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.

• Biographies xiForeword by Professor Sun xiiiForeword by Professor Ouyang xvSeries Preface xviiPreface xix1 Introduction 11.1 Background 11.2 Electric Vehicle Fundamentals 21.3 Requirements for Battery Systems in Electric Vehicles 31.3.1 Range Per Charge 41.3.2 Acceleration Rate 101.3.3 Maximum Speed 111.4 Battery Systems 111.4.1 Introduction to Electrochemistry of Battery Cells 121.4.1.1 Ohmic Overvoltage Drop 141.4.1.2 Activation Overvoltage 141.4.1.3 Concentration Overvoltage 141.4.2 Lead–Acid Batteries 151.4.3 NiCd and NiMH Batteries 161.4.3.1 NiCd Batteries 161.4.3.2 NiMH Batteries 171.4.4 Lithium-Ion Batteries 181.4.5 Battery Performance Comparison 191.4.5.1 Nominal Voltage 201.4.5.2 Specific Energy and Energy Density 201.4.5.3 Capacity Efficiency and Energy Efficiency 201.4.5.4 Specific Power and Power Density 201.4.5.5 Self-discharge 211.4.5.6 Cycle Life 211.4.5.7 Temperature Operation Range 211.5 Key Battery Management Technologies 211.5.1 Battery Modeling 211.5.2 Battery States Estimation 231.5.3 Battery Charging 241.5.4 Battery Balancing 251.6 Battery Management Systems 251.6.1 Hardware of BMS 261.6.2 Software of BMS 261.6.3 Centralized BMS 271.6.4 Distributed BMS 281.7 Summary 28References 282 BatteryModeling 312.1 Background 312.2 Electrochemical Models 312.3 Black Box Models 332.4 Equivalent Circuit Models 342.4.1 General n-RC Model 352.4.2 Models with Different Numbers of RC Networks 352.4.2.1 Rint Model 352.4.2.2 Thevenin Model 362.4.2.3 Dual Polarization Model 372.4.2.4 n-RC Model 382.4.3 Open Circuit Voltage 392.4.4 Polarization Characteristics 422.5 Experiments 432.6 Parameter Identification Methods 472.6.1 Offline Parameter Identification Method 472.6.2 Online Parameter Identification Method 502.7 Case Study 512.7.1 Testing Data 512.7.2 Case One – OFFPIM Application 512.7.3 Case Two – ONPIM Application 542.7.4 Discussions 562.8 Model Uncertainties 572.8.1 Battery Aging 572.8.2 Battery Type 592.8.3 Battery Temperature 612.9 Other Battery Models 622.10 Summary 64References 643 Battery State of Charge and State of Energy Estimation 673.1 Background 673.2 Classification 673.2.1 Look-Up-Table-Based Method 673.2.2 Ampere-Hour Integral Method 683.2.3 Data-Driven Estimation Methods 693.2.4 Model-Based Estimation Methods 703.3 Model-Based SOC Estimation Method with Constant Model Parameters 713.3.1 Discrete-Time Realization Algorithm 713.3.2 Extended Kalman Filter 723.3.2.1 Selection of Correction Coefficients 733.3.2.2 SOC Estimation Based on EKF 733.3.3 SOC Estimation Based on HIF 753.3.4 Case Study 773.3.5 Influence of Uncertainties on SOC Estimation 783.3.5.1 Initial SOC Value 793.3.5.2 Dynamic Working Condition 803.3.5.3 Battery Temperature 813.4 Model-Based SOC Estimation Method with Identified Model Parameters in Real-Time 843.4.1 Real-Time Modeling Process 843.4.2 Case Study 863.5 Model-Based SOE Estimation Method with Identified Model Parameters in Real-Time 863.5.1 SOE Definition 873.5.2 State Space Modeling 873.5.3 Case Study 883.5.4 Influence of Uncertainties on SOE Estimation 893.5.4.1 Initial SOE Value 893.5.4.2 DynamicWorking Condition 903.5.4.3 Battery Temperature 903.6 Summary 92References 924 Battery State of Health