A machine learning method for prediction of remaining useful life of supercapacitors with multi-stage modification

Abstract

Stable and accurate prediction of the remaining useful life (RUL) of supercapacitors is of great significance for the safe operation and economic maximization of the energy storage system based on supercapacitors. For the phenomenon of unstable discharge capacity of supercapacitor during the cycling, a multi-stage (MS) prediction model based on empirical mode decomposition (EMD) and gated recurrent unit (GRU) neural network is proposed. The prediction model is based on multi-feature inputs with high correlation, and the final output is obtained through EMD reconstruction. The modification process ensures the stability of the model to predict the discharge capacity during the cycling of the supercapacitor. Compared with the traditional seven prediction models, the root mean square error is reduced by 80 %, and the goodness of fit is increased by 6 %. Our method has higher stability and prediction accuracy, while satisfying the high compatibility between the features and models, and provides a feasible strategy for the application of supercapacitors in energy storage systems.