Degradation prediction method of PEMFC based on deep learning hybrid model integrating ARIMA and LSTM

Abstract

Fuel cell involves many disciplines such as electricity, mechanics, electrochemistry, and thermodynamics, and its performance degradation process is complex, involving multi-physics, multi-scale, multi-parts, and multi-factors. Thus, it is difficult for a single model to capture all kinds of characteristics of fuel cell simultaneously in degradation prediction. To ensure the prediction accuracy while better fitting the data linearly and nonlinearly, a prediction model of ARIMA combined with LSTM neural network is proposed in this study. The prediction results with residuals are used as features for LSTM prediction work after first predicting the voltage decay data by ARIMA and LSTM. Comparing the hybrid model with the single ARIMA model and the NAR model with support vector regression learning, it is found that the hybrid model performs better in terms of prediction accuracy and prediction performance.