Degradation Prediction of Proton Exchange Membrane Fuel Cell Based on Multi-Head Attention Neural Network and Transformer Model

Yikai Tang, Xing Huang, Yanju Li, Haoran Ma, Kai Zhang and Ke Song ()
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Yikai Tang: School of Automotive Studies, Tongji University, Shanghai 201804, China
Xing Huang: School of Automotive Studies, Tongji University, Shanghai 201804, China
Yanju Li: School of Automotive Studies, Tongji University, Shanghai 201804, China
Haoran Ma: School of Automotive Studies, Tongji University, Shanghai 201804, China
Kai Zhang: School of Aerospace Engineering and Applied Mechanics, Tongji University, Shanghai 200092, China
Ke Song: School of Automotive Studies, Tongji University, Shanghai 201804, China

Energies, 2025, vol. 18, issue 12, 1-20

Abstract: Proton exchange membrane fuel cells are a clean energy technology with wide application in transportation and stationary energy systems. Due to the problem of voltage degradation under long-term dynamic loads, predicting their performance degradation trend is of great significance for extending the life of proton exchange membrane fuel cells and improving system reliability. This study adopts a data-driven approach to construct a degradation prediction model. In view of the problem of many input parameters and complex distribution of degradation features, a neural network model based on a multi-head attention mechanism and class token is first proposed to analyze the impact of different operating parameters on the output voltage prediction. The importance of each input variable is quantified by the attention weight matrix to assist feature screening. Subsequently, a prediction model is constructed based on Transformer to characterize the voltage degradation trend of fuel cells under dynamic conditions. The experimental results show that the root mean square error and mean absolute error of the model in the test phase are 0.008954 and 0.006590, showing strong prediction performance. Based on the importance evaluation provided by the first model, 11 key parameters were selected as inputs. After this input simplification, the model still maintained a prediction accuracy comparable to that of the full-feature model. This result verifies the effectiveness of the feature screening strategy and demonstrates its contribution to improved generalization and robustness.

Keywords: proton exchange membrane fuel cell; data-driven model; degradation prediction; multi-head attention; transformer model (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: 2025
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