Rui Xiong, Weixiang Shen

Advanced Battery Management Technologies for Electric Vehicles

• Gebundenes Buch

Produktbeschreibung

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.

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Produktdetails

• Automotive Series
• Verlag: Wiley / Wiley & Sons
• Artikelnr. des Verlages: 1W119481640
• 1. Auflage
• Seitenzahl: 288
• Erscheinungstermin: 26. Februar 2019
• Englisch
• Abmessung: 251mm x 174mm x 22mm
• Gewicht: 653g
• ISBN-13: 9781119481645
• ISBN-10: 1119481643
• Artikelnr.: 54326952

Autorenporträt

RUI XIONG, PHD, is Associate Professor, Department of Vehicle Engineering, School of Mechanical Engineering, Beijing Institute of Technology, China. He is an Associate Editor of IEEE Access and SAE International Journal of Alternative Powertrains, and Editorial Board member of the Applied Energy, Energies, Sustainability and Batteries. He is the conference chair of the 2017 International Symposium on Electric Vehicles (ISEV2017) and the 2018 International Conference on Electric and Intelligent Vehicles (ICEIV2018) and has authored over 100 peer-reviewed journal articles. WEIXIANG SHEN, PHD, is Associate Professor, Faculty of Science, Engineering and Technology, Swinburne University of Technology, Melbourne, Australia. Dr. Shen is an Editor of Vehicles, a guest Editor of Sustainability, and a guest Editor of IEEE Access. He is the conference chair of the 2018 International Conference on Energy, Ecology and Environment (ICEEE2018) and has published over 80 peer-reviewed journal articles.

Inhaltsangabe

Biographies xi

Foreword by Professor Sun xiii

Foreword by Professor Ouyang xv

Series Preface xvii

Preface xix

1 Introduction 1

1.1 Background 1

1.2 Electric Vehicle Fundamentals 2

1.3 Requirements for Battery Systems in Electric Vehicles 3

1.3.1 Range Per Charge 4

1.3.2 Acceleration Rate 10

1.3.3 Maximum Speed 11

1.4 Battery Systems 11

1.4.1 Introduction to Electrochemistry of Battery Cells 12

1.4.1.1 Ohmic Overvoltage Drop 14

1.4.1.2 Activation Overvoltage 14

1.4.1.3 Concentration Overvoltage 14

1.4.2 Lead-Acid Batteries 15

1.4.3 NiCd and NiMH Batteries 16

1.4.3.1 NiCd Batteries 16

1.4.3.2 NiMH Batteries 17

1.4.4 Lithium-Ion Batteries 18

1.4.5 Battery Performance Comparison 19

1.4.5.1 Nominal Voltage 20

1.4.5.2 Specific Energy and Energy Density 20

1.4.5.3 Capacity Efficiency and Energy Efficiency 20

1.4.5.4 Specific Power and Power Density 20

1.4.5.5 Self-discharge 21

1.4.5.6 Cycle Life 21

1.4.5.7 Temperature Operation Range 21

1.5 Key Battery Management Technologies 21

1.5.1 Battery Modeling 21

1.5.2 Battery States Estimation 23

1.5.3 Battery Charging 24

1.5.4 Battery Balancing 25

1.6 Battery Management Systems 25

1.6.1 Hardware of BMS 26

1.6.2 Software of BMS 26

1.6.3 Centralized BMS 27

1.6.4 Distributed BMS 28

1.7 Summary 28

References 28

2 BatteryModeling 31

2.1 Background 31

2.2 Electrochemical Models 31

2.3 Black Box Models 33

2.4 Equivalent Circuit Models 34

2.4.1 General n-RC Model 35

2.4.2 Models with Different Numbers of RC Networks 35

2.4.2.1 Rint Model 35

2.4.2.2 Thevenin Model 36

2.4.2.3 Dual Polarization Model 37

2.4.2.4 n-RC Model 38

2.4.3 Open Circuit Voltage 39

2.4.4 Polarization Characteristics 42

2.5 Experiments 43

2.6 Parameter Identification Methods 47

2.6.1 Offline Parameter Identification Method 47

2.6.2 Online Parameter Identification Method 50

2.7 Case Study 51

2.7.1 Testing Data 51

2.7.2 Case One - OFFPIM Application 51

2.7.3 Case Two - ONPIM Application 54

2.7.4 Discussions 56

2.8 Model Uncertainties 57

2.8.1 Battery Aging 57

2.8.2 Battery Type 59

2.8.3 Battery Temperature 61

2.9 Other Battery Models 62

2.10 Summary 64

References 64

3 Battery State of Charge and State of Energy Estimation 67

3.1 Background 67

3.2 Classification 67

3.2.1 Look-Up-Table-Based Method 67

3.2.2 Ampere-Hour Integral Method 68

3.2.3 Data-Driven Estimation Methods 69

3.2.4 Model-Based Estimation Methods 70

3.3 Model-Based SOC Estimation Method with Constant Model Parameters 71

3.3.1 Discrete-Time Realization Algorithm 71

3.3.2 Extended Kalman Filter 72

3.3.2.1 Selection of Correction Coefficients 73

3.3.2.2 SOC Estimation Based on EKF 73

3.3.3 SOC Estimation Based on HIF 75

3.3.4 Case Study 77

3.3.5 Influence of Uncertainties on SOC Estimation 78

3.3.5.1 Initial SOC Value 79

3.3.5.2 Dynamic Working Condition 80

3.3.5.3 Battery Temperature 81

3.4 Model-Based SOC Estimation Method with Ident