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Advanced Battery Management Technologies for Electric Vehicles.

By: Contributor(s): Material type: TextTextSeries: Automotive SeriesPublisher: Newark : John Wiley & Sons, Incorporated, 2019Copyright date: ©2019Edition: 1st edDescription: 1 online resource (281 pages)Content type:
  • text
Media type:
  • computer
Carrier type:
  • online resource
ISBN:
  • 9781119481676
Subject(s): Genre/Form: Additional physical formats: Print version:: Advanced Battery Management Technologies for Electric VehiclesDDC classification:
  • 629.25024
LOC classification:
  • TL220 .X566 2019
Online resources:
Contents:
Cover -- Title Page -- Copyright -- Contents -- Biographies -- Foreword by Professor Sun -- Foreword by Professor Ouyang -- Series Preface -- Preface -- Chapter 1 Introduction -- 1.1 Background -- 1.2 Electric Vehicle Fundamentals -- 1.3 Requirements for Battery Systems in Electric Vehicles -- 1.3.1 Range Per Charge -- 1.3.2 Acceleration Rate -- 1.3.3 Maximum Speed -- 1.4 Battery Systems -- 1.4.1 Introduction to Electrochemistry of Battery Cells -- 1.4.1.1 Ohmic Overvoltage Drop -- 1.4.1.2 Activation Overvoltage -- 1.4.1.3 Concentration Overvoltage -- 1.4.2 Lead-Acid Batteries -- 1.4.3 NiCd and NiMH Batteries -- 1.4.3.1 NiCd Batteries -- 1.4.3.2 NiMH Batteries -- 1.4.4 Lithium‐Ion Batteries -- 1.4.5 Battery Performance Comparison -- 1.4.5.1 Nominal Voltage -- 1.4.5.2 Specific Energy and Energy Density -- 1.4.5.3 Capacity Efficiency and Energy Efficiency -- 1.4.5.4 Specific Power and Power Density -- 1.4.5.5 Self‐discharge -- 1.4.5.6 Cycle Life -- 1.4.5.7 Temperature Operation Range -- 1.5 Key Battery Management Technologies -- 1.5.1 Battery Modeling -- 1.5.2 Battery States Estimation -- 1.5.3 Battery Charging -- 1.5.4 Battery Balancing -- 1.6 Battery Management Systems -- 1.6.1 Hardware of BMS -- 1.6.2 Software of BMS -- 1.6.3 Centralized BMS -- 1.6.4 Distributed BMS -- 1.7 Summary -- References -- Chapter 2 Battery Modeling -- 2.1 Background -- 2.2 Electrochemical Models -- 2.3 Black Box Models -- 2.4 Equivalent Circuit Models -- 2.4.1 General n‐RC Model -- 2.4.2 Models with Different Numbers of RC Networks -- 2.4.2.1 Rint Model -- 2.4.2.2 Thevenin Model -- 2.4.2.3 Dual Polarization Model -- 2.4.2.4 n‐RC Model -- 2.4.3 Open Circuit Voltage -- 2.4.4 Polarization Characteristics -- 2.5 Experiments -- 2.6 Parameter Identification Methods -- 2.6.1 Offline Parameter Identification Method -- 2.6.2 Online Parameter Identification Method.
