Advanced battery management technologies for electric vehicles

Advanced battery management technologies for electric vehicles / Rui Xiong, Beijing Institute of Technology, China, Weixiang Shen, Swinburne University of Technology, Australia.

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 st...

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Bibliographic Details
Main Authors: Xiong, Rui (Author), Shen, Weixiang (Author)
Format: eBook
Language:English
Published: Hoboken, NJ : John Wiley & Sons, Inc., 2019.
Series:Automotive series (Wiley)
Subjects:
Electric vehicles > Batteries.
TECHNOLOGY & ENGINEERING > Engineering (General)
Electric vehicles > Batteries
Online Access:Click for online access
Table of 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 Batteries1.4.4 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 BMS1.7 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 Application2.7.3 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 Filter3.3.2.1 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

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