Technology, Swinburne University of Technology, Melbourne,
Australia since 2017. Over the years, he has been carrying out systematical and
in-depth investigations in cutting-edge electric vehicle technologies on battery
testing, modeling, active controlling, reliability, durability and safety, all-climate
batteries and battery system integration, management and application, yielding
outstanding achievements. This book is the concentrated representation of his
persistent efforts and experiences in the development of core algorithms in battery
management system for electric vehicles.
Ihighlyrecommend thisbooktopeople whoareworking inthefieldofbattery
management and controlling represented by scholars and researchers, senior
undergraduate, and postgraduate students as well as engineers and technicians.
First, this book has solid technical content and can provide in-depth theory source
and comprehensive reference for those people. Second, it is the quintessence of
Prof. Xiong who is the world’s leading expert in this field. He has made excellent
contributions to the research and development of core algorithms for the battery
management system and played a unique role in promoting advanced and intelli-
gentbatterymanagementsystemsforall-climateelectricvehicles.Insummary,this
book is a required material for people who are going to enter the electric vehicle
industry and want to acquire core algorithms and relevant technologies of the
battery management system for electric vehicles.
Fengchun Sun
Professor and Academician
Beijing Institute of Technology
Beijing, China
Preface
The development of energy-saving and new energy vehicles is an international
consensus and the China’s strategic emerging industry, as well as a key develop-
ment direction for “Made in China 2025”. According to the statistics of China
AssociationofAutomobileManufacturers,theannualproductionandsalesvolume
of new energy vehicles in China reached 1.27 million and 1.256 million in 2018.
By the end of 2018, the global sales of new energy vehicles exceeded 5.5 million,
and China accounted for more than 53%. China makes significant contributions to
reducing energy consumption and carbon emission. Obviously, the new energy
vehicle industry has ushered in unprecedented development opportunities, and at
the same time led to the rapid development of the lithium-ion battery industry.
The battery system is the technical bottleneck of new energy vehicles, and the
battery management technology is the core and key to ensure the high efficiency,
safety, and long life of the battery system. Batteries have the characteristics of
limited measurable parameters and characteristic coupling, degradation with
cycling, strong time-varying, strong ambient temperature dependence, and strong
nonlinearity. Therefore, battery state estimation with high precision and strong
robustness is the fundamental way to actively manage the battery system.
Improving the dynamic modeling accuracy of the battery system, breaking the
technical bottleneck of multistate collaborative estimation, strengthening the
remaining life prediction and durability management, and ensuring the realization
of the expected service life becomes an important part of the battery management
system.Theyarethekeytoensurethevitalityandsustainabledevelopmentofnew
energy vehicle market and also a basic requirement for building confidence to the
general public in the new energy vehicle.
This book combines the author’s research practice for more than 10 years, and
elaboratesthetechnicaldetailsofthecorealgorithmdevelopmentofthenewenergy
vehicle battery management system. Chapter 1 analyzes the new energy vehicle
developmentplanandthetechnicalindicatorsofthebatterymanagementsystemin
“The 13th Five-Year Plan” of China, and systematically expounds the key points
of the design and implementation of the battery management system. Chapter 2
describes the construction of the battery test platform, the experimental method
vii
viii Preface
designandthecharacteristicanalysis,andthensystematicallyanalyzestheworking
characteristics of the battery under different conditions of aging, temperature, and
charge/discharge current rates, which provides the direction for the development
of the core algorithm of the battery management system. Chapters 3–7 systemati-
cally discuss the theory basis and construction and implementation details of core
algorithms,whichincludethebatterysystemmodeling,stateofchargeandstateof
health collaborative estimation, peak power prediction, remaining useful life
prediction, low-temperature rapid heating, and optimized charging methods.
Finally,the“V”developmentprocessofthebatterymanagementsystemalgorithm
is discussed from the perspective of hardware and software simulation, bench test,
and real vehicle verification of the core algorithm. The author strives to make the
text accurate and refined, with clear illustrations and make the content systematic,
detailed,andadvanced,andstrivestohelpreadersmasterthecorealgorithmsofthe
new energy vehicle battery management system.
This book is under the guidance of my supervisors, Academician
Prof. Dr. Fengchun Sun and Prof. Dr. Hongwen He. It is the crystallization of the
research and developments of the National Engineering Laboratory for Electric
Vehicles in the field of new energy vehicle battery and its management and
application. I would like to express my heartfelt thanks to the people who partic-
ipated inthe compilation of this book including Associate Prof. Dr. QuanqingYu,
Postdoc. Yongzhi Zhang, Zeyu Ma, Ph.D. student Cheng Chen, Shanshan Guo,
Jiahuan Lu, Jinpeng Tian, Ju Wang, Ruixin Yang, Xinggang Li, Yanzhou Duan,
Master student Linlin Li, Zhirun Li, Liang Lv, Kan Wang, and so on.
After years of preparation, we are making efforts to dedicate the latest research
progress in the field and the research results and experience in the battery man-
agementsystemstocolleaguesandreaders,tohelpinnovation andprogressinthis
field, and to promote the technological development. Although the book has been
revisedmanytimes,itisstillnotgoodenough.Themainreasonisthatsomework
isstillnotfinishedandsometheoriesandtechnologiesarestillbeingcarried.Ihope
the readers can understand it. We welcome comments and critical comments to
jointly promote the rapid development of research and development of worldwide
battery management systems.
