A fusion prediction method of lithium-ion battery cycle-life

According to the problems of traditional lithium-ion battery remaining useful life (RUL) prediction method based on particle filter, such as excessive reliance on battery experience degradation model and the single input variable of the model, a fusion RUL estimation approach for lithium-ion battery is proposed based on relevance vector machine (RVM), particle filter (PF) and the autoregressive (AR) model. The degradation trend of battery historical data is extracted by RVM, and the trend equation is built to replace the battery experience degradation model, which is adopted as the state transition equation of the PF algorithm. Long-term trend prediction values of the AR model are used to replace the real values, and then the observation equation of the PF algorithm is constructed. Three methods are integrated to estimate the battery RUL. Experimental results show that the prediction precision of fusion method is high, and the proposed data driven approach is more common because it can avoid building the complex experience degradation model based on battery failure mechanism.