Advanced Battery Management Technologies for Electric Vehicles

 
 
Standards Information Network (Verlag)
  • 1. Auflage
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  • erschienen am 28. Dezember 2018
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  • 280 Seiten
 
E-Book | PDF mit Adobe-DRM | Systemvoraussetzungen
978-1-119-48167-6 (ISBN)
 
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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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.
  • 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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