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Battery Management System and its Applications

Tan, Xiaojun / Vezzini, Andrea / Fan, Yuqian / Khare, Neeta / Xu, You / Wei, Liangliang

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1. Auflage Januar 2023
416 Seiten, Hardcover
Wiley & Sons Ltd

ISBN: 978-1-119-15400-6
John Wiley & Sons

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BATTERY MANAGEMENT SYSTEM AND ITS APPLICATIONS

Enables readers to understand basic concepts, design, and implementation of battery management systems

Battery Management System and its Applications is an all-in-one guide to basic concepts, design, and applications of battery management systems (BMS), featuring industrially relevant case studies with detailed analysis, and providing clear, concise descriptions of performance testing, battery modeling, functions, and topologies of BMS.

In Battery Management System and its Applications, readers can expect to find information on:
* Core and basic concepts of BMS, to help readers establish a foundation of relevant knowledge before more advanced concepts are introduced
* Performance testing and battery modeling, to help readers fully understand Lithium-ion batteries
* Basic functions and topologies of BMS, with the aim of guiding readers to design simple BMS themselves
* Some advanced functions of BMS, drawing from the research achievements of the authors, who have significant experience in cross-industry research

Featuring detailed case studies and industrial applications, Battery Management System and its Applications is a must-have resource for researchers and professionals working in energy technologies and power electronics, along with advanced undergraduate/postgraduate students majoring in vehicle engineering, power electronics, and automatic control.

