Advances in Lithium-Ion Batteries for Electric Vehicles
Degradation Mechanism, Health Estimation, and Lifetime Prediction
- 1st Edition - February 15, 2024
- Latest edition
- Authors: Haifeng Dai, Jiangong Zhu
- Language: English
Advances in Lithium-Ion Batteries for Electric Vehicles: Degradation Mechanism, Health Estimation, and Lifetime Prediction examines the electrochemical nature of lithium-i… Read more
Description
Description
Advances in Lithium-Ion Batteries for Electric Vehicles: Degradation Mechanism, Health Estimation, and Lifetime Prediction examines the electrochemical nature of lithium-ion batteries, including battery degradation mechanisms and how to manage the battery state of health (SOH) to meet the demand for sustainable development of electric vehicles. With extensive case studies, methods and applications, the book provides practical, step-by-step guidance on battery tests, degradation mechanisms, and modeling and management strategies. The book begins with an overview of Li-ion battery aging and battery aging tests before discussing battery degradation mechanisms and methods for analysis.
Further methods are then presented for battery state of health estimation and battery lifetime prediction, providing a range of case studies and techniques. The book concludes with a thorough examination of lifetime management strategies for electric vehicles, making it an essential resource for students, researchers, and engineers needing a range of approaches to tackle battery degradation in electric vehicles.
Key features
Key features
- Evaluates the cause of battery degradation from the material level to the cell level
- Explains key battery basic lifetime test methods and strategies
- Presents advanced technologies of battery state of health estimation
Readership
Readership
Researchers, students and engineers interested in the areas of lithium-ion batteries, energy storage, and automotive engineering
Table of contents
Table of contents
1. Overview of Li-ion Battery Aging
1.1 Requirements for Batteries in Electric Vehicles
1.2 Different Types of Aging
1.2.1 Calendar Life
1.2.2 Cycling Life
1.2.3 Dynamic Working in Electric Vehicles
1.2 Performance of Aging Battery
1.2.1 Electrical Characteristics
1.2.2 Thermal Characteristics
1.2.3 Mechanical Properties
1.3 State of Health (SoH)
1.3.1 Battery SoH Definition Based on Capacity Fade
1.3.2 Battery SoH Definition Based on Power Fade
1.4 Remaining Useful Life (RUL)
1.5 Conclusions
1.6 Reference
2. Battery Aging Test
2.1 Testing Standards
2.1.1 Electric Vehicle (Industry) Standard
2.1.2 Laboratory Test Standard
2.2 Test Platform
2.2.1 Test System in Laboratory
2.2.2 Charge and Discharge Test Equipment
2.2.3 Environment Simulation Equipment
2.2.4 Electrochemical Workstation
2.2.5 Data Acquisition Devices
2.2.6 Others
2.3 Case Study
2.3.1 Cycling Life Test of Dynamic Condition in Electric Vehicles
2.3.2 Cycling Life Test of Low-temperature Charge
2.4 Conclusions
2.5 References
3. Battery Degradation Mechanism and Analysis Method
3.1 Principles of Battery
3.1.1 Working Principles
3.1.2 Basic Structure
3.2 Degradation Mechanisms
3.2.1 Anode
3.2.2 Cathode
3.2.3 Electrolyte
3.2.4 Separator
3.2.5 Current Collector
3.2.6 Nonlinear Degradation
3.3 Influence of working conditions in Electric Vehicles
3.3.1 Main factors from working conditions in Electric Vehicles
3.3.2 Charging Rate
3.3.3 Charging Cutoff Voltage
3.3.4 Depth of Discharge
3.3.5 Temperature
3.4 Analysis Method
3.4.1 Cell Level
3.4.2 Electrode Level
3.4.3 Material Level
3.5 Conclusions
3.6 Reference
4. Battery State of Health Estimation
