Prediction of the Remaining Useful Life of Lithium-Ion Batteries Based on Health Features Extraction and Improved Stochastic Configuration Networks

The remaining useful life (RUL) of lithium-ion batteries plays a crucial role in fault prognosis and health management. Therefore, accurate RUL prediction can effectively improve equipment safety and mitigate operational risks. However, existing RUL prediction methods often exhibit limited accuracy caused by capacity regeneration and excessively long training times due to model complexity. In this study, Pearson and Spearman correlation analyses are employed to effectively extract health features that are highly correlated with battery capacity to characterize capacity degradation, and a lithium-ion battery RUL prediction model based on stochastic configuration networks (SCNs) optimized by sparrow search algorithm (SSA) is proposed. The battery datasets from NASA and CALCE are used for validation and testing. Experimental results demonstrate that the proposed SSA-SCN achieves a root mean squared error of 0.0036 and a mean absolute error of 0.0030, while exhibiting faster training time compared with other hybrid methods. The results verify that the proposed method can provide more accurate battery RUL predictions and effectively improve the accuracy and reliability of lithium-ion battery RUL estimation.