Remaining useful life prediction for lithium-ion batteries in later period based on a fusion model

Abstract

Lithium-ion batteries are broadly used in many fields. Accurate remaining useful life (RUL) prediction ensures the reliable operation and the safety of battery systems. However, no single model can realize long-term prediction for RUL with the reliable uncertainty management in the later period. To this end, a competitive model based on an improved autoregressive (AR) and particle filter (PF) model is proposed. Specifically, the similarity capacity series is creatively employed in the AR model, while the underlying capacity is introduced as a new approach for the parameter estimation of the observation equation in PF. Then, average weight is used to update the state equation and describe the future system states. After that, the RUL and its probability density function are obtained by PF again. The effectiveness and robustness are verified by the National Aeronautics and Space Administration (NASA) dataset. Results illustrate that the fusion model outperforms others and accurately predicts RUL with narrow uncertainty representation in the later period.