A Novel Remaining Useful Life Prediction Method for Capacity Diving Lithium-Ion Batteries

To be prepared for the capacity diving phenomena in future capacity deterioration, a hybrid method for predicting the remaining useful life (RUL) of lithium-ion batteries (LIBs) is proposed. First, a novel empirical degradation model is proposed in this paper to improve the generalization applicability and accuracy of the algorithm. A particle filter (PF) algorithm is then implemented to generate the original error series using prognostic results. Next, a discrete wavelet transform (DWT) algorithm is designed to decompose and reconstruct the original error series to improve the data validity by reducing the local noise distribution information. A relatively less approximate component is selected as the reconstructed error series, which preserves the primary evolutionary information. Finally, to make full use of the information contained in the PF algorithm's prognosis results, the support vector regression (SVR) algorithm is utilized to correct the PF prognosis results. The results indicate that long-short-term deterioration progress and RUL prediction tasks can both benefit from significant performance improvements.

Automated summary

A hybrid method is proposed in this paper to predict the remaining useful life (RUL) of lithium-ion batteries (LIBs) in order to be prepared for future capacity deterioration. The method utilizes an empirical degradation model, a particle filter algorithm, and a discrete wavelet transform algorithm to improve prediction performance and data validity. Experimental results demonstrate significant improvements in both long-short-term deterioration progress and RUL prediction tasks.