A two-stage online remaining useful life prediction framework for supercapacitors based on the fusion of deep learning network and state estimation algorithm

Abstract

Supercapacitors have been widely used in many fields. The safe and stable operation of supercapacitors requires accurate remaining useful life (RUL) prediction. This paper proposes a two-stage online RUL prediction framework based on the bidirectional long short-term memory (BiLSTM) network and the H∞ observer. In Stage 1, the BiLSTM network as well as the Bayesian Optimization algorithm is used to estimate capacitance. The Bayesian Optimization (BO) algorithm aims to optimize the hyperparameters of the BiLSTM network. In Stage 2, the estimated capacitance is handled by the moving average filter (MAF) to alleviate short-term fluctuations. The double exponential model is employed to describe the degradation trajectory, and the model is iteratively updated by the H∞ observer with the estimated capacitance as the measurement. Experiments are implemented to verify the validation of the proposed framework. The results indicate that the BiLSTM network can explain more than 99% variation of capacitance. The deviation of RUL prediction is less than 6% in most cases, and it is even less than 2.5% in late phase. The proposed framework takes the advantages of online deployment, and achieves competitive prediction accuracy when compared with offline methods.