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Cited by 91Fuel Cells and Related MaterialsBattery Modeling and ManagementElectric and Hybrid Vehicle Technologies

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

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Meng, Xuan, Mei, Jian, Tang, Xingwang, Jiang, Jinhai, Sun, Chuanyu, Song, Kai·2024·Energies
DOI 10.3390/en17123050
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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.

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Research works printed but not linked: 30

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Metadata

Authors
Meng, X., Mei, J., Tang, X., Jiang, J., Sun, C., & Song, K.
Affiliations
Harbin Institute of Technology (CN)
Corresponding author
  • Sun, Chuanyu
  • Song, Kai
ORCID
Venue
Energies
Publisher
MDPI AG
Published
2024-06-20
Volume
17
Issue
12
Pages
3050
ISSN
1996-1073 (ISSN-L)
DOI
10.3390/en17123050
OpenAlex
W4399855094
Cited by
91
References
0
Citations by year
2026: 46 · 2025: 26 · 2024: 19
FWCI
9.41
Type
journal article
Language
en
Open access
Licence
Locations
  • Energies (publishedVersion, cc-by)
  • RePEc: Research Papers in Economics (submittedVersion)
  • DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
Funders
  • National Natural Science Foundation of China (52238002, 2022FRFK060001, DPGCSG-2024-KF-25, AUGA2160100623, AUGA2160501723)
  • Fundamental Research Funds for the Central Universities (52238002, 2022FRFK060001, DPGCSG-2024-KF-25, AUGA2160100623, AUGA2160501723)
  • Digital Power Grid Fund of CSG (52238002, 2022FRFK060001, DPGCSG-2024-KF-25, AUGA2160100623, AUGA2160501723)
  • 2023 Youth Talent Introduction Scientific Research Startup Fee (52238002, 2022FRFK060001, DPGCSG-2024-KF-25, AUGA2160100623, AUGA2160501723)
  • 2023 Support Funds for Talent Introduction in Heilongjiang Province (52238002, 2022FRFK060001, DPGCSG-2024-KF-25, AUGA2160100623, AUGA2160501723)
Topics
  • Physical Sciences › Engineering › Electrical and Electronic Engineering › Fuel Cells and Related Materials
  • Physical Sciences › Engineering › Automotive Engineering › Battery Modeling and Management
  • Physical Sciences › Engineering › Automotive Engineering › Electric and Hybrid Vehicle Technologies
Keywords
proton exchange membrane fuel cells, performance degradation prediction, transformer models, self-attention, masking mechanism, accelerated degradation

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CoChat. (2026). Citation check: The Degradation Prediction of Proton Exchange Membrane Fuel Cell Performance Based on a Transformer Model. https://app.cochat.ai/papers/10.3390/en17123050
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Meng, X., Mei, J., Tang, X., Jiang, J., Sun, C., & Song, K. (2024). The Degradation Prediction of Proton Exchange Membrane Fuel Cell Performance Based on a Transformer Model. Energies. https://doi.org/10.3390/en17123050