RUL Prediction of Lithium-Ion Battery in Early-Cycle Stage Based on Similar Sample Fusion Under Lebesgue Sampling Framework.

Remaining useful life (RUL) prediction of lithium-ion battery in early-cycle stage is of great significance to improve battery performance and reduce losses caused by failure. Because of complex degradation mechanism and insufficient data in early-cycle stage, current RUL prediction schemes for lithium-ion battery have trouble obtaining degradation characteristics to achieve satisfactory prediction accuracy. Aiming at this problem, this article proposes an RUL prediction method of lithium-ion batteries in early-cycle stage based on similar sample fusion under the Lebesgue sampling framework. First, a novel similarity measurement index based on the fusion of Pearson correlation coefficient (PCC) and Euclidean distance (EuD) is proposed, and the fusion parameter is optimized by the jumping spider optimization algorithm (JSOA). Similar samples are selected as references for the prediction model. Then, the Lebesgue sampling theory is introduced to complete data structure transformation for similar samples so as to ensure that the fused points of different similar samples are under the same degradation state. Finally, a similar sample fusion result is transformed into Riemann sampling framework, and a linear fitting is performed. Fitting results are used to construct a particle filter (PF) model for the capacity degradation process and RUL prediction. Experimental results and comparative studies on the APR18650M1A battery dataset demonstrate the effectiveness of the proposed method.