4.1 Battery State of Health Estimation based on Feature
4.1.1 Charge Curve-Based Estimation Method
4.1.2 Relaxation Voltage-Based Estimation Method
4.1.3 ICA/DVA-Based Estimation Method
4.1.4 AC Impedance Spectrum-Based Estimation Method
4.1.5 Case Study
4.2 Battery State of Health Estimation based on Model and Algorithm
4.2.1 Capacity Estimation-Based Estimation Method
4.2.2 EIS Calculation-Based Estimation Method
4.2.3 Case Study
4.3 Conclusions
4.4 References
5. Battery Lifetime Prediction Methods
5.1 Model-based Approach
5.1.1 Empirical Model
5.1.2 Equivalent Circuit Method
5.1.3 Filter Method
5.1.4 Fuzzy System Method
5.1.5 Semi-Empirical Model
5.1.6 Case Study
5.2 Data driven methods
5.2.1 Black-Box Modeling
5.2.2 Data Acquisition
5.2.3 Trained and Advanced Algorithms
5.2.4 Data-Driven Calibration Process
5.2.5 Artificial Neutral Networks
5.2.6 Relevance Vector Machine
5.2.7 Grey Theory
5.2.8 Case Study
5.3 Conclusions
5.4 References
6. Lifetime Management of Battery Degradation for Electric Vehicles
6.1 Battery Multi-Layer Management Strategy
6.1.1 Physical Layer
6.1.2 Core Layer
6.1.3 Management Layer
6.2 Battery modeling and state estimation battery lifetime model
6.2.1 Electrochemical-thermal-aging model
6.2.2 Low Temperature Charging Capacity Fade Model
6.2.3 Life Model of Series Battery Pack
6.2.4 Life Model of Parallel Battery Pack
6.3 Optimization of Charging Strategy Based on Lifetime Model
6.3.1 Normal Temperature Charging
6.3.2 Low Temperature Charging
6.4 Lifetime Balance Strategy of Battery Pack
6.4.1 Life Balance Strategy for Maximizing of Single Discharge Capacity
6.4.2 Power Balance Strategy for Maximizing Total Discharge Capacity
6.4.3 Energy Equalization Strategy for Maximizing Total Discharge Energy
6.5 Conclusions
6.6 References
1.1 Requirements for Batteries in Electric Vehicles
1.2 Different Types of Aging
1.2.1 Calendar Life
1.2.2 Cycling Life
1.2.3 Dynamic Working in Electric Vehicles
1.2 Performance of Aging Battery
1.2.1 Electrical Characteristics
1.2.2 Thermal Characteristics
1.2.3 Mechanical Properties
1.3 State of Health (SoH)
1.3.1 Battery SoH Definition Based on Capacity Fade
1.3.2 Battery SoH Definition Based on Power Fade
1.4 Remaining Useful Life (RUL)
1.5 Conclusions
1.6 Reference
2. Battery Aging Test
2.1 Testing Standards
2.1.1 Electric Vehicle (Industry) Standard
2.1.2 Laboratory Test Standard
2.2 Test Platform
2.2.1 Test System in Laboratory
2.2.2 Charge and Discharge Test Equipment
2.2.3 Environment Simulation Equipment
2.2.4 Electrochemical Workstation
2.2.5 Data Acquisition Devices
2.2.6 Others
2.3 Case Study
2.3.1 Cycling Life Test of Dynamic Condition in Electric Vehicles
2.3.2 Cycling Life Test of Low-temperature Charge
2.4 Conclusions
2.5 References
3. Battery Degradation Mechanism and Analysis Method
3.1 Principles of Battery
3.1.1 Working Principles
3.1.2 Basic Structure
3.2 Degradation Mechanisms
3.2.1 Anode
3.2.2 Cathode
3.2.3 Electrolyte
3.2.4 Separator
3.2.5 Current Collector
3.2.6 Nonlinear Degradation
3.3 Influence of working conditions in Electric Vehicles
3.3.1 Main factors from working conditions in Electric Vehicles
3.3.2 Charging Rate
3.3.3 Charging Cutoff Voltage
3.3.4 Depth of Discharge
3.3.5 Temperature
3.4 Analysis Method
3.4.1 Cell Level
3.4.2 Electrode Level
3.4.3 Material Level
3.5 Conclusions
3.6 Reference
4. Battery State of Health Estimation
4.1 Battery State of Health Estimation based on Feature
4.1.1 Charge Curve-Based Estimation Method
4.1.2 Relaxation Voltage-Based Estimation Method
4.1.3 ICA/DVA-Based Estimation Method
