| Description |
1 online resource (xxii, 257 pages) : illustrations (some color) |
| Series |
Automotive series |
|
Automotive series (Wiley)
|
| Bibliography |
Includes bibliographical references and index. |
| 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 |
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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 |
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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 |
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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 |
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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 |
| Summary |
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. |
| Subject |
Electric vehicles -- Batteries.
|
|
Electric vehicles -- Batteries |
| Added Author |
Shen, Weixiang, author.
|
| Other Form: |
Print version: Xiong, Rui. Advanced battery management technologies for electric vehicles. Hoboken, NJ : John Wiley & Sons, Inc., [2018] 9781119481645 (DLC) 2018044881 |
| ISBN |
1119481678 |
|
9781119481683 (electronic publication) |
|
1119481686 (electronic publication) |
|
9781119481652 (electronic book) |
|
1119481651 (electronic book) |
|
9781119481676 (electronic book) |
|
(hardcover) |
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