Contents

Prefacexiii

About the Authorsxv

Part I Introduction 1

1 Why Does a Battery Need a BMS? 3

1.1 General Introduction to a BMS 3

1.1.1 Why a Battery Needs a BMS 3

1.1.2 What Is a BMS? 3

1.1.3 Why a BMS Is Required in Any Energy Storage System 4

1.1.4 How a BMS Makes a Storage System Efficient, Safe, and Dependable 4

1.2 Example of a BMS in a Real System 5

1.2.1 LabView Based BMS 5

1.2.2 PLC Based BMS 6

1.2.3 Microprocessor Based BMS 6

1.2.4 Microcontroller Based BMS 6

1.3 System Failures Due to the Absence of a BMS 7

1.3.1 Dreamline Boeing Fire Incidences 7

1.3.2 Fire Accident at the Hawaii Grid Connected Energy Storage 8

1.3.3 Fire Accidents in Electric Vehicles 8

References 9

2 General Requirements (Functions and Features) 11

2.1 Basic Functions of a BMS 11

2.1.1 Key Parameter Monitoring 11

2.1.2 Battery State Analysis 11

2.1.3 Safety Management 13

2.1.4 Energy Control Management 14

2.1.5 Information Management 14

2.2 Topological Structure of a BMS 16

2.2.1 Relationship Between a BMC and a Cell 16

2.2.2 Relationship Between a BCU and a BMC 16

References 18

3 General Procedure of the BMS Design 19

3.1 Universal Battery Management System and Customized Battery Management System 19

3.1.1 Ideal Condition 19

3.1.2 Feasible Solution 19

3.1.3 Discussion of Universality 20

3.2 General Development Flow of the Power Battery Management System 21

3.2.1 Applicable Standards for BMS Development 21

3.2.2 Boundary of BMS Development 22

3.2.3 Battery Characteristic Test Is Essential to BMS Development 23

3.3 Core Status of Battery Modeling in the BMS Development Process 23

References 25

Part II Li-Ion Batteries 27

4 Introduction to Li-Ion Batteries 29

4.1 Components of Li-Ion Batteries: Electrodes, Electrolytes, Separators, and Cell Packing 29

4.2 Li-Ion Electrode Manufacturing 31

4.3 Cell Assembly in an Li-Ion Battery 32

4.4 Safety and Cost Prediction 33 References 35

5 Schemes of Battery Testing 37

5.1 Battery Tests for BMS Development 37

5.1.1 Test Items and Purpose 37

5.1.2 Standardization of Characteristic Tests 38

5.1.3 Some Issues on Characteristic Tests 40

5.1.4 Contents of Other Sections of This Chapter 41

5.2 Capacity and the Charge and Discharge Rate Test 41

5.2.1 Test Methods 42

5.2.2 Test Report Template 43

5.3 Discharge Rate Characteristic Test 44

5.3.1 Test Method 44

5.3.2 Test Report Template 45

5.4 Charge and Discharge Equilibrium Potential Curves and Equivalent Internal Resistance Tests 46

5.4.1 Test Method for Discharge Electromotive Force Curve and Equivalent Internal Resistance 46

5.4.2 Test Method for Charge Electromotive Force Curve and Equivalent Internal Resistance 47

5.4.3 Discussion of the Test Method 48

5.4.4 Test Report Template 49

5.5 Battery Cycle Test 49

5.5.1 Features of Battery Cycle Test 49

5.5.2 Fixed Rate Cycle Test Method 50

5.5.3 Cycle Test Schemes Based on Standard Working Conditions 52

5.5.4 Test Report Template 57

5.6 Phased Evaluation of the Cycle Process 58

5.6.1 Evaluation Method 59

5.6.2 Estimation of the Test Time 59

5.6.3 Test Report Template 63

References 65

6 Test Results and Analysis 67

6.1 Characteristic Test Results and Their Analysis 67

6.1.1 Actual Test Arrangement 67

6.1.2 Characteristic Test Results of the LiFePO4 Battery 68

6.1.3 Characteristic Test Results of the Li(NiCoMn)O2 Ternary Battery 76

6.1.4 Characteristics Comparison of the Two Battery Types 79

6.2 Degradation Test and Analysis 80

6.2.1 Capacity Change Rule During Battery Degradation 81

6.2.2 Internal Resistance Spectrum Change Rule During Battery Degradation 87

6.2.3 Impact of Storage Conditions on Battery Degradation 96

References 99

7 Battery Modeling 101

7.1 Battery Modeling for BMS 101

7.1.1 Purpose of Battery Modeling 101

7.1.2 Battery Modeling Requirement of BMS 102

7.2 Common Battery Models and Their Deficiencies 102

7.2.1 Non-circuit Models 102

7.2.2 Equivalent Circuit Models 103

7.3 External Characteristics of the Li-Ion Power Battery and Their Analysis 105

7.3.1 Electromotive Force Characteristic of the Li-Ion Battery 105

7.3.2 Over-potential Characteristics of the Li-Ion Battery 107

7.4 A Power Battery Model Based on a Three-Order RC Network 110

7.4.1 Establishment of a New Power Battery Model 110

7.4.2 Estimation of Model Parameters 112

7.5 Model Parameterization and Its Online Identification 117

7.5.1 Offline Extension Method of Model Parameters 117

7.5.2 Online Identification Method of Model Parameters 121

7.6 Battery Cell Simulation Model 124

7.6.1 Realization of Battery Cell Simulation Model Based on Matlab/Simulink 124

