Advances in Lithium-Ion Batteries for Electric Vehicles: Degradation Mechanism, Health Estimation, and...

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Haifeng Dai (Professor at Tongji University, School of Automotive Studies, Tongji University, CHINA) Jiangong Zhu (Associate Professor at Tongji University, School of Automotive Studies, Tongji University, China.)

Advances in Lithium-Ion Batteries for Electric Vehicles

Degradation Mechanism, Health Estimation, and Lifetime Prediction

Haifeng Dai (Professor at Tongji University, School of Automotive Studies, Tongji University, CHINA), Jiangong Zhu (Associate Professor at Tongji University, School of Automotive Studies, Tongji University, China.)

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English

Elsevier - Health Sciences Division
19 February 2024

Power generation & distribution; Energy conversion & storage; Alternative & renewable energy sources & technology; Transport technology & trades

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.

By: Haifeng Dai (Professor at Tongji University School of Automotive Studies Tongji University CHINA), Jiangong Zhu (Associate Professor at Tongji University, School of Automotive Studies, Tongji University, China.)
Imprint: Elsevier - Health Sciences Division
Country of Publication: United States
Dimensions: Height: 229mm, Width: 151mm,
Weight: 450g
ISBN: 9780443155437
ISBN 10: 0443155437
Pages: 324
Publication Date: 19 February 2024
Audience: Professional and scholarly , Undergraduate
Format: Paperback
Publisher's Status: Active

1. Overview of Li-ion Battery Aging 2. Battery Aging Test 3. Battery Degradation Mechanism and Analysis Method 4. Battery State of Health Estimation 5. Battery Lifetime Prediction Methods 6. Lifetime Management of Battery Degradation for Electric Vehicles

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. 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.