| Summary: | Lithium-ion batteries (LIBs) have been widely used in various electronic equipment. The development of an effective method for predicting the remaining useful life (RUL) of LIBs can ensure the normal operation of equipment by providing an appropriate warning before the battery fails. This study presents a method for predicting the RUL of LIBs based on Dempster-Shafer theory (DST) and the support vector regression-particle filter (SVR-PF), which improves the prediction accuracy when the available data are relatively sparse. The model of LIB RUL prediction based on DST and SVR-PF was developed and proposed based on a DST algorithm and the central limit theorem. Moreover, this study proposes an approach to update the basic probability assignment (BPA) of DST, which represents the confidence of the prediction, at each iteration during the RUL prediction. The updated BPA at each iteration will increase the importance of the high confidence prediction method in the combined results. Thus, it will provide a more accurate prediction result. The proposed method can also be used as a framework to combine the prediction results obtained from various independent data. The simulation results and the comparison with the existing LIB RUL prediction methods show that the proposed method provides more accurate and reliable prediction results.
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