A Critical Review of Online Battery Remaining Useful Lifetime Prediction Methods

• Main Authors: Shunli Wang, Siyu Jin, Dan Deng, Carlos Fernandez
• Format: Article
• Language: English
• Published: Frontiers Media S.A. 2021-08-01
• Series: Frontiers in Mechanical Engineering
• Subjects: lithium-ion batteries; remaining useful lifetime; machine learning; adaptive filtering; stochastic process methods
• Online Access: https://www.frontiersin.org/articles/10.3389/fmech.2021.719718/full

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.