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The Degradation Prediction of Proton Exchange Membrane Fuel Cell Performance Based on a Transformer Model
Open accessAbstract
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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“The Fenton reagent is commonly used in the accelerated stressed test, which can be utilized for chemical stability evaluation of PEMs to simulate the real-time chemical degradation.”
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Research works printed but not linked: 30
- Yan, S., Yang, M., Sun, C., and Xu, S. (2023). Liquid water characteristics in the compressed gradient porosity gas diffusion layer of proton exchange membrane fuel cells using the lattice boltzmann method. Energies, 16. · DOI
- Zhang, X., Yu, Z., and Chen, W. (2019). Life prediction based on ds elm for pemfc. Energies, 12. · DOI
- Barzegari, 2019, Performance prediction and analysis of a dead-end pemfc stack using data-driven dynamic model, Energy, 188, 116049, 10.1016/j.energy.2019.116049 · DOI
- Tang, 2024, Adaptive state-of-health temperature sensitivity characteristics for durability improvement of pem fuel cells, Chem. Eng. J., 491, 151951, 10.1016/j.cej.2024.151951 · DOI
- Ma, 2021, A hybrid prognostic method for pemfc with aging parameter prediction, IEEE Trans. Transp. Electrif., 7, 2318, 10.1109/TTE.2021.3075531 · DOI
- Li, H., Liu, L., Zhang, L., Xie, L., and Chen, Q. A fusion intelligent degradation interval prediction method based on hybrid health indicator for proton exchange membrane fuel cells. IEEE Trans. Transp. Electrif., 2024. in press. · DOI
- Zhang, 2021, Cost-effective iron-based aqueous redox flow batteries for large-scale energy storage application: A review, J. Power Sources, 493, 229445, 10.1016/j.jpowsour.2020.229445 · DOI
- Tang, 2023, Temperature sensitivity characteristics of pem fuel cell and output performance improvement based on optimal active temperature control, Int. J. Heat Mass Transf., 206, 123966, 10.1016/j.ijheatmasstransfer.2023.123966 · DOI
- Rao, M., Wang, L., Chen, C., Xiong, K., Li, M., Chen, Z., Dong, J., Xu, J., and Li, X. (2022). Data-driven state prediction and analysis of sofc system based on deep learning method. Energies, 15. · DOI
- Kanani, 2015, Model development and optimization of operating conditions to maximize pemfc performance by response surface methodology, Energy Convers. Manag., 93, 9, 10.1016/j.enconman.2014.12.093 · DOI
- Wang, T., Zhou, H., and Zhu, C. (2022). A short-term and long-term prognostic method for pem fuel cells based on gaussian process regression. Energies, 15. · DOI
- Tang, 2023, Experimental study of pem fuel cell temperature characteristic and corresponding automated optimal temperature calibration model, Energy, 283, 128456, 10.1016/j.energy.2023.128456 · DOI
- Mayur, M., Gerard, M., Schott, P., and Bessler, W.G. (2018). Lifetime prediction of a polymer electrolyte membrane fuel cell under automotive load cycling using a physically-based catalyst degradation model. Energies, 11. · DOI
- Xiao, 2024, Transport properties evaluation of pore-scale gdls for pemfc using orthogonal design method, Appl. Energy, 357, 122445, 10.1016/j.apenergy.2023.122445 · DOI
- Jia, 2024, A performance degradation prediction model for pemfc based on bi-directional long short-term memory and multi-head self-attention mechanism, Int. J. Hydrogen Energy, 60, 133, 10.1016/j.ijhydene.2024.02.181 · DOI
- Zhao, 2024, Development of practical empirically and statistically-based equations for predicting the temperature characteristics of pemfc applied in the cchp system, Int. J. Hydrogen Energy, 52, 894, 10.1016/j.ijhydene.2022.12.180 · DOI
- Hua, 2022, A review on lifetime prediction of proton exchange membrane fuel cells system, J. Power Sources, 529, 231256, 10.1016/j.jpowsour.2022.231256 · DOI
- Chandesris, 2017, Membