Parallel GhostNet classification prediction method for supercapacitor remaining useful life prediction

Abstract

Accurate and rapid prediction of supercapacitors’ remaining useful life (RUL) and timely replacement of failing supercapacitors are of great importance to systemic stability and safety. To decrease the effects of the manual extraction of aging characteristics and fluctuations in the capacity data of supercapacitors for supercapacitor RUL prediction, a parallel GhostNet classification prediction method for supercapacitor RUL prediction is proposed. In this study, the mapping relationship between supercapacitor charging/discharging capacity data and RUL is established directly. In addition, the aging characteristics are learned from the raw observation data without relevant reserve knowledge. The supercapacitor RUL is quantified into 30 rank intervals and predicted by the parallel GhostNet classification method. The validation results based on 60 supercapacitors indicate that the prediction precision of the parallel GhostNet for supercapacitor RUL is 81.84 %, 21.28 % higher than that of a single GhostNet, 19.86 % higher than that of the Xeption model with the highest accuracy among other classical networks. Furthermore, introducing depth separable convolution, the prediction speed of the proposed parallel GhostNet model is 50576 s faster than that of the Xeption model.