A Review on the Prediction of Health State and Serving Life of Lithium‐Ion Batteries. Issue 10 (4th July 2022)

• Record Type: Journal Article
• Authors: Pang, Xiaoxian; Zhong, Shi; Wang, Yali; Yang, Wei; Zheng, Wenzhi; Sun, Gengzhi
• Is Part Of: Chemical record. Volume 22:Issue 10(2022)
• Page Start: n/a
• Page End: n/a
• Publication Date: 2022-07-04
• Subjects: Lithium-ion battery -- State of health -- End of life -- Prediction method; Chemistry -- Periodicals; 540
• DOI: 10.1002/tcr.202200131
• Languages: English
• ISSNs: 1527-8999

Abstract

Abstract: The monitoring and prediction of the health status and the end of life of batteries during the actual operation plays a key role in the battery safety management. However, although many related studies have achieved exciting results, there are few systematic and comprehensive reviews on these prediction methods. In this paper, the current prediction models of remaining useful life of lithium‐ion batteries are divided into mechanism‐based models, semi‐empirical models and data‐driven models. Their advantages, technical obstacles, improvement methods and prediction performance are summarized, and the latest research results are shown by comparison. We highlight that the fusion models of convolution neural network, long short term memory network and so on, which have great practical application prospects because of their outstanding computing efficiency and strong modeling ability. Finally, we look forward to the future work in simplifying the model and improving its interpretability. Abstract : In this paper, the research progress of mechanism‐based model, semi‐empirical model and data‐driven model in the prediction of health state and remaining useful life of lithium‐ion battery are reviewed, and the advantages, limitations and improved methods of the three models are compared. This review contributes to an in‐depth understanding of the mechanism, quantitative methods and prediction methods of battery life aging, and provides an important reference for the further development of high‐accuracy prediction models.