Lithium battery health status and lifespan prediction based on deep learning

Rechargeable lithium batteries are widely used in the electric vehicle industry due to their long lifespan and high energy density. However, after long-term repeated charging and discharging, various electrochemical reactions inside lithium batteries can lead to performance degradation and even cause battery fires. Estimating the health status and predicting the remaining life of lithium batteries can provide insights into their future operating conditions, which is crucial for achieving fault warnings and ensuring the safe operation of battery-related equipment. In terms of predicting the health status of lithium batteries, this paper proposes a method based on an improved LSTM for health status estimation. This method first employs nearest neighbor component analysis to eliminate feature redundancy among the multidimensional health factors of the battery. Then, the differential grey wolf optimization algorithm is used to globally optimize the hyperparameters of the LSTM. The experiments show that this method improves the accuracy of lithium battery health state estimation. Regarding the prediction of the remaining life of lithium batteries, this paper proposes a dual-order attention-based method. Firstly, multiple health factors that characterize the aging of lithium batteries are established. Using these health factors as inputs and the remaining capacity of the lithium-ion battery as output, a neural network is constructed that integrates a two-stage attention mechanism and a gated recurrent unit to predict the remaining life of the lithium-ion battery. The experimental results demonstrate that this method further enhances the accuracy of predicting the remaining life of lithium batteries. Keywords: Electric vehicle lithium battery; Deep learning; Health status prediction; Lifespan prediction