2.7 Case Study -- 2.7.1 Testing Data -- 2.7.2 Case One - OFFPIM Application -- 2.7.3 Case Two - ONPIM Application -- 2.7.4 Discussions -- 2.8 Model Uncertainties -- 2.8.1 Battery Aging -- 2.8.2 Battery Type -- 2.8.3 Battery Temperature -- 2.9 Other Battery Models -- 2.10 Summary -- References -- Chapter 3 Battery State of Charge and State of Energy Estimation -- 3.1 Background -- 3.2 Classification -- 3.2.1 Look‐Up‐Table‐Based Method -- 3.2.2 Ampere‐Hour Integral Method -- 3.2.3 Data‐Driven Estimation Methods -- 3.2.4 Model‐Based Estimation Methods -- 3.3 Model‐Based SOC Estimation Method with Constant Model Parameters -- 3.3.1 Discrete‐Time Realization Algorithm -- 3.3.2 Extended Kalman Filter -- 3.3.2.1 Selection of Correction Coefficients -- 3.3.2.2 SOC Estimation Based on EKF -- 3.3.3 SOC Estimation Based on HIF -- 3.3.4 Case Study -- 3.3.5 Influence of Uncertainties on SOC Estimation -- 3.3.5.1 Initial SOC Value -- 3.3.5.2 Dynamic Working Condition -- 3.3.5.3 Battery Temperature -- 3.4 Model‐Based SOC Estimation Method with Identified Model Parameters in Real‐Time -- 3.4.1 Real‐Time Modeling Process -- 3.4.2 Case Study -- 3.5 Model‐Based SOE Estimation Method with Identified Model Parameters in Real‐Time -- 3.5.1 SOE Definition -- 3.5.2 State Space Modeling -- 3.5.3 Case Study -- 3.5.4 Influence of Uncertainties on SOE Estimation -- 3.5.4.1 Initial SOE Value -- 3.5.4.2 Dynamic Working Condition -- 3.5.4.3 Battery Temperature -- 3.6 Summary -- References -- Chapter 4 Battery State of Health Estimation -- 4.1 Background -- 4.2 Experimental Methods -- 4.2.1 Direct Measurement Methods -- 4.2.1.1 Capacity or Energy Measurement -- 4.2.1.2 Internal Resistance Measurement -- 4.2.1.3 Impedance Measurement -- 4.2.1.4 Cycle Number Counting -- 4.2.1.5 Destructive Methods -- 4.2.2 Indirect Analysis Methods -- 4.2.2.1 Voltage Trajectory Method.
4.2.2.2 ICA Method -- 4.2.2.3 DVA Method -- 4.3 Model‐Based Methods -- 4.3.1 Adaptive State Estimation Methods -- 4.3.2 Data‐Driven Methods -- 4.3.2.1 Empirical and Fitting Methods -- 4.3.2.2 Response Surface‐Based Optimization Algorithms -- 4.3.2.3 Sample Entropy Methods -- 4.4 Joint Estimation Method -- 4.4.1 Relationship Between SOC and Capacity -- 4.4.2 Case Study -- 4.5 Dual Estimation Method -- 4.5.1 Implementation with the AEKF Algorithm -- 4.5.2 SOC-SOH Estimation -- 4.5.3 Case Study -- 4.6 Summary -- References -- Chapter 5 Battery State of Power Estimation -- 5.1 Background -- 5.2 Instantaneous SOP Estimation Methods -- 5.2.1 HPPC Method -- 5.2.2 SOC‐Limited Method -- 5.2.3 Voltage‐Limited Method -- 5.2.4 MCD Method -- 5.2.5 Case Study -- 5.3 Continuous SOP Estimation Method -- 5.3.1 Continuous Peak Current Estimation -- 5.3.2 Continuous SOP Estimation -- 5.3.3 Influences of Battery States and Parameters on SOP Estimation -- 5.3.3.1 Uncertainty of SOC -- 5.3.3.2 Case Study -- 5.3.3.3 Uncertainty of Model Parameters -- 5.3.3.4 Case Study -- 5.3.3.5 Uncertainty of SOH -- 5.4 Summary -- References -- Chapter 6 Battery Charging -- 6.1 Background -- 6.2 Basic Terms for Evaluating Charging Performances -- 6.2.1 Cell and Pack -- 6.2.2 Nominal Ampere‐Hour Capacity -- 6.2.3 C‐rate -- 6.2.4 Cut‐off Voltage for Discharge or Charge -- 6.2.5 Cut‐off Current -- 6.2.6 State of Charge -- 6.2.7 State of Health -- 6.2.8 Cycle Life -- 6.2.9 Charge Acceptance -- 6.2.10 Ampere‐Hour Efficiency -- 6.2.11 Ampere‐Hour Charging Factor -- 6.2.12 Energy Efficiency -- 6.2.13 Watt‐Hour Charging Factor -- 6.2.14 Trickle Charging -- 6.3 Charging Algorithms for Li‐Ion Batteries -- 6.3.1 Constant Current and Constant Voltage Charging -- 6.3.2 Multistep Constant Current Charging -- 6.3.3 Two‐Step Constant Current Constant Voltage Charging.
