A deep learning approach for supercapacitor remaining useful life prediction using pre-classifying strategy

Accurate prediction of the remaining useful life (RUL) of supercapacitors (SCs) is essential for the application of energy storage systems. In the existing end-to-end RUL prediction methods, there exist large errors in life prediction in SCs, especially in the early cycle prediction. This paper proposes an augmentation of the end-to- end RUL prediction method to improve the precision named pre-classifying. First, the pre-classifying is used to divide SCs' cycling data into different classes to ensure consistency in each class. Then the end-to-end models are built for each class to predict the RULs. In this work, a novel hybrid model that combines convolutional neural networks and long short-term memory networks is proposed to capture spatiotemporal features from the cycling data. The experiment results demonstrate that the proposed hybrid model outperforms various deep neural networks in terms of prediction accuracy. When using the same hybrid model as the backbone, we report competitive results of the pre-classifying method on the public dataset with the root mean square error (RMSE) of 147.67 cycles, a 69.5% improvement compared with the RMSE of 484.18 cycles of the classical end-to-end RUL prediction model. Furthermore, experiment results indicate that the proposed pre-classification method significantly enhances the prediction accuracy of the standard end-to-end RUL prediction method with different deep neural network architectures. These results demonstrate that the pre-classifying method can be a general and effective methodology for RUL prediction of complex SC systems.