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A critical review of online battery remaining useful lifetime prediction methods.

Wang, Shunli; Jin, Siyu; Deng, Dan; Fernandez, Carlos

Authors

Shunli Wang

Siyu Jin

Dan Deng



Abstract

Lithium-ion batteries play an important role in our daily lives. The prediction of the remaining service life of lithium-ion batteries has become an important issue. This article reviews the methods for predicting the remaining service life of lithium-ion batteries from three aspects: machine learning, adaptive filtering, and random processes. The purpose of this study is to review, classify and compare different methods proposed in the literature to predict the remaining service life of lithium-ion batteries. This article first summarizes and classifies various methods for predicting the remaining service life of lithium-ion batteries that have been proposed in recent years. On this basis, by selecting specific criteria to evaluate and compare the accuracy of different models, find the most suitable method. Finally, summarize the development of various methods. According to the research in this article, the average accuracy of machine learning is 32.02% higher than the average of the other two methods, and the prediction cycle is 9.87% shorter than the average of the other two methods.

Citation

WANG, S., JIN, S., DENG, D. and FERNANDEZ, C. 2021. A critical review of online battery remaining useful lifetime prediction methods. Frontiers in mechanical engineering [online], 7, article 719718. Available from: https://doi.org/10.3389/fmech.2021.719718

Journal Article Type Article
Acceptance Date Jul 16, 2021
Online Publication Date Aug 3, 2021
Publication Date Dec 31, 2021
Deposit Date Aug 27, 2021
Publicly Available Date Aug 27, 2021
Journal Frontiers in Mechanical Engineering
Electronic ISSN 2297-3079
Publisher Frontiers Media
Peer Reviewed Peer Reviewed
Volume 7
Article Number 719718
DOI https://doi.org/10.3389/fmech.2021.719718
Keywords Lithium-ion batteries; Remaining useful lifetime; Machine learning; Adaptive filtering; Stochastic process methods
Public URL https://rgu-repository.worktribe.com/output/1428388

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