Beijing, China Prof. Dr. Rui Xiong
Contents
1 Overview of Battery and Its Management . . . . . . . . . . . . . . . . . . . . 1
1.1 Developing Plan for the New Energy Vehicle in China . . . . . . . . 2
1.2 Application Requirements for Batteries and BMS. . . . . . . . . . . . . 3
1.2.1 Battery Electric Vehicle . . . . . . . . . . . . . . . . . . . . . . . . . . 5
1.2.2 Hybrid Electric Vehicle . . . . . . . . . . . . . . . . . . . . . . . . . . 5
1.2.3 Plug-in Hybrid Electric Vehicle . . . . . . . . . . . . . . . . . . . . 6
1.2.4 Relevant R&D Indicators . . . . . . . . . . . . . . . . . . . . . . . . . 6
1.3 The Battery . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7
1.3.1 Development Background of the Battery. . . . . . . . . . . . . . 7
1.3.2 Principles and Classification of the Lithium-Ion
Battery . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 9
1.3.3 Lithium Iron Phosphate Battery . . . . . . . . . . . . . . . . . . . . 12
1.3.4 Ternary Lithium-Ion Battery. . . . . . . . . . . . . . . . . . . . . . . 14
1.4 Battery Management System. . . . . . . . . . . . . . . . . . . . . . . . . . . . 17
1.4.1 Basic Functions of the Battery Management System . . . . . 17
1.4.2 Topology of the BMS . . . . . . . . . . . . . . . . . . . . . . . . . . . 19
1.4.3 Development Process of the BMS . . . . . . . . . . . . . . . . . . 22
1.5 Conclusion. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 23
References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 23
2 Battery Test . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 25
2.1 Battery Test Platform . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 25
2.1.1 Charge and Discharge Test Equipment . . . . . . . . . . . . . . . 25
2.1.2 Impedance Characteristic Test Equipment
in Frequency Domain . . . . . . . . . . . . . . . . . . . . . . . . . . . 29
2.1.3 Environmental Simulation Equipment . . . . . . . . . . . . . . . . 30
2.1.4 Battery Test Platform. . . . . . . . . . . . . . . . . . . . . . . . . . . . 32
2.2 Battery Test Process . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 33
2.2.1 Introduction of Testing Standards . . . . . . . . . . . . . . . . . . . 33
2.2.2 BMS Algorithm Development and Experimental
Design . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 34
ix
x Contents
2.2.3 Battery Conventional Electrical Performance Test . . . . . . . 36
2.2.4 AC Impedance Test. . . . . . . . . . . . . . . . . . . . . . . . . . . . . 42
2.2.5 Remaining Life Test . . . . . . . . . . . . . . . . . . . . . . . . . . . . 44
2.3 Battery Test Data . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 49
2.4 Characteristics Analysis of the Battery Experiments . . . . . . . . . . . 49
2.4.1 Temperature Characteristics of the Battery . . . . . . . . . . . . 49
2.4.2 Performance Degradation Characteristics
of the Battery . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 52
2.4.3 Cycle Characteristics of the Battery . . . . . . . . . . . . . . . . . 54
2.5 Conclusion. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 60
References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 61
3 Modeling Theory of Lithium-Ion Batteries . . . . . . . . . . . . . . . . . . . . 63
3.1 Electrochemical Model . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 64
3.1.1 Introduction. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 64
3.1.2 Model Construction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 64
3.1.3 Parameter Identification of the Model . . . . . . . . . . . . . . . . 79
3.1.4 Case Study . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 80
3.2 The Equivalent Circuit Models . . . . . . . . . . . . . . . . . . . . . . . . . . 81
3.2.1 Model Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 81
3.2.2 Model Constructing . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 86
3.2.3 Parameters Identification . . . . . . . . . . . . . . . . . . . . . . . . . 88
3.2.4 Case Study . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 93
3.3 The Fractional-Order Model . . . . . . . . . . . . . . . . . . . . . . . . . . . . 97
3.3.1 Model Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 97
3.3.2 Model Constructing . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 100
3.3.3 Parameter Identification . . . . . . . . . . . . . . . . . . . . . . . . . . 101
3.3.4 Case Study . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 101
3.4 Conclusion. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 103
References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 105
4 Battery SOC and SOH Estimation . . . . . . . . . . . . . . . . . . . . . . . . . . 107
4.1 SOC Estimation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 107
4.1.1 Classification. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 108
4.1.2 Model-Based SOC Estimation Method . . . . . . . . . . . . . . . 113
4.1.3 Battery SOC Estimation Based on AEKF Algorithm . . . . . 117
4.1.4 SOC Estimation Based on HIF Algorithm. . . . . . . . . . . . . 124
4.2 SOH Estimation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 126
4.2.1 Classification of SOH Estimation Methods . . . . . . . . . . . . 127
4.2.2 Capacity Estimation Based on SOC Estimation . . . . . . . . . 132
4.2.3 Available Capacity Estimation Method Based on
Response Surface. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 140
4.2.4 SOH Estimation Method Based on ICA/DVA. . . . . . . . . . 144