Lithium-ion batteries remaining useful life prediction using Wiener process and unscented particle filter

Remaining useful life (RUL) prediction plays an important role in the prognosis and health management of lithium-ion batteries (LIBs). This paper proposes a new method based on the Wiener process for the RUL prediction of LIBs. Firstly, a state-space model based on the Wiener process is constructed to describe the LIBs degradation process, which considers the four variability sources of the degradation process simultaneously. Then, the model parameters are initialized using maximum likelihood estimation (MLE) and dynamically estimated by an unscented particle filter (UPF) algorithm. Finally, through comparison with other models, the proposed method shows its effectiveness and superiority in describing the degradation process and RUL prediction of LIBs.