A Review on the Prediction of Health State and Serving Life of Lithium‐Ion Batteries
Xiaoxian Pang, Shi Zhong, Yali Wang, Wei Yang, Wenzhi Zheng, Gengzhi Sun
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.