Estimation 954.1 Background 954.2 Experimental Methods 954.2.1 Direct Measurement Methods 964.2.1.1 Capacity or Energy Measurement 964.2.1.2 Internal Resistance Measurement 964.2.1.3 Impedance Measurement 974.2.1.4 Cycle Number Counting 974.2.1.5 Destructive Methods 984.2.2 Indirect Analysis Methods 984.2.2.1 Voltage Trajectory Method 984.2.2.2 ICA Method 1004.2.2.3 DVA Method 1024.3 Model-Based Methods 1044.3.1 Adaptive State Estimation Methods 1044.3.2 Data-Driven Methods 1114.3.2.1 Empirical and Fitting Methods 1124.3.2.2 Response Surface-Based Optimization Algorithms 1124.3.2.3 Sample Entropy Methods 1154.4 Joint Estimation Method 1164.4.1 Relationship between SOC and Capacity 1164.4.2 Case Study 1174.5 Dual Estimation Method 1184.5.1 Implementation with the AEKF Algorithm 1184.5.2 SOC–SOH Estimation 1224.5.3 Case Study 1254.6 Summary 128References 1295 Battery State of Power Estimation 1315.1 Background 1315.2 Instantaneous SOP Estimation Methods 1315.2.1 HPPC Method 1325.2.2 SOC-Limited Method 1335.2.3 Voltage-Limited Method 1335.2.4 MCD Method 1345.2.5 Case Study 1365.3 Continuous SOP Estimation Method 1395.3.1 Continuous Peak Current Estimation 1395.3.2 Continuous SOP Estimation 1405.3.3 Influences of Battery States and Parameters on SOP Estimation 1415.3.3.1 Uncertainty of SOC 1415.3.3.2 Case Study 1425.3.3.3 Uncertainty of Model Parameters 1465.3.3.4 Case Study 1485.3.3.5 Uncertainty of SOH 1505.4 Summary 154References 1546 Battery Charging 1556.1 Background 1556.2 Basic Terms for Evaluating Charging Performances 1576.2.1 Cell and Pack 1576.2.2 Nominal Ampere-Hour Capacity 1576.2.3 C-rate 1576.2.4 Cut-off Voltage for Discharge or Charge 1576.2.5 Cut-off Current 1576.2.6 State of Charge 1586.2.7 State of Health 1586.2.8 Cycle Life 1586.2.9 Charge Acceptance 1586.2.10 Ampere-Hour Efficiency 1586.2.11 Ampere-Hour Charging Factor 1596.2.12 Energy Efficiency 1596.2.13 Watt-Hour Charging Factor 1596.2.14 Trickle Charging 1596.3 Charging Algorithms for Li-Ion Batteries 1596.3.1 Constant Current and Constant Voltage Charging 1606.3.2 Multistep Constant Current Charging 1656.3.3 Two-Step Constant Current Constant Voltage Charging 1686.3.4 Constant Voltage Constant Current Constant Voltage Charging 1696.3.5 Pulse Charging 1696.3.6 Charging Termination 1726.3.7 Comparison of Charging Algorithms for Lithium-Ion Batteries 1726.4 Optimal Charging Current Profiles for Lithium-Ion Batteries 1736.4.1 Energy Loss Modeling 1746.4.2 Minimization of Energy Loss 1756.5 Lithium Titanate Oxide Battery with Extreme Fast Charging Capability 1776.6 Summary 179References 1807 Battery Balancing 1837.1 Background 1837.2 Battery Sorting 1847.2.1 Battery Sorting Based on Capacity and Internal Resistance 1847.2.2 Battery Sorting Based on a Self-organizing Map 1857.3 Battery Passive Balancing 1897.3.1 Fixed Shunt Resistor 1897.3.2 Switched Shunt Resistor 1897.3.3 Shunt Transistor 1907.4 Battery Active Balancing 1917.4.1 Balancing Criterion 1917.4.2 Balancing Control 1937.4.3 Balancing Circuits 1937.4.3.1 Cell to Cell 1947.4.3.2 Cell to Pack 1967.4.3.3 Pack to Cell 1997.4.3.4 Cell to Energy Storage Tank to Cell 2017.4.3.5 Cell to Pack to Cell 2017.5 Battery Active Balancing Systems 2037.5.1 Active Balancing System Based on the