Lithium-Ion Battery Health State Prediction Based on Improved War Optimization Assisted-Long and Short-Term Memory Network

It is essential that the state of health (SOH) for lithium-ion batteries is measured to ensure the safety and reliability of electric vehicles. However, an accurate prediction of SOH is still an art due to the complex degradation mechanisms. To address this challenge, a SOH prediction model based on Warfare Strategy Optimization-assisted hybrid mutual information in-former-Long Short-Term Memory neural network (IWSO-MILSTM) is proposed. First, both direct and virtual health indicators are derived from battery degradation curves. Building on this foundation, mutual information is applied to the correlation analysis of these health indicators, and the redundant health indicators can be filtered. Then, the selected health indicators are fed into the informer-LSTM to construct an interpretable predicted model for the health status of lithium-ion batteries. Notably, both redundancy of health indicators and the imprecision of model hyperparameters for LSTM affect the SOH prediction precision. IWSO is proposed to achieve co-optimization of filtering for health indicators and hyperparameters for the informer-LSTM based on developed initializing distribution methods and adaptive function so that the SOH prediction precision is ensured. Finally, the NASA dataset is used to validate the prediction precision of the IWSO-MILSTM, and the experimental results show that the IWSO-MILSTM can provide more competitive results, i.e., the R2 value is improved by 25.68% and 3.63%, respectively, while the RMSE is reduced by 48.76% and 75.91% compared with XGBoost, LSTM, etc. Such results indicate the proposed method can predict SOH efficiently.