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

Xing Shu, Shiquan Shen, Jiangwei Shen, Yuanjian Zhang, Guang Li, Zheng Chen*, Yonggang Liu*

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

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Abstract

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.
Original languageEnglish
Article number103265
JournaliScience
Volume24
Issue number11
Early online date14 Oct 2021
DOIs
Publication statusPublished - 19 Nov 2021

Keywords

  • Energy management
  • Energy storage
  • Machine learning

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