A review of deep learning approach to predicting the state of health and state of charge of lithium-ion batteries

Kai Luo, Xiang Chen, Huiru Zheng, Zhicong Shi

Research output: Contribution to journalArticlepeer-review

Abstract

In the field of energy storage, it is very important to predict the state of charge and the state of health of lithium-ion batteries. In this paper, we review the current widely used equivalent circuit and electrochemical models for battery state predictions. The review demonstrates that machine learning and deep learning approaches can be used to construct fast and accurate data-driven models for the prediction of battery performance. The details, advantages, and limitations of these approaches are presented, compared, and summarized. Finally, future key challenges and opportunities are discussed.
Original languageEnglish
JournalJournal of Energy Chemistry
Early online date2 Jul 2022
DOIs
Publication statusE-pub ahead of print - 2 Jul 2022

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