SOC as a Balancing Criterion 2047.5.1.1 Battery Balancing Criterion 2047.5.1.2 Battery Balancing Circuit 2087.5.1.3 Battery Balancing Control 2087.5.1.4 Experimental Results 2087.5.2 Active Balancing System Based on FL Controller 2127.5.2.1 Balancing Principle 2157.5.2.2 Design of FL Controller 2157.5.2.3 Adaptability of FL Controller 2207.5.2.4 Battery Balancing Criterion 2227.5.2.5 Experimental Results 2227.6 Summary 227References 2278 Battery Management Systems in Electric Vehicles 2318.1 Background 2318.2 Battery Management Systems 2318.2.1 Battery Parameter Acquisition Module 2328.2.2 Battery System Balancing Module 2338.2.3 Battery Information Management Module 2368.2.4 Thermal Management Module 2378.3 Typical Structure of BMSs 2388.3.1 Centralized BMS 2388.3.2 Distributed BMS 2398.4 Representative Products 2398.4.1 E-Power BMS 2398.4.2 Klclear BMS 2408.4.3 Tesla BMS 2418.4.4 ICs for BMS Design 2428.5 Key Points of BMSs in Future Generation 2428.5.1 Self-Heating Management 2438.5.2 Safety Management 2448.5.3 Cloud Computing 2448.6 Summary 247References 247Index 249

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.

• Biographies xiForeword by Professor Sun xiiiForeword by Professor Ouyang xvSeries Preface xviiPreface xix1 Introduction 11.1 Background 11.2 Electric Vehicle Fundamentals 21.3 Requirements for Battery Systems in Electric Vehicles 31.3.1 Range Per Charge 41.3.2 Acceleration Rate 101.3.3 Maximum Speed 111.4 Battery Systems 111.4.1 Introduction to Electrochemistry of Battery Cells 121.4.1.1 Ohmic Overvoltage Drop 141.4.1.2 Activation Overvoltage 141.4.1.3 Concentration Overvoltage 141.4.2 Lead–Acid Batteries 151.4.3 NiCd and NiMH Batteries 161.4.3.1 NiCd Batteries 161.4.3.2 NiMH Batteries 171.4.4 Lithium-Ion Batteries 181.4.5 Battery Performance Comparison 191.4.5.1 Nominal Voltage 201.4.5.2 Specific Energy and Energy Density 201.4.5.3 Capacity Efficiency and Energy Efficiency 201.4.5.4 Specific Power and Power Density 201.4.5.5 Self-discharge 211.4.5.6 Cycle Life 211.4.5.7 Temperature Operation Range 211.5 Key Battery Management Technologies 211.5.1 Battery Modeling 211.5.2 Battery States Estimation 231.5.3 Battery Charging 241.5.4 Battery Balancing 251.6 Battery Management Systems 251.6.1 Hardware of BMS 261.6.2 Software of BMS 261.6.3 Centralized BMS 271.6.4 Distributed BMS 281.7 Summary 28References 282 BatteryModeling 312.1 Background 312.2 Electrochemical Models 312.3 Black Box Models 332.4 Equivalent Circuit Models 342.4.1 General n-RC Model 352.4.2 Models with Different Numbers of RC Networks 352.4.2.1 Rint Model 352.4.2.2 Thevenin Model 362.4.2.3 Dual Polarization Model 372.4.2.4 n-RC Model 382.4.3 Open Circuit Voltage 392.4.4 Polarization Characteristics 422.5 Experiments 432.6 Parameter Identification Methods 472.6.1 Offline Parameter Identification Method 472.6.2 Online Parameter Identification Method 502.7 Case Study 512.7.1 Testing Data 512.7.2 Case One – OFFPIM Application 512.7.3 Case Two – ONPIM Application 542.7.4 Discussions 562.8 Model Uncertainties 572.8.1 Battery Aging 572.8.2 Battery Type 592.8.3 Battery Temperature 612.9 Other Battery Models 622.10 Summary 64References 643 Battery State of Charge and State of Energy Estimation 673.1 Background 673.2 Classification 