An interpretable remaining useful life prediction scheme of lithium-ion battery considering capacity regeneration

Abstract

Remaining useful life (RUL) prediction is the core part of battery management system. The capacity regeneration phenomenon during battery capacity degradation interferes with the accuracy of RUL prediction. Therefore, this paper proposes an interpretable scheme named VPA model for lithium-ion battery RUL prediction by integrating algorithms with sufficient mathematical support. Firstly, trend signal (TS) and capacity regeneration signal (CRS) are obtained from capacity degradation sequence by variational mode decomposition algorithm. Then, prediction of TS and CRS is implemented by particle filter model and autoregressive integrated moving average model, respectively, and the prediction results of TS and CRS are superimposed as capacity degradation forecast. Finally, RUL prediction is performed based on degradation prediction result and failure threshold. Experimental results in engineering data prove the effectiveness of the proposed scheme.