A fusion method for predicting the remaining useful life of lithium-ion batteries based on IMMUKF and WAVENN

Accurate remaining useful life (RUL) prediction is critical for optimizing the performance and safety of lithium-ion battery systems across various applications. To enhance prediction performance, a novel fusion framework is proposed. This framework uniquely integrates the interacting multiple-model unscented Kalman Filter (IMM-UKF) with a wavelet neural network (WAVENN), leveraging their complementary strengths. In this framework, the IMM-UKF is employed to integrate various existing mathematical models that describe the capacity degradation of lithium-ion batteries, while simultaneously incorporating predictions generated by the WAVENN. The method features automatic identification and switching between different models through the online updating of weight coefficients and model probabilities. Consequently, an optimal capacity estimate is derived from a weighted aggregation of the forecasts. This iterative process ultimately yields the RUL prediction. The proposed multi-model fusion method is rigorously evaluated against traditional single-model benchmarks. Experimental results confirm its superior predictive accuracy.