Bidirectional LSTM RNN for precise predict remaining useful life of supercapacitors

In this study, the bidirectional long short-term memory recurrent neural network is used to evaluate the remaining useful life of supercapacitors efficiently and accurately. Compared with the one-way recurrent neural network, the bidirectional structure can process the input information from both positive and negative aspects so as to adjust the weight comprehensively. At the same time, the bidirectional structure can make full use of the training data to improve the training efficiency. The simulation results show that the maximum root mean square error and the maximum mean absolute error of the bidirectional long short-term memory recurrent neural network applied to predict the remaining useful life of supercapacitors are 0.0311 and 0.0256 respectively. It is verified that it has excellent performance in predicting the remaining useful life of supercapacitors.