Particle Swarm Optimization-Based Feature Weighting and Early-Cycle Remaining Useful Life Prediction of Lithium-Ion Batteries
Predicting the remaining useful life (RUL) of lithium-ion batteries is essential as it relates to the safety, reliability, and maintenance of both electric vehicles and energy storage systems. While machine learning algorithms have greatly enhanced the accuracy of prediction, a lot of the models available today still have some major flaws including redundant features, poor performance due to non-optimized model settings, and the necessity of the complete degradation process of the system. In this study, we develop a machine learning framework using particle swarm optimization (PSO) to perform hyperparameter tuning and feature optimization to improve lithium-ion battery RUL prediction. The framework allows for the automatic determination of the key degradation indicators, resulting in the optimization of the learning model and enhancement of both the accuracy and interpretability of the predictions. Furthermore, the framework is designed to be employed in an early-cycle learning environment wherein only the first 30% of battery discharge cycles are utilized in the model training. This method is intended to mimic the constraints of real-world applications in which complete lifecycle data is unavailable. Under a properly nested evaluation protocol, the results indicate that the model achieves a mean R2 of 0.354 ± 0.157 and RMSE of 19.13 ± 1.39 using only 30% of degradation cycles, compared to a mean R2 of 0.922 ± 0.014 and RMSE of 18.10 ± 1.11 under full-cycle training across five independent evaluations. While early-cycle prediction shows reduced and more variable explanatory power due to the smaller sample size, the model maintains reasonable error magnitude, supporting its potential utility for early diagnosis of lithium-ion batteries. The effectiveness of the framework is confirmed by feature importance analysis, evaluation of optimization convergence and multi-metric error assessment. In conclusion, the framework offers a highly interpretable and effective solution for predicting lithium-ion battery RUL, in alignment with the emerging data-driven approaches to battery management systems.