State of health prediction of lithium-ion batteries based on machine learning: Advances and perspectives

Summary

Accurate state of health (SOH) prediction is significant to guarantee operation safety and avoid latent failures of lithium-ion batteries. With the development of communication and artificial intelligence technologies, a body of researches have been performed toward precise and reliable SOH prediction method based on machine learning (ML) techniques. In this paper, the conception of SOH is defined, and the state-of-the-art prediction methods are classified based on their primary implementation procedure. As an essential step in ML-based SOH algorithms, the health feature extraction methods reported in the literature are comprehensively surveyed. Next, an exhausted comparison is conducted to elaborate the development of ML-based SOH prediction techniques. Not only their advantages and disadvantages of the application in SOH prediction are reviewed but also their accuracy and execution process are fully discussed. Finally, pivotal challenges and corresponding research directions are provided for more reliable and high-fidelity SOH prediction.

Graphical abstract

Highlights

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  A full review is given for state of health estimation with limitations discussed

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  Existing health feature extraction methods are comprehensively surveyed

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  Machine learning based State of Health estimation is elaborately compared

Abstract

Machine learning; Energy management; Energy storage