Health Status Prediction of Lithium Battery Based on LSTM Model with Optimization Algorithms

Abstract Consider the current status of health (SOH) of lithium batteries, which presents challenging existing issues of accurately predicting and calculating. In this paper, an LSTM model and multi-optimization algorithm were used to estimate the battery health state. Taking advantage of the fast convergence speed and wide global optimization range of the optimization algorithm, optimized the number of layers and neurons in the LSTM model so the LSTM model was established, used to predict the health status of lithium batteries, and compared with the LSTM model prediction method without optimization. The results showed that the error of the battery health prediction model based on the proposed prediction model was less than 3%, the prediction accuracy was higher than the LSTM model without optimization, and the model had better accuracy and stability.