Description
Internal Short-Circuit Fault Identification of Lithium-Ion Batteries
Li, Bin; Li, Guanzheng
ISBN 978-981-92-5766-9 · Springer · XVI, 167 p. 74 illus., 60 illus. in color. ·
Description / Beschreibung
This book focuses on state estimation and internal short-circuit fault identification, two key challenges in ensuring the safe operation and maintenance of lithium-ion batteries. It begins with an overview of lithium-ion batteries and battery management systems, followed by a systematic introduction to data-driven state estimation methods. The book then examines internal short-circuit faults in depth, including their underlying mechanisms, diagnostic requirements, and advanced identification techniques. Particular emphasis is placed on novel data-driven methods for detecting incipient internal short-circuits, as well as model-based approaches for the quantitative estimation of both early-stage and severe faults. Supported by extensive experimental data, tables, and illustrative analyses, the book effectively integrates theoretical foundations with practical applications. It provides researchers and industry professionals with valuable insights and practical tools for battery state monitoring, fault diagnosis, and the development of safer battery systems.
Contents / Inhaltsverzeichnis
Chapter 1. Introduction.- Chapter 2. Battery Management System.- Chapter 3. Data-Driven State Estimation Methods.- Chapter 4. Causes Analysis of Internal Short-Circuit Faults.- Chapter 5. A Data-Driven Internal Short-Circuit Faults Identification Method.- Chapter 6. An Early-Stage Internal Short-Circuit Fault Identification Method Based on Battery Model.- Chapter 7. A Severe Internal Short-Circuit Faults Identification Method Based on Battery Model.- Chapter 8. An Internal Short-Circuit Faults Identification Method Based on Inter-Cell Comparison.
Contributors / Mitwirkende
Bin Li is a professor of the School of Electrical and Information Engineering, Tianjin University, Tianjin, China. He is an IEEE Fellow, IET Fellow and CES Fellow. He was ever an academic visitor of the University of Manchester, U.K. in 2006, and an engineer in AREVA Company U.K. from 2008 to 2009. His main research field is involved in the protection and control of smart grids and energy systems. Bin Li has published 8 books as an author or co-author. He has more than 70 invention patents and published more than 200 academic international journal papers. Currently, Bin Li serves as a principal investigator of the National Industry-Education Platform for Energy Storage in Tianjin University, and is undertaking several ongoing research projects supported by the National Natural Science Foundation of China and industry partners.
Guanzheng Li received the B.Sc. and M.Sc. degrees in electrical engineering from Tianjin University, Tianjin, China, in 2019 and 2022, respectively, where he is currently working toward the Ph.D. degree. His research interests include the state estimation and internal short-circuit identification methods in lithium-ion batteries. He has published several influential papers and holds multiple invention patents.
Features / Besonderheiten
Elaborates on cutting-edge data-driven state estimation methods for lithium-ion batteries Covers systematic analysis of internal short-circuit fault causes and identification requirements Details model-light quantitative identification techniques for both early-stage and severe internal short-circuit faults
