Stacked long short-term memory model for proton exchange membrane fuel cell systems degradation

Abstract Proton exchange membrane fuel cell (PEMFC) systems have numerous applications such as transportation, portable power generation, and military. In this study, we propose a stacked long-short term memory (S-LSTM) model for fitting the degradation of a PEMFC system. Moreover, the proposed model provides the remaining useful life (RUL) prediction. A stacked LSTM architecture with dropout parameters can improve the prediction accuracy of the fuel cell degradation. We optimize the hyper parameters of the S-LSTM model using a differential evolution algorithm. The ageing test conditions of two PEMFC systems are carried by a fixed current and a ripple current, respectively. The results indicate that the S-LSTM model outperforms the other models in the RUL prediction of the PEMFC degradation in terms of mean absolute percent error and root mean square error.

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