7.6.2 Model Validation 125

References 130

Part III Functions of BMS 133

8 Battery Monitoring 135

8.1 Discussion on Real Time and Synchronization 135

8.1.1 Factors Causing Delay 135

8.1.2 Synchronization 136

8.1.3 Negative Impact of Non-real-time and Non-synchronous Problems 137

8.1.4 Proposal on Solution 137

8.2 Battery Voltage Monitoring 139

8.2.1 Voltage Monitoring Based on a Photocoupler Relay Switch Array (PhotoMOS) 139

8.2.2 Voltage Monitoring Based on a Differential Operational Amplifier 140

8.2.3 Voltage Monitoring Based on a Special Integrated Chip 141

8.2.4 Comparison of Various Voltage Monitoring Schemes 142

8.2.5 Significance of Accurate Voltage Monitoring for Effective Capacity Utilization of the Battery Pack 142

8.3 Battery Current Monitoring 145

8.3.1 Accuracy 145

8.3.2 Current Monitoring Based on Series Resistance 146

8.3.3 Current Monitoring Based on a Hall Sensor 147

8.3.4 A Compromised Method 148

8.4 Temperature Monitoring 149

8.4.1 Importance of Temperature Monitoring 149

8.4.2 Common Implementation Schemes 150

8.4.3 Setting of the Temperature Sensor 151

8.4.4 Accuracy 152

References 152

9 SoC Estimation of a Battery 153

9.1 Different Understandings of the SoC Definition 153

9.1.1 Difference on the Understanding of SoC 153

9.1.2 Difference and Relation Between SoC and SoP as Well as SoE 155

9.2 Classical Estimation Methods 158

9.2.1 Coulomb Counting Method 158

9.2.2 Open Circuit Voltage Method 159

9.2.3 A Compromised Method 160

9.2.4 Estimation Methods Not Applicable for the Lithium-Ion Battery 161

9.3 Difficulty in an SoC Estimation 162

9.3.1 Difficulty in an Estimation Resulting from Inaccurate Battery State Monitoring 162

9.3.2 Difficulty in an Estimation Resulting from Battery Difference 164

9.3.3 Difficulty in an Estimation Resulting from an Uncertain Future Working Condition 165

9.3.4 Difficulty in an Estimation Resulting from an Uncertain Battery Usage History 165

9.4 Actual Problems to Be Considered During an SoC Estimation 166

9.4.1 Safety of the Electric Vehicle 166

9.4.2 Feasibility 167

9.4.3 Actual Requirements of Drivers 168

9.5 Estimation Method Based on the Battery Model and the Extended Kalman Filter 169

9.5.1 Common Complicated Estimation Method 169

9.5.2 Advantages of a Kalman Filter in an SoC Estimation 170

9.5.3 Combination of an EKF and a Lithium-Ion Battery Model 171

9.5.4 Implementation Rule of the EKF Algorithm 174

9.5.5 Experimental Verification 176

9.6 Error Spectrum of the SoC Estimation Based on the EKF 177

9.6.1 Estimation Error Caused by the Inaccurate Battery Model 177

9.6.2 Estimation Error Resulting from a Measurement Error of the Sensor 185

9.6.3 Factors Affecting SoC Estimation Accuracy 190

References 191

10 Charge Control 193

10.1 Introduction 193

10.2 Charging Power Categories 196

10.3 Charge Control Methods 198

10.3.1 Semi-constant Current 199

10.3.2 Constant Current (CC) 201

10.3.3 Constant Voltage (CV) 201

10.3.4 Constant Power (CP) 202

10.3.5 Time-Based Charging 202

10.3.6 Pulse Charging 202

10.3.7 Trickle Charging 203

10.4 Effect of Charge Control on Battery Performance 203

10.5 Charging Circuits 204

10.5.1 Half-Bridge and Full-Bridge Circuits 204

10.5.2 On-Board Charger (Level 1 and Level 2 Chargers) 205

10.5.3 Off-Board Charger (Level 3) 206

10.5.4 Fast Charger 206

10.5.5 Ultra-Fast Charger 208

10.6 Infrastructure Development and Challenges 209

10.6.1 Home Charging Station 209

10.6.2 Workplace Charging Station 209

10.6.3 Community and Highways EV Charging Station 210

10.6.4 Electrical Infrastructure Upgrades 210

10.6.5 Infrastructure Challenges and Issues 210

10.6.6 Commercially Available Charges 210

10.7 Isolation and Safety Requirement for EC Chargers 211

References 212

11 Balancing/Balancing Control 213

11.1 Balancing Control Management and Its Significance 213

11.1.1 Two Expressions of Battery Capacity and SoC Inconsistency 213

11.1.2 Significance of Balancing Control Management 215

11.2 Classification of Balancing Control Management 218

11.2.1 Centralized Balancing and Distributed Balancing 218

11.2.2 Discharge Balancing, Charge Balancing, and Bidirectional Balancing 219

11.2.3 Passive Balancing and Active Balancing 220

11.3 Review and Analysis of Active Balancing Technologies 221

11.3.1 Independent-Charge Active Balancing Control 221

11.3.2 Energy-Transfer Active Balancing Control 221

11.3.3 How to Evaluate the Advantages and Disadvantages of an Active Balancing Control Scheme (an Efficiency Problem of Active Balancing Control) 223