6.3.4 Constant Voltage Constant Current Constant Voltage Charging -- 6.3.5 Pulse Charging -- 6.3.6 Charging Termination -- 6.3.7 Comparison of Charging Algorithms for Lithium‐Ion Batteries -- 6.4 Optimal Charging Current Profiles for Lithium‐Ion Batteries -- 6.4.1 Energy Loss Modeling -- 6.4.2 Minimization of Energy Loss -- 6.5 Lithium Titanate Oxide Battery with Extreme Fast Charging Capability -- 6.6 Summary -- References -- Chapter 7 Battery Balancing -- 7.1 Background -- 7.2 Battery Sorting -- 7.2.1 Battery Sorting Based on Capacity and Internal Resistance -- 7.2.2 Battery Sorting Based on a Self‐organizing Map -- 7.3 Battery Passive Balancing -- 7.3.1 Fixed Shunt Resistor -- 7.3.2 Switched Shunt Resistor -- 7.3.3 Shunt Transistor -- 7.4 Battery Active Balancing -- 7.4.1 Balancing Criterion -- 7.4.2 Balancing Control -- 7.4.3 Balancing Circuits -- 7.4.3.1 Cell to Cell -- 7.4.3.2 Cell to Pack -- 7.4.3.3 Pack to Cell -- 7.4.3.4 Cell to Energy Storage Tank to Cell -- 7.4.3.5 Cell to Pack to Cell -- 7.5 Battery Active Balancing Systems -- 7.5.1 Active Balancing System Based on the SOC as a Balancing Criterion -- 7.5.1.1 Battery Balancing Criterion -- 7.5.1.2 Battery Balancing Circuit -- 7.5.1.3 Battery Balancing Control -- 7.5.1.4 Experimental Results -- 7.5.2 Active Balancing System Based on FL Controller -- 7.5.2.1 Balancing Principle -- 7.5.2.2 Design of FL Controller -- 7.5.2.3 Adaptability of FL Controller -- 7.5.2.4 Battery Balancing Criterion -- 7.5.2.5 Experimental Results -- 7.6 Summary -- References -- Chapter 8 Battery Management Systems in Electric Vehicles -- 8.1 Background -- 8.2 Battery Management Systems -- 8.2.1 Battery Parameter Acquisition Module -- 8.2.2 Battery System Balancing Module -- 8.2.3 Battery Information Management Module -- 8.2.4 Thermal Management Module -- 8.3 Typical Structure of BMSs -- 8.3.1 Centralized BMS.
8.3.2 Distributed BMS -- 8.4 Representative Products -- 8.4.1 E‐Power BMS -- 8.4.2 Klclear BMS -- 8.4.3 Tesla BMS -- 8.4.4 ICs for BMS Design -- 8.5 Key Points of BMSs in Future Generation -- 8.5.1 Self‐Heating Management -- 8.5.2 Safety Management -- 8.5.3 Cloud Computing -- 8.6 Summary -- References -- Index -- EULA.
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Cover -- Title Page -- Copyright -- Contents -- Biographies -- Foreword by Professor Sun -- Foreword by Professor Ouyang -- Series Preface -- Preface -- Chapter 1 Introduction -- 1.1 Background -- 1.2 Electric Vehicle Fundamentals -- 1.3 Requirements for Battery Systems in Electric Vehicles -- 1.3.1 Range Per Charge -- 1.3.2 Acceleration Rate -- 1.3.3 Maximum Speed -- 1.4 Battery Systems -- 1.4.1 Introduction to Electrochemistry of Battery Cells -- 1.4.1.1 Ohmic Overvoltage Drop -- 1.4.1.2 Activation Overvoltage -- 1.4.1.3 Concentration Overvoltage -- 1.4.2 Lead-Acid Batteries -- 1.4.3 NiCd and NiMH Batteries -- 1.4.3.1 NiCd Batteries -- 1.4.3.2 NiMH Batteries -- 1.4.4 Lithium‐Ion Batteries -- 1.4.5 Battery Performance Comparison -- 1.4.5.1 Nominal Voltage -- 1.4.5.2 Specific Energy and Energy Density -- 1.4.5.3 Capacity Efficiency and Energy Efficiency -- 1.4.5.4 Specific Power and Power Density -- 1.4.5.5 Self‐discharge -- 1.4.5.6 Cycle Life -- 1.4.5.7 Temperature Operation Range -- 1.5 Key Battery Management Technologies -- 1.5.1 Battery Modeling -- 1.5.2 Battery States Estimation -- 1.5.3 Battery Charging -- 1.5.4 Battery Balancing -- 1.6 Battery Management Systems -- 1.6.1 Hardware of BMS -- 1.6.2 Software of BMS -- 1.6.3 Centralized BMS -- 1.6.4 Distributed BMS -- 1.7 Summary -- References -- Chapter 2 Battery Modeling -- 2.1 Background -- 2.2 Electrochemical Models -- 2.3 Black Box Models -- 2.4 Equivalent Circuit Models -- 2.4.1 General n‐RC Model -- 2.4.2 Models with Different Numbers of RC Networks -- 2.4.2.1 Rint Model -- 2.4.2.2 Thevenin Model -- 2.4.2.3 Dual Polarization Model -- 2.4.2.4 n‐RC Model -- 2.4.3 Open Circuit Voltage -- 2.4.4 Polarization Characteristics -- 2.5 Experiments -- 2.6 Parameter Identification Methods -- 2.6.1 Offline Parameter Identification Method -- 2.6.2 Online Parameter Identification Method.

