A Novel Health Index for Battery RUL Degradation Modeling and Prognostics

Abstract

Lithium-ion batteriesare widely used in our daily life. However, with the frequent use of lithium battery, the performance of lithium battery decreases due to the change of internal physical properties. The most intuitive result is that the capacity gradually decreases with the use of the battery. Therefore, timely and effective prediction of lithium-ion battery remaining useful life (RUL) is particularly important. In this paper, two new health indexes (HI), namely, discharging time difference of equal voltage interval (DtD_EVI) and discharging temperature difference of equal time interval (DTD_EtI), are proposed to represent the degradation process of lithium battery. Pearson correlation coefficient is used to analyze the relationship between these two health indexes and capacity, and then support vector regression (SVR) is used to establish the RUL regression model. Finally, the validity of the proposed method is verified by analyzing the lithium battery dataset of NASA.