673.2.1 Look-Up-Table-Based Method 673.2.2 Ampere-Hour Integral Method 683.2.3 Data-Driven Estimation Methods 693.2.4 Model-Based Estimation Methods 703.3 Model-Based SOC Estimation Method with Constant Model Parameters 713.3.1 Discrete-Time Realization Algorithm 713.3.2 Extended Kalman Filter 723.3.2.1 Selection of Correction Coefficients 733.3.2.2 SOC Estimation Based on EKF 733.3.3 SOC Estimation Based on HIF 753.3.4 Case Study 773.3.5 Influence of Uncertainties on SOC Estimation 783.3.5.1 Initial SOC Value 793.3.5.2 Dynamic Working Condition 803.3.5.3 Battery Temperature 813.4 Model-Based SOC Estimation Method with Identified Model Parameters in Real-Time 843.4.1 Real-Time Modeling Process 843.4.2 Case Study 863.5 Model-Based SOE Estimation Method with Identified Model Parameters in Real-Time 863.5.1 SOE Definition 873.5.2 State Space Modeling 873.5.3 Case Study 883.5.4 Influence of Uncertainties on SOE Estimation 893.5.4.1 Initial SOE Value 893.5.4.2 DynamicWorking Condition 903.5.4.3 Battery Temperature 903.6 Summary 92References 924 Battery State of Health Estimation 954.1 Background 954.2 Experimental Methods 954.2.1 Direct Measurement Methods 964.2.1.1 Capacity or Energy Measurement 964.2.1.2 Internal Resistance Measurement 964.2.1.3 Impedance Measurement 974.2.1.4 Cycle Number Counting 974.2.1.5 Destructive Methods 984.2.2 Indirect Analysis Methods 984.2.2.1 Voltage Trajectory Method 984.2.2.2 ICA Method 1004.2.2.3 DVA Method 1024.3 Model-Based Methods 1044.3.1 Adaptive State Estimation Methods 1044.3.2 Data-Driven Methods 1114.3.2.1 Empirical and Fitting Methods 1124.3.2.2 Response Surface-Based Optimization Algorithms 1124.3.2.3 Sample Entropy Methods 1154.4 Joint Estimation Method 1164.4.1 Relationship between SOC and Capacity 1164.4.2 Case Study 1174.5 Dual Estimation Method 1184.5.1 Implementation with the AEKF Algorithm 1184.5.2 SOC–SOH Estimation 1224.5.3 Case Study 1254.6 Summary 128References 1295 Battery State of Power Estimation 1315.1 Background 1315.2 Instantaneous SOP Estimation Methods 1315.2.1 HPPC Method 1325.2.2 SOC-Limited Method 1335.2.3 Voltage-Limited Method 1335.2.4 MCD Method 1345.2.5 Case Study 1365.3 Continuous SOP Estimation Method 1395.3.1 Continuous Peak Current Estimation 1395.3.2 Continuous SOP Estimation 1405.3.3 Influences of Battery States and Parameters on SOP Estimation 1415.3.3.1 Uncertainty of SOC 1415.3.3.2 Case Study 1425.3.3.3 Uncertainty of Model Parameters 1465.3.3.4 Case Study 1485.3.3.5 Uncertainty of SOH 1505.4 Summary 154References 1546 Battery Charging 1556.1 Background 1556.2 Basic Terms for Evaluating Charging Performances 1576.2.1 Cell and Pack 1576.2.2 Nominal Ampere-Hour Capacity 1576.2.3 C-rate 1576.2.4 Cut-off Voltage for Discharge or Charge 1576.2.5 Cut-off Current 1576.2.6 State of Charge 1586.2.7 State of Health 1586.2.8 Cycle Life 1586.2.9 Charge Acceptance 1586.2.10 Ampere-Hour Efficiency 1586.2.11 Ampere-Hour Charging Factor 1596.2.12 Energy Efficiency 1596.2.13 Watt-Hour Charging Factor 1596.2.14 Trickle Charging 1596.3 Charging Algorithms for Li-Ion Batteries 1596.3.1 Constant Current and Constant Voltage Charging 1606.3.2 Multistep Constant Current Charging 1656.3.3 Two-Step Constant Current Constant Voltage Charging 1686.3.4 