Lithium-ion batteries remaining useful life prediction based on a mixture of empirical mode decomposition and ARIMA model

Abstract

Prediction of lithium-ion batteries remaining useful life (RUL) plays an important role in battery management system (BMS) used in electric vehicles. A novel approach which combines empirical mode decomposition (EMD) and autoregressive integrated moving average (ARIMA) model is proposed for RUL prognostic in this paper. At first, EMD is utilized to decouple global deterioration trend and capacity regeneration from state-of-health (SOH) time series, which are then used in ARIMA model to predict the global deterioration trend and capacity regeneration, respectively. Next, all the separate prediction results are added up to obtain a comprehensive SOH prediction from which the RUL is acquired. The proposed method is validated through lithium-ion batteries aging test data. By comparison with relevance vector machine, monotonic echo sate networks and ARIMA methods, EMD-ARIMA approach gives a more satisfying and accurate prediction result.