11.4 Balancing Strategy Study 226

11.4.1 Balancing Time 227

11.4.2 Variable for Balancing 229

11.5 Two Active Balancing Control Strategies 234

11.5.1 Topologies of Two Active Balancing Schemes 234

11.5.2 Hierarchical Balancing Control Strategy 238

11.5.3 Lead-Acid Battery Transfer Balancing Control Strategy 243

11.6 Evaluation and Comparison of Balancing Control Strategies 245

11.6.1 Evaluation Indexes of Balancing Control Strategies 245

11.6.2 Comparison of Flows for Balancing Strategies 247

11.6.3 Comparison of Balancing Time 249

11.6.4 Comparison of Energy Consumption 251

11.6.5 Comparison of the Impact of Balancing on Battery Life 253

11.6.6 Comparison of the Capacity Utilization Ratio 253

11.6.7 Analysis of the Optimization Case 253 References 255

12 State of Health (SoH) Estimation of a Battery 257

12.1 Definition and Indices/Parameters of SoH 257

12.1.1 Relationship Between Battery Degradation and Battery Life 257

12.1.2 Relationship Between Battery Degradation and SoH of the Battery 259

12.1.3 Main Indicators to Describe Battery Degradation 262

12.2 Modeling of Battery Degradation (Aging) and SoH Estimation 265

12.2.1 Support Vector Regression 266

12.2.2 Battery Degradation Model Based on a Support Vector Regression Machine 269

12.2.3 Steps and Procedures for Evaluating Battery Degradation 276

12.3 Battery Degradation Diagnosis for EVs 278

12.3.1 Offline Degradation Diagnosis of the Power Battery 278

12.3.2 Online Degradation Diagnosis of the Power Battery 281 References 289

13 Communication Interface for BMS 291

13.1 BMS Communication Bus and Protocols 293

13.1.1 System Management Bus (SMBus) 294

13.1.2 BMS: Internal Data Communication 294

13.1.3 BMS: External Data Communication 295

13.2 Higher-Layer Communication Protocols 298

13.3 A Case Study: Universal CiA EnergyBus for a Low-Emission Vehicle (LEV) 299

References 300

14 Battery Lifecycle Information Management 301

14.1 Data Type of Power Battery 301

14.2 Vehicle Instrument Data Display 302

14.2.1 Battery Information Displayed on the Vehicle Instrument 303

14.2.2 Upgrade Based on a Traditional Instrument Panel 303

14.2.3 Design of the New Instrument Panel 304

14.3 Battery Data Transmission Mode 306

14.3.1 Hardware Implementation of Data Transmission 306

14.3.2 Control Flow of Data Transmission 307

14.3.3 Hierarchical Management of Power Battery Data 309

14.4 Information Concerning a Full-Power Battery Lifecycle 311

14.4.1 Database Structure of a Power Battery 311

14.4.2 Power Battery Data Volume Estimation 315

14.5 Storage and Analysis of Historical Information of a Battery 316

14.5.1 Necessity for Storage of Historical Information 316

14.5.2 Achievement of Historical Information Storage 317

14.5.3 Analysis and Processing of Historical Information 318

14.6 Battery Detection System Based on a Mobile Terminal 320

14.6.1 Server Program Design and Implementation 322

14.6.2 Design and Implementation of the Mobile Terminal 322

Reference 325

Part IV Case Studies 327

15 BMS for an E-Bike 329

15.1 Balancing 329

15.1.1 Passive Balancing 330

15.1.2 Active Charge Compensation 330

15.2 Battery Pack Design for an E-Bike 331

15.2.1 E-Bike Battery Pack Design Specifications 332

15.2.2 Testing 332

15.3 Methodology 333

15.4 Active Balancing Solutions 337

15.4.1 Structure of LTC3300 338

15.4.2 Discharging Procedure 338

15.4.3 Charging Process 339

15.5 Test Results 341

15.5.1 Measurements with Different Discharges 341

