Capacity Prediction Model of Lithium-Ion Batteries Based on Transfer Entropy and JS-BP Neural Network - Xiaozhong WU; Lihua XIAO; Chao TONG; Xiangyang XIA; Ling YUAN; Xing GAN; Zhiwen JIANG; Xiangyuan HUANG (Zhongguo dianli)
Accurately predicting the available capacity of lithium-ion batteries is critical to ensuring the safe operation of energy storage systems. Therefore, this paper proposes a method for predicting the capacity of lithium-ion batteries in energy storage systems based on transfer entropy and JS-BP neural networks. Based on an analysis of the information entropy of relevant parameters of the energy storage batteries, the health factors that have a significant impact on the available capacity of batteries are selected, and a prediction model for the available capacity of batteries is established by combining the selected healthy factors with the JS-BP neural network. Finally, a comprehensive analysis is carried out based on the aging datasets from NASA and the battery aging experimental platform, and the results show that the proposed method has a high prediction accuracy of battery capacity, and the error indicators MAE and RMSE are at a low level, which verifies the accuracy of the model.