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42. The development of machine learning-based remaining useful life prediction for lithium-ion batteries

Author

Li, Xingjun, Yu, Dan, Vilsen, Søren Byg, and Stroe, Daniel-Ioan

Subjects
Remaining useful lifetime prediction, Lithium-ion batteries, Fuel Technology, Machine learning, SDG 13 - Climate Action, Electrochemistry, Energy Engineering and Power Technology, Lifetime extension, SDG 7 - Affordable and Clean Energy, Energy (miscellaneous)
Abstract

Lithium-ion batteries are the most widely used energy storage devices, for which the accurate prediction of the remaining useful life (RUL) is crucial to their reliable operation and accident prevention. This work thoroughly investigates the developmental trend of RUL prediction with machine learning (ML) algorithms based on the objective screening and statistics of related papers over the past decade to analyze the research core and find future improvement directions. The possibility of extending lithium-ion battery lifetime using RUL prediction results is also explored in this paper. The ten most used ML algorithms for RUL prediction are first identified in 380 relevant papers. Then the general flow of RUL prediction and an in-depth introduction to the four most used signal pre-processing techniques in RUL prediction are presented. The research core of common ML algorithms is given first time in a uniform format in chronological order. The algorithms are also compared from aspects of accuracy and characteristics comprehensively, and the novel and general improvement directions or opportunities including improvement in early prediction, local regeneration modeling, physical information fusion, generalized transfer learning, and hardware implementation are further outlooked. Finally, the methods of battery lifetime extension are summarized, and the feasibility of using RUL as an indicator for extending battery lifetime is outlooked. Battery lifetime can be extended by optimizing the charging profile serval times according to the accurate RUL prediction results online in the future. This paper aims to give inspiration to the future improvement of ML algorithms in battery RUL prediction and lifetime extension strategy.

Published
2023
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