PFFN: A Parallel Feature Fusion Network for Remaining Useful Life Early Prediction of Lithium-Ion Battery

Remaining useful life (RUL) early prediction of lithium-ion battery (LIB) is crucial to develop advanced battery health management and complete security assessment. However, most of the existing methods still suffer from two limitations, i.e., the inadaptability to the different data distribution and the inability to capture the relationship between input series and RUL, which always make the RUL early prediction difficult and challengeable. To address these issues, this article proposes a parallel feature fusion network (PFFN) for RUL early prediction of LIB. First, a feature selection strategy is designed to filter the optimal feature sets (containing cycle statistical features and domain knowledge-based features) that are most related to RUL of LIB. Second, two specific transformer encoders connected in parallel configuration are developed to integrate the cycle statistical features and domain knowledge-based features, respectively, achieving original RUL early prediction results. Furthermore, Bayesian optimization is applied for global iterative optimization, aiming to enhance the prediction accuracy and generalization capability. A series of experiments are conducted with different data distributions. Experimental results demonstrate that the proposed PFFN outperforms the state-of-the-art (SOTA) methods, achieving 6.00%~27.61%, 0.58%~6.49%, and 5.95%~7.03% reduction in root mean square error (RMSE), mean absolute percentage error (MAPE), and score, respectively.