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 coef-ficient 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 pro-posed method has great significance for online testing and health management of PEMFC.(c) 2022 Hydrogen Energy Publications LLC. Published by Elsevier Ltd. All rights reserved.
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