A short- and long-term prognostic associating with remaining useful life estimation for proton exchange membrane fuel cell

Proton exchange membrane fuel cell (PEMFC), as a promising power source, provides a feasible solution for clean and low-carbon energy systems. The durability problem restricts PEMFC application in some scenarios, which can be improved by the prognostic technology indirectly. This paper aims to develop a data-based method to implement the short-term and long-term prognostic simultaneously, and the developed long-term prognostic can be performed without future operation information. First, the short-term prognostics of five multi-step ahead forecasting strategies are proposed and compared based on a long short-term memory (LSTM) network. Results show that the multi-step input and multi-step output (MIMO) with LSTM strategy has a better performance in the short-term prognostics under the test conditions of the stationary and dynamic current. Then, the hyper parameters of the prediction model are determined by an evolutionary algorithm. Furthermore, in the longterm prognostics regime, the variable-step long-term method is proposed and rectified by the short-term prognostics. Finally, the developed remaining useful life (RUL) prediction is compared with a model-based extended Kalman filter. The average root mean square error results for the short-term prognostics of two conditions are 0.00532 and 0.00538, respectively. The RUL estimations of two PEMFCs named FC1 and FC2 are given with 95% and 90% confidence intervals, respectively. Consequently, the proposed method can achieve acceptable accuracies in the short-term prognostic, the long-term prognostic, and the RUL prediction.