Machine Learning-based Remaining Useful Life Prediction Techniques for Lithium-ion Battery Management Systems: A Comprehensive Review

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カテゴリ: 論文誌(論文単位)グループ名: 【D】産業応用部門（英文）発行日: 2023/07/01タイトル(英語): Machine Learning-based Remaining Useful Life Prediction Techniques for Lithium-ion Battery Management Systems: A Compreh ensive Review著者名: Akash Samanta (Department of Electrical, Computer and Software Engineering, Ontario Tech University), Sheldon Williamson (Department of Electrical, Computer and Software Engineering, Ontario Tech University)著者名(英語): Akash Samanta (Department of Electrical, Computer and Software Engineering, Ontario Tech University), Sheldon Williamson (Department of Electrical, Computer and Software Engineering, Ontario Tech University)キーワード: prognostics and health management，electric vehicle，machine learning，deep learning，state estimation要約(英語): Lithium-ion batteries (LIBs) are used to power a range of applications starting from portable consumer electronics to electric vehicles and grid-tied energy storage systems. Now, with the increasing application of LIB in high power and sophisticated applications, it is of great significance to predict the remaining useful life (RUL) for reliable operation and to protect the battery pack from unwanted incidents including catastrophic failure. Real-time information on RUL is essential to predict battery failure condition resulting in effective prevention or at least reduction of the damage that may cause by the battery failure. Moreover, accurate RUL is extremely helpful for scheduling routine maintenance and necessary replacement at the end of its useful life. Consequently, RUL prediction has become a topic of interest to researchers. There are several RUL estimation techniques proposed in the last decade where machine learning (ML)-based techniques showed superiority in terms of accuracy, adaptability, and modeling. Therefore, ML-based RUL prediction methods are comprehensively reviewed based on their essential performance parameters in this paper. A detailed discussion on the issues, challenges, trends, and future research scopes are also presented to provide clear guideline to the researchers.本誌: IEEJ Journal of Industry Applications Vol.12 No.4 （2023） Special Issue on “IPEC-Himeji 2022”本誌掲載ページ: 563-574 p原稿種別: 論文／英語電子版へのリンク: https://www.jstage.jst.go.jp/article/ieejjia/12/4/12_22004793/_article/-char/ja/ Show more