2.7 Case Study -- 2.7.1 Testing Data -- 2.7.2 Case One - OFFPIM Application -- 2.7.3 Case Two - ONPIM Application -- 2.7.4 Discussions -- 2.8 Model Uncertainties -- 2.8.1 Battery Aging -- 2.8.2 Battery Type -- 2.8.3 Battery Temperature -- 2.9 Other Battery Models -- 2.10 Summary -- References -- Chapter 3 Battery State of Charge and State of Energy Estimation -- 3.1 Background -- 3.2 Classification -- 3.2.1 Look‐Up‐Table‐Based Method -- 3.2.2 Ampere‐Hour Integral Method -- 3.2.3 Data‐Driven Estimation Methods -- 3.2.4 Model‐Based Estimation Methods -- 3.3 Model‐Based SOC Estimation Method with Constant Model Parameters -- 3.3.1 Discrete‐Time Realization Algorithm -- 3.3.2 Extended Kalman Filter -- 3.3.2.1 Selection of Correction Coefficients -- 3.3.2.2 SOC Estimation Based on EKF -- 3.3.3 SOC Estimation Based on HIF -- 3.3.4 Case Study -- 3.3.5 Influence of Uncertainties on SOC Estimation -- 3.3.5.1 Initial SOC Value -- 3.3.5.2 Dynamic Working Condition -- 3.3.5.3 Battery Temperature -- 3.4 Model‐Based SOC Estimation Method with Identified Model Parameters in Real‐Time -- 3.4.1 Real‐Time Modeling Process -- 3.4.2 Case Study -- 3.5 Model‐Based SOE Estimation Method with Identified Model Parameters in Real‐Time -- 3.5.1 SOE Definition -- 3.5.2 State Space Modeling -- 3.5.3 Case Study -- 3.5.4 Influence of Uncertainties on SOE Estimation -- 3.5.4.1 Initial SOE Value -- 3.5.4.2 Dynamic Working Condition -- 3.5.4.3 Battery Temperature -- 3.6 Summary -- References -- Chapter 4 Battery State of Health Estimation -- 4.1 Background -- 4.2 Experimental Methods -- 4.2.1 Direct Measurement Methods -- 4.2.1.1 Capacity or Energy Measurement -- 4.2.1.2 Internal Resistance Measurement -- 4.2.1.3 Impedance Measurement -- 4.2.1.4 Cycle Number Counting -- 4.2.1.5 Destructive Methods -- 4.2.2 Indirect Analysis Methods -- 4.2.2.1 Voltage Trajectory Method.

4.2.2.2 ICA Method -- 4.2.2.3 DVA Method -- 4.3 Model‐Based Methods -- 4.3.1 Adaptive State Estimation Methods -- 4.3.2 Data‐Driven Methods -- 4.3.2.1 Empirical and Fitting Methods -- 4.3.2.2 Response Surface‐Based Optimization Algorithms -- 4.3.2.3 Sample Entropy Methods -- 4.4 Joint Estimation Method -- 4.4.1 Relationship Between SOC and Capacity -- 4.4.2 Case Study -- 4.5 Dual Estimation Method -- 4.5.1 Implementation with the AEKF Algorithm -- 4.5.2 SOC-SOH Estimation -- 4.5.3 Case Study -- 4.6 Summary -- References -- Chapter 5 Battery State of Power Estimation -- 5.1 Background -- 5.2 Instantaneous SOP Estimation Methods -- 5.2.1 HPPC Method -- 5.2.2 SOC‐Limited Method -- 5.2.3 Voltage‐Limited Method -- 5.2.4 MCD Method -- 5.2.5 Case Study -- 5.3 Continuous SOP Estimation Method -- 5.3.1 Continuous Peak Current Estimation -- 5.3.2 Continuous SOP Estimation -- 5.3.3 Influences of Battery States and Parameters on SOP Estimation -- 5.3.3.1 Uncertainty of SOC -- 5.3.3.2 Case Study -- 5.3.3.3 Uncertainty of Model Parameters -- 5.3.3.4 Case Study -- 5.3.3.5 Uncertainty of SOH -- 5.4 Summary -- References -- Chapter 6 Battery Charging -- 6.1 Background -- 6.2 Basic Terms for Evaluating Charging Performances -- 6.2.1 Cell and Pack -- 6.2.2 Nominal Ampere‐Hour Capacity -- 6.2.3 C‐rate -- 6.2.4 Cut‐off Voltage for Discharge or Charge -- 6.2.5 Cut‐off Current -- 6.2.6 State of Charge -- 6.2.7 State of Health -- 6.2.8 Cycle Life -- 6.2.9 Charge Acceptance -- 6.2.10 Ampere‐Hour Efficiency -- 6.2.11 Ampere‐Hour Charging Factor -- 6.2.12 Energy Efficiency -- 6.2.13 Watt‐Hour Charging Factor -- 6.2.14 Trickle Charging -- 6.3 Charging Algorithms for Li‐Ion Batteries -- 6.3.1 Constant Current and Constant Voltage Charging -- 6.3.2 Multistep Constant Current Charging -- 6.3.3 Two‐Step Constant Current Constant Voltage Charging.

