Deep Learning Based Prediction of Lithium Battery State of Health

Lithium-ion batteries are often used as energy storage elements to enable the storage and use of electrical energy, and accurate estimation of their health status can provide a theoretical basis for the practical use of the batteries. However, the health state cannot be measured directly. In this study, in order to better realize the prediction of the health status of lithium batteries, based on analyzing the existing literature, a deep learning-based battery health status prediction method is proposed. The steps of this method are firstly, after recording the charging and discharging of lithium batteries, the health factors related to the battery capacity among them are selected for purposive analysis, after which the time series features are fully learned by the constructed BiLSTM network model, and finally the key parameters are optimized by the dung-beetle algorithm to realize the prediction of the health state of lithium batteries. Finally, by comparing with other models, it is found that the model used in this study has higher accuracy in the prediction of the health status of lithium batteries, and it also proves the correctness and feasibility of this study.