An integrated method is proposed for RUL prediction as well as short-term capacity prediction.
Relevance vector regression model is employed as a nonlinear time-series prediction model.
Unscented Kalman filter is used to recursively update the states for battery model parameters during the prediction.
A time-varying weighting scheme is utilized to improve the accuracy of the RUL prediction.
The proposed method demonstrates high reliability and prediction accuracy.