6.3.4 Constant Voltage Constant Current Constant Voltage Charging -- 6.3.5 Pulse Charging -- 6.3.6 Charging Termination -- 6.3.7 Comparison of Charging Algorithms for Lithium‐Ion Batteries -- 6.4 Optimal Charging Current Profiles for Lithium‐Ion Batteries -- 6.4.1 Energy Loss Modeling -- 6.4.2 Minimization of Energy Loss -- 6.5 Lithium Titanate Oxide Battery with Extreme Fast Charging Capability -- 6.6 Summary -- References -- Chapter 7 Battery Balancing -- 7.1 Background -- 7.2 Battery Sorting -- 7.2.1 Battery Sorting Based on Capacity and Internal Resistance -- 7.2.2 Battery Sorting Based on a Self‐organizing Map -- 7.3 Battery Passive Balancing -- 7.3.1 Fixed Shunt Resistor -- 7.3.2 Switched Shunt Resistor -- 7.3.3 Shunt Transistor -- 7.4 Battery Active Balancing -- 7.4.1 Balancing Criterion -- 7.4.2 Balancing Control -- 7.4.3 Balancing Circuits -- 7.4.3.1 Cell to Cell -- 7.4.3.2 Cell to Pack -- 7.4.3.3 Pack to Cell -- 7.4.3.4 Cell to Energy Storage Tank to Cell -- 7.4.3.5 Cell to Pack to Cell -- 7.5 Battery Active Balancing Systems -- 7.5.1 Active Balancing System Based on the SOC as a Balancing Criterion -- 7.5.1.1 Battery Balancing Criterion -- 7.5.1.2 Battery Balancing Circuit -- 7.5.1.3 Battery Balancing Control -- 7.5.1.4 Experimental Results -- 7.5.2 Active Balancing System Based on FL Controller -- 7.5.2.1 Balancing Principle -- 7.5.2.2 Design of FL Controller -- 7.5.2.3 Adaptability of FL Controller -- 7.5.2.4 Battery Balancing Criterion -- 7.5.2.5 Experimental Results -- 7.6 Summary -- References -- Chapter 8 Battery Management Systems in Electric Vehicles -- 8.1 Background -- 8.2 Battery Management Systems -- 8.2.1 Battery Parameter Acquisition Module -- 8.2.2 Battery System Balancing Module -- 8.2.3 Battery Information Management Module -- 8.2.4 Thermal Management Module -- 8.3 Typical Structure of BMSs -- 8.3.1 Centralized BMS.

8.3.2 Distributed BMS -- 8.4 Representative Products -- 8.4.1 E‐Power BMS -- 8.4.2 Klclear BMS -- 8.4.3 Tesla BMS -- 8.4.4 ICs for BMS Design -- 8.5 Key Points of BMSs in Future Generation -- 8.5.1 Self‐Heating Management -- 8.5.2 Safety Management -- 8.5.3 Cloud Computing -- 8.6 Summary -- References -- Index -- EULA.

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Electronic reproduction. Ann Arbor, Michigan : ProQuest Ebook Central, 2024. Available via World Wide Web. Access may be limited to ProQuest Ebook Central affiliated libraries.

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