Prognostics of remaining useful life for lithium-ion batteries based on a feature vector selection and relevance vector machine approach

Abstract

Although the capacity is often used as a criterion to evaluate the state of health (SOH) of a lithium-ion battery, it cannot be measured on-line. Besides, degradation modeling only depending on historical capacity data will cause a large prediction error in the long term. Actually, some parameters can be monitored, such as the duration of equal discharging voltage difference, the interval of equal charging voltage difference at different experiment cycles, also exhibit a degradation trend. In order to make more accurate SOH and remaining useful life (RUL) estimations of an on-line operating battery, in this paper, the relevance vector machine (RVM) is applied to quantify the relationship between those monitoring parameters and capacity data. Based on the deduced model, the capacity could be extrapolated with the corresponding monitoring parameters. Moreover, feature vector selection (FVS) is used to remove redundant points in the input data. It improves the sparsity of relevance vectors (RVs) and decreases the memory-consuming. In the end, Battery degradation datasets from NASA demonstrated the approach has good RUL prediction accuracy, higher sparsity compared to RVM.