Constant Voltage Constant Current Constant Voltage Charging 1696.3.5 Pulse Charging 1696.3.6 Charging Termination 1726.3.7 Comparison of Charging Algorithms for Lithium-Ion Batteries 1726.4 Optimal Charging Current Profiles for Lithium-Ion Batteries 1736.4.1 Energy Loss Modeling 1746.4.2 Minimization of Energy Loss 1756.5 Lithium Titanate Oxide Battery with Extreme Fast Charging Capability 1776.6 Summary 179References 1807 Battery Balancing 1837.1 Background 1837.2 Battery Sorting 1847.2.1 Battery Sorting Based on Capacity and Internal Resistance 1847.2.2 Battery Sorting Based on a Self-organizing Map 1857.3 Battery Passive Balancing 1897.3.1 Fixed Shunt Resistor 1897.3.2 Switched Shunt Resistor 1897.3.3 Shunt Transistor 1907.4 Battery Active Balancing 1917.4.1 Balancing Criterion 1917.4.2 Balancing Control 1937.4.3 Balancing Circuits 1937.4.3.1 Cell to Cell 1947.4.3.2 Cell to Pack 1967.4.3.3 Pack to Cell 1997.4.3.4 Cell to Energy Storage Tank to Cell 2017.4.3.5 Cell to Pack to Cell 2017.5 Battery Active Balancing Systems 2037.5.1 Active Balancing System Based on the SOC as a Balancing Criterion 2047.5.1.1 Battery Balancing Criterion 2047.5.1.2 Battery Balancing Circuit 2087.5.1.3 Battery Balancing Control 2087.5.1.4 Experimental Results 2087.5.2 Active Balancing System Based on FL Controller 2127.5.2.1 Balancing Principle 2157.5.2.2 Design of FL Controller 2157.5.2.3 Adaptability of FL Controller 2207.5.2.4 Battery Balancing Criterion 2227.5.2.5 Experimental Results 2227.6 Summary 227References 2278 Battery Management Systems in Electric Vehicles 2318.1 Background 2318.2 Battery Management Systems 2318.2.1 Battery Parameter Acquisition Module 2328.2.2 Battery System Balancing Module 2338.2.3 Battery Information Management Module 2368.2.4 Thermal Management Module 2378.3 Typical Structure of BMSs 2388.3.1 Centralized BMS 2388.3.2 Distributed BMS 2398.4 Representative Products 2398.4.1 E-Power BMS 2398.4.2 Klclear BMS 2408.4.3 Tesla BMS 2418.4.4 ICs for BMS Design 2428.5 Key Points of BMSs in Future Generation 2428.5.1 Self-Heating Management 2438.5.2 Safety Management 2448.5.3 Cloud Computing 2448.6 Summary 247References 247Index 249

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.

• Biographies xiForeword by Professor Sun xiiiForeword by Professor Ouyang xvSeries Preface xviiPreface xix1 Introduction 11.1 Background 11.2 Electric Vehicle Fundamentals 21.3 Requirements for Battery Systems in Electric Vehicles 31.3.1 Range Per Charge 41.3.2 Acceleration Rate 101.3.3 Maximum Speed 111.4 Battery Systems 111.4.1 Introduction to Electrochemistry of Battery Cells 121.4.1.1 Ohmic Overvoltage Drop 141.4.1.2 Activation Overvoltage 141.4.1.3 Concentration Overvoltage 141.4.2 Lead–Acid Batteries 151.4.3 NiCd and NiMH Batteries 161.4.3.1 NiCd Batteries 161.4.3.2 NiMH Batteries 171.4.4 Lithium-Ion Batteries 181.4.5 Battery Performance Comparison 191.4.5.1 Nominal Voltage 201.4.5.2 Specific Energy and Energy Density 201.4.5.3 Capacity Efficiency and Energy Efficiency 201.4.5.4 Specific Power and Power Density 201.4.5.5 Self-discharge 211.4.5.6 Cycle Life 211.4.5.7 Temperature Operation Range 211.5 Key Battery Management Technologies 211.5.1 Battery Modeling 211.5.2 Battery States Estimation 231.5.3 Battery