A Remaining Useful Life Prediction of Lithium-Ion Batteries via Improved Dream Optimization Algorithm-Tuned LSTM Networks

Abstract

Lithium-ion batteries have been widely utilized in modern society due to their excellent performance, making the accurate prediction of their remaining useful life (RUL) of paramount importance. Current RUL prediction methods are primarily categorized into battery discharge model-based approaches and data-driven approaches. However, the charging and discharging processes of batteries are typically accompanied by nonlinear variations in internal structural parameters, which pose significant challenges for model-based prediction methods. To address this issue, this paper proposes a predictive model based on Long Short-Term Memory (LSTM) networks optimized by an Improved Dream Optimization Algorithm (IDOA). First, to overcome the limitations of the original Dream Optimization Algorithm (DOA)—namely, the tendencies to become trapped in local optima and slow convergence—an optimal point set is employed for population initialization, and an adaptive population reduction mechanism is introduced to accelerate convergence. Second, the IDOA is utilized to optimize the hyperparameters of the LSTM network, thereby enhancing its predictive capability. Finally, simulation experiments are conducted using the CALCE and NASA battery datasets via the leave-one-out cross-validation method. The performance is evaluated using the average Root Mean Square Error (RMSE) and Mean Absolute Error (MAE) calculated over ten independent runs. Comparative analysis with the original DOA and other baseline models demonstrates that the proposed IDOA-LSTM model can accurately predict the RUL of lithium-ion batteries with higher precision. Specifically, for the four batteries in the CALCE dataset, the RMSE values are 0.0201, 0.0198, 0.0213, and 0.0186, while the MAE values are 0.0181, 0.0174, 0.0163, and 0.0154, respectively. The results indicate that the average prediction accuracy of the proposed model is improved by 53.8% compared to the unoptimized model.