15.5.2 Comparison Between the Batteries 346

15.6 Possibility with Active Balancing 349

15.7 Results and Evaluation 349

Reference 351

16 BMS for a Fork-Lift 353

16.1 Lithium-Iron-Phosphate Batteries for Fork-Lifts 353

16.2 Battery Management Systems for Fork-Lifts 355

16.3 The LIONIC(r) Battery System for Truck Applications 356

16.4 Application 357

16.5 The Usable Energy Li-Ion Traction Batteries 359

Reference 361

17 BMS for a Minibus 363

17.1 Internal Resistance Analysis of a Power Battery System and Discharging Strategy Research of Vehicles 361

17.1.1 Internal Resistance Change Characteristic Research of a Power Battery 364

17.1.2 Internal Resistance Characteristic-Based Discharge Strategy 369

17.1.3 Research of a Charging Method for a Power Battery System Based on an Internal Resistance Characteristic 374

17.2 Consistency Evaluation Research of a Power Battery System 377

17.2.1 Analysis of a Battery Pack Maintenance Strategy and Performance Evaluation Index 377

17.2.2 Comparison of the Battery Pack Performance Evaluation Methods 378

17.2.3 Internal Resistance Characteristic-Based Consistency Evaluation Theory of the Battery Pack 379

17.2.4 Internal Resistance Characteristic-Based Consistency Evaluation of the Battery Pack 380

17.2.5 Internal Resistance Characteristic-Based Staged Consistency Evaluation Method for the Battery Pack 381

17.2.6 Internal Resistance Consistency Evaluation Test of the Battery Pack for a Pure Electric Vehicle 384

17.3 Safety Management and Protection of a Power Battery System 386

Index 389
Xiaojun Tan, Sun Yat-sen University, China, is a Professor and leads the Research Center of New Energy Vehicles at the School of Intelligent Systems Engineering, Sun Yat-sen University. He has nearly two decades' research experience in battery modeling, testing and diagnoses, and has spearheaded many industry partnerships.

Andrea Vezzini, Bern University of Applied Sciences, Switzerland is a Professor with more than two decades' experience in battery systems research and development. He leads the Energy Storage Research Centre (ESReC) at Bern University of Applied Sciences and has been involved through several spin-offs in the product development of customized battery system solutions for the industrial and automotive market.

Yuqian Fan, received his PhD. in Intelligent Transportation Engineering from Sun Yat-sen University, China. His research interests include intelligent control and optimization design for power battery systems, battery thermal management and thermal safety, and battery state of health prediction.

Neeta Khare, is a Director with Iveco Group. Dr. Khare acquired her doctoral degree in Intelligent Battery Monitoring from Banasthali University, India. Her core expertise is in aging algorithms of battery/ cell using AI and adaptive algorithms, Battery Pack, Battery Management System (BMS) development, and more.

You Xu, is an Associate Professor in Guangdong Polytechnic Normal University, China, where he has been engaged in power battery system, precision reverse equipment. Dr. You received his PhD. from Sun Yat-sen University. He has authored over 20 scientific publications, and his research interests include battery management for electrical vehicles.

Liangliang Wei, is an Associate Professor in Control Science and Engineering at Sun Yat-Sen University, China. Dr. Wei has authored over 20 scientific publications and received his PhD. in Electrical Engineering from the Wuhan University, China.