4.1.4 AC Impedance Spectrum-Based Estimation Method
4.1.5 Case Study
4.2 Battery State of Health Estimation based on Model and Algorithm
4.2.1 Capacity Estimation-Based Estimation Method
4.2.2 EIS Calculation-Based Estimation Method
4.2.3 Case Study
4.3 Conclusions
4.4 References
5. Battery Lifetime Prediction Methods
5.1 Model-based Approach
5.1.1 Empirical Model
5.1.2 Equivalent Circuit Method
5.1.3 Filter Method
5.1.4 Fuzzy System Method
5.1.5 Semi-Empirical Model
5.1.6 Case Study
5.2 Data driven methods
5.2.1 Black-Box Modeling
5.2.2 Data Acquisition
5.2.3 Trained and Advanced Algorithms
5.2.4 Data-Driven Calibration Process
5.2.5 Artificial Neutral Networks
5.2.6 Relevance Vector Machine
5.2.7 Grey Theory
5.2.8 Case Study
5.3 Conclusions
5.4 References
6. Lifetime Management of Battery Degradation for Electric Vehicles
6.1 Battery Multi-Layer Management Strategy
6.1.1 Physical Layer
6.1.2 Core Layer
6.1.3 Management Layer
6.2 Battery modeling and state estimation battery lifetime model
6.2.1 Electrochemical-thermal-aging model
6.2.2 Low Temperature Charging Capacity Fade Model
6.2.3 Life Model of Series Battery Pack
6.2.4 Life Model of Parallel Battery Pack
6.3 Optimization of Charging Strategy Based on Lifetime Model
6.3.1 Normal Temperature Charging
6.3.2 Low Temperature Charging
6.4 Lifetime Balance Strategy of Battery Pack
6.4.1 Life Balance Strategy for Maximizing of Single Discharge Capacity
6.4.2 Power Balance Strategy for Maximizing Total Discharge Capacity
6.4.3 Energy Equalization Strategy for Maximizing Total Discharge Energy
6.5 Conclusions
6.6 References
Product details
Product details
- Edition: 1
- Latest edition
- Published: February 19, 2024
- Language: English
About the authors
About the authors
HD
Haifeng Dai
Haifeng Dai (Senior Member, IEEE) received B.S. and M.S. degrees in mechanical engineering and his Ph.D. degree in automotive engineering from Tongji University, Shanghai, China, in 2003, 2005, and 2008, respectively. He is currently a Professor at the National Fuel Cell Vehicle and Powertrain System Research and Engineering Center and the School of Automotive Studies, Tongji University. He has been involved in the research of vehicle electrification for more than 10 years and has carried out original research on vehicle electrification including battery multi-domain & multi-scale modeling, state estimation and thermal management, system control, etc. He has published more than 120 papers and is the IEEE Senior Member and one of Elsevier’s “Most Cited Researchers.”
Affiliations and expertise
Professor at Tongji University, School of Automotive Studies, Tongji University, CHINAJZ
Jiangong Zhu
Dr. Jiangong Zhu is an Associate Professor at Tongji University. He received his Ph.D. degree in automotive engineering from Tongji University, Shanghai, China, in 2017. He was previously a Post-Doctoral Researcher with the Institute for Applied Materials—Energy Storage Systems (IAM-ESS), Karlsruhe Institute of Technology (KIT), Karlsruhe, Germany. He is a “Humboldtian” with the support from the Humboldt Foundation. His research focus is on applying the ex-situ (post-mortem analysis) and in-situ methods (e.g., impedance, neutron powder diffraction) to investigate the battery degradation, and inventing new science methods (e.g., machine learning and optimization) to prognose the battery state of health and manage the battery lifespan.
Affiliations and expertise
Associate Professor at Tongji University, School of Automotive Studies, Tongji University, China.View book on ScienceDirect
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