Life-decomposition-based battery remaining useful life estimation: From a probabilistic view

Precise battery remaining useful life (RUL) estimation is essential for battery system’s long-term management. Most current battery RUL estimation methods rely on historical data and make point estimations of RUL, which are incompetent when historical data is unavailable or a group of batteries is involved. To make battery RUL estimation independent of historical data and applicable for both a single cell and a group of cells, a life-decomposition-based battery RUL estimation framework is proposed in this paper. It decomposes RUL as the product of two random variables, extent of cycling (EOC) and whole life (WL), and forms RUL distribution based on current cycle’s data. For single-cell RUL estimation, the proposed method is tested on the open-access ISU-ILCC Battery Aging Dataset and achieves a relatively accurate estimation with a root mean square error (RMSE) of 306.13 cycles and an average normalized mean square percentage error (N-RMSPE) of 23.11 % on unseen battery cycling data. With more accurately observed WL information, these results are further improved to an RMSE of 217.77 cycles and an average N-RMSPE of 11.32 %. For battery group analysis, the RUL distribution is applied to the end-of-life warning from a group perspective. Warning threshold is calculated from RUL distribution’s mode and variance to indicate the existence of near-end-of-life cells, achieving an overall 0.8150 F2 score. Moreover, the predicted EOC may serve as a notable index for inconsistency among cells and battery sorting. • Remaining useful life (RUL) is decomposed into two effective elements: extent of cycling (EOC) and whole life (WL). • The distribution properties of WL and conditioned EOC are defined and thoroughly studied. • The RUL distribution is applied to RUL estimation at single cycles and analysis of battery group.