A double broad learning approach based on variational modal decomposition for Lithium-Ion battery prognostics

Abstract

Predicting the remaining life of lithium-ion battery equipment is becoming increasingly important as enterprises transition to smart manufacturing. Accurate prediction results can be used to effectively determine the battery's health status and improve operational safety. However, during the decline process, lithium-ion battery capacity regeneration occurs, resulting in significant fluctuations in the degradation data that can easily lead to insufficient prediction accuracies. At the same time, a factor influencing the prediction results is the unification of modal information and insufficient feature extraction of the battery capacity data in the prediction process. Therefore, in this paper, a novel model based on variational modal decomposition and double broad learning (VMD-DBL) is proposed. First, we use VMD to perform adaptive decomposition of the degraded data to form intrinsic mode function (IMF) components and residual components to solve the data noise problem. Second, these two modal data of the feature extraction and modal fusion are inputted into the trained DBL model. Finally, the two modes are connected to the output layer to obtain the predicted result. The NASA dataset is used for experimental validation in this paper, and the results show that our proposed method outperforms other methods in terms of accuracy and feasibility.