Degradation prediction of proton exchange membrane fuel cell based on Bi-LSTM-GRU and ESN fusion prognostic framework

Abstract

The durability of proton exchange membrane fuel cell (PEMFC) is one of the technical challenges restricting its commercial applications. To enhance the reliability and durability of PEMFC, a fusion prognostic framework is proposed based on bi-direction long short-term memory (Bi-LSTM), bi-direction gated recurrent unit (Bi-GRU) and echo state network (ESN), which can achieve short-term degradation prediction and remaining useful life (RUL) estimation of PEMFC with fewer training datasets. For short-term prediction, using the first 200 h of voltage degradation data for training can achieve an acceptable and accurate prediction, with the root mean square error (RMSE), mean absolute error (MAE) and coefficient of determination (R2) of 0.0235, 0.0195 and 0.9822, respectively. Compared with traditional machine learning methods, the proposed fusion prognostic framework shows a better predictive performance. In addition, a 100-step-sliding-windows method based on the fusion prognostic framework was implemented for RUL estimation. The results show that the percentage error (Er) is only 1.22% with the first 200 h of training data. The proposed method has great significance for online testing and health management of PEMFC.