Charging 241.5.4 Battery Balancing 251.6 Battery Management Systems 251.6.1 Hardware of BMS 261.6.2 Software of BMS 261.6.3 Centralized BMS 271.6.4 Distributed BMS 281.7 Summary 28References 282 BatteryModeling 312.1 Background 312.2 Electrochemical Models 312.3 Black Box Models 332.4 Equivalent Circuit Models 342.4.1 General n-RC Model 352.4.2 Models with Different Numbers of RC Networks 352.4.2.1 Rint Model 352.4.2.2 Thevenin Model 362.4.2.3 Dual Polarization Model 372.4.2.4 n-RC Model 382.4.3 Open Circuit Voltage 392.4.4 Polarization Characteristics 422.5 Experiments 432.6 Parameter Identification Methods 472.6.1 Offline Parameter Identification Method 472.6.2 Online Parameter Identification Method 502.7 Case Study 512.7.1 Testing Data 512.7.2 Case One – OFFPIM Application 512.7.3 Case Two – ONPIM Application 542.7.4 Discussions 562.8 Model Uncertainties 572.8.1 Battery Aging 572.8.2 Battery Type 592.8.3 Battery Temperature 612.9 Other Battery Models 622.10 Summary 64References 643 Battery State of Charge and State of Energy Estimation 673.1 Background 673.2 Classification 673.2.1 Look-Up-Table-Based Method 673.2.2 Ampere-Hour Integral Method 683.2.3 Data-Driven Estimation Methods 693.2.4 Model-Based Estimation Methods 703.3 Model-Based SOC Estimation Method with Constant Model Parameters 713.3.1 Discrete-Time Realization Algorithm 713.3.2 Extended Kalman Filter 723.3.2.1 Selection of Correction Coefficients 733.3.2.2 SOC Estimation Based on EKF 733.3.3 SOC Estimation Based on HIF 753.3.4 Case Study 773.3.5 Influence of Uncertainties on SOC Estimation 783.3.5.1 Initial SOC Value 793.3.5.2 Dynamic Working Condition 803.3.5.3 Battery Temperature 813.4 Model-Based SOC Estimation Method with Identified Model Parameters in Real-Time 843.4.1 Real-Time Modeling Process 843.4.2 Case Study 863.5 Model-Based SOE Estimation Method with Identified Model Parameters in Real-Time 863.5.1 SOE Definition 873.5.2 State Space Modeling 873.5.3 Case Study 883.5.4 Influence of Uncertainties on SOE Estimation 893.5.4.1 Initial SOE Value 893.5.4.2 DynamicWorking Condition 903.5.4.3 Battery Temperature 903.6 Summary 92References 924 Battery State of Health Estimation 954.1 Background 954.2 Experimental Methods 954.2.1 Direct Measurement Methods 964.2.1.1 Capacity or Energy Measurement 964.2.1.2 Internal Resistance Measurement 964.2.1.3 Impedance Measurement 974.2.1.4 Cycle Number Counting 974.2.1.5 Destructive Methods 984.2.2 Indirect Analysis Methods 984.2.2.1 Voltage Trajectory Method 984.2.2.2 ICA Method 1004.2.2.3 DVA Method 1024.3 Model-Based Methods 1044.3.1 Adaptive State Estimation Methods 1044.3.2 Data-Driven Methods 1114.3.2.1 Empirical and Fitting Methods 1124.3.2.2 Response Surface-Based Optimization Algorithms 1124.3.2.3 Sample Entropy Methods 1154.4 Joint Estimation Method 1164.4.1 Relationship between SOC and Capacity 1164.4.2 Case Study 1174.5 Dual Estimation Method 1184.5.1 Implementation with the AEKF Algorithm 1184.5.2 SOC–SOH Estimation 1224.5.3 Case Study 1254.6 Summary 128References 1295 Battery State of Power Estimation 1315.1 Background 1315.2 Instantaneous SOP Estimation Methods 1315.2.1 HPPC Method 1325.2.2 SOC-Limited Method 1335.2.3 Voltage-Limited Method 1335.2.4 MCD Method 1345.2.5 Case Study 