Abstract
The prediction of the remaining useful life (RUL)of Lithium-ion batteries (LIBs) is more challenging due to the strong interference of internal characteristics and external factors. Therefore, the two different preprocessing methods are proposed in this paper to alleviate this problem, and the reliability and applicability of different preprocessing techniques for RUL prediction need to be concerned and studied. The aim of this study is to compare the capabilities of two preprocessing technology approaches and to evaluate the performance of lithium-ion battery RUL and capacity prediction. Firstly, the historical degraded data are decomposed using empirical modal decomposition and wavelet transform, respectively. Secondly, the real data are reconstructed by correlation coefficients and alignment entropy association coefficients (AC). Finally, support vector regression (SVR) model is built and the RUL of LIBs is predicted. This experimental data is obtained from the actual test Lithium-ion batteries 18650 provided by NASA PCoE, and additionally, some quantitative metrics are used to analyze and evaluate the battery RUL estimation results.
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