The Degradation Prediction of Proton Exchange Membrane Fuel Cell Performance Based on a Transformer Model

Xuan Meng, Jian Mei, Xingwang Tang, Jinhai Jiang, Chuanyu Sun () and Kai Song ()
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Xuan Meng: School of Electrical Engineering and Automation, Harbin Institute of Technology, Harbin 150001, China
Jian Mei: School of Electrical Engineering and Automation, Harbin Institute of Technology, Harbin 150001, China
Xingwang Tang: School of Electrical Engineering and Automation, Harbin Institute of Technology, Harbin 150001, China
Jinhai Jiang: School of Electrical Engineering and Automation, Harbin Institute of Technology, Harbin 150001, China
Chuanyu Sun: School of Electrical Engineering and Automation, Harbin Institute of Technology, Harbin 150001, China
Kai Song: School of Electrical Engineering and Automation, Harbin Institute of Technology, Harbin 150001, China

Energies, 2024, vol. 17, issue 12, 1-13

Abstract: Proton exchange membrane fuel cells have attracted widespread attention due to their cleanliness and high energy density, but the performance degradation during operation greatly limits their commercialization. Therefore, the reliable degradation prediction of fuel cell performance is of great significance. The recovery phenomenon of the reversible voltage loss that occurs during the operation of fuel cells has posed great difficulties for model training and prediction. Moreover, the models may easily and erroneously learn the combined trends in the recovery of reversible voltage loss and performance degradation. To address this issue, this paper employs the Transformer model to predict the performance degradation of fuel cells. By utilizing the unique self-attention structure and masking mechanism of the Transformer model, the signal for the recovery of the reversible voltage loss is adopted as the input for the model to avoid interference from information before voltage recovery on subsequent predictions. Experimental results show that the model has the highest prediction accuracy at various prediction starting points. Meanwhile, it can predict the accelerated performance degradation of fuel cells, which has positive implications for health management.

Keywords: proton exchange membrane fuel cells; Transformer model; recovery of the reversible voltage loss; performance degradation prediction; health management; PEMFC (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2024
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (7)

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