1365.3 Continuous SOP Estimation Method 1395.3.1 Continuous Peak Current Estimation 1395.3.2 Continuous SOP Estimation 1405.3.3 Influences of Battery States and Parameters on SOP Estimation 1415.3.3.1 Uncertainty of SOC 1415.3.3.2 Case Study 1425.3.3.3 Uncertainty of Model Parameters 1465.3.3.4 Case Study 1485.3.3.5 Uncertainty of SOH 1505.4 Summary 154References 1546 Battery Charging 1556.1 Background 1556.2 Basic Terms for Evaluating Charging Performances 1576.2.1 Cell and Pack 1576.2.2 Nominal Ampere-Hour Capacity 1576.2.3 C-rate 1576.2.4 Cut-off Voltage for Discharge or Charge 1576.2.5 Cut-off Current 1576.2.6 State of Charge 1586.2.7 State of Health 1586.2.8 Cycle Life 1586.2.9 Charge Acceptance 1586.2.10 Ampere-Hour Efficiency 1586.2.11 Ampere-Hour Charging Factor 1596.2.12 Energy Efficiency 1596.2.13 Watt-Hour Charging Factor 1596.2.14 Trickle Charging 1596.3 Charging Algorithms for Li-Ion Batteries 1596.3.1 Constant Current and Constant Voltage Charging 1606.3.2 Multistep Constant Current Charging 1656.3.3 Two-Step Constant Current Constant Voltage Charging 1686.3.4 Constant Voltage Constant Current Constant Voltage Charging 1696.3.5 Pulse Charging 1696.3.6 Charging Termination 1726.3.7 Comparison of Charging Algorithms for Lithium-Ion Batteries 1726.4 Optimal Charging Current Profiles for Lithium-Ion Batteries 1736.4.1 Energy Loss Modeling 1746.4.2 Minimization of Energy Loss 1756.5 Lithium Titanate Oxide Battery with Extreme Fast Charging Capability 1776.6 Summary 179References 1807 Battery Balancing 1837.1 Background 1837.2 Battery Sorting 1847.2.1 Battery Sorting Based on Capacity and Internal Resistance 1847.2.2 Battery Sorting Based on a Self-organizing Map 1857.3 Battery Passive Balancing 1897.3.1 Fixed Shunt Resistor 1897.3.2 Switched Shunt Resistor 1897.3.3 Shunt Transistor 1907.4 Battery Active Balancing 1917.4.1 Balancing Criterion 1917.4.2 Balancing Control 1937.4.3 Balancing Circuits 1937.4.3.1 Cell to Cell 1947.4.3.2 Cell to Pack 1967.4.3.3 Pack to Cell 1997.4.3.4 Cell to Energy Storage Tank to Cell 2017.4.3.5 Cell to Pack to Cell 2017.5 Battery Active Balancing Systems 2037.5.1 Active Balancing System Based on the SOC as a Balancing Criterion 2047.5.1.1 Battery Balancing Criterion 2047.5.1.2 Battery Balancing Circuit 2087.5.1.3 Battery Balancing Control 2087.5.1.4 Experimental Results 2087.5.2 Active Balancing System Based on FL Controller 2127.5.2.1 Balancing Principle 2157.5.2.2 Design of FL Controller 2157.5.2.3 Adaptability of FL Controller 2207.5.2.4 Battery Balancing Criterion 2227.5.2.5 Experimental Results 2227.6 Summary 227References 2278 Battery Management Systems in Electric Vehicles 2318.1 Background 2318.2 Battery Management Systems 2318.2.1 Battery Parameter Acquisition Module 2328.2.2 Battery System Balancing Module 2338.2.3 Battery Information Management Module 2368.2.4 Thermal Management Module 2378.3 Typical Structure of BMSs 2388.3.1 Centralized BMS 2388.3.2 Distributed BMS 2398.4 Representative Products 2398.4.1 E-Power BMS 2398.4.2 Klclear BMS 2408.4.3 Tesla BMS 2418.4.4 ICs for BMS Design 2428.5 Key Points of BMSs in Future Generation 2428.5.1 Self-Heating Management 2438.5.2 Safety Management 2448.5.3 Cloud Computing 2448.6 Summary 247References 247Index 249