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

Zhendong Zhang, Ya-Xiong Wang, Hongwen He and Fengchun Sun

Applied Energy, 2021, vol. 304, issue C, No S0306261921011661

Abstract: 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 long-term 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.

Keywords: Proton exchange membrane fuel cell; Neural network; long short-term memory (LSTM); Multi-step ahead prognostics; Data-driven model; remaining useful lifetime (RUL) (search for similar items in EconPapers)
Date: 2021
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Citations: View citations in EconPapers (12)

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DOI: 10.1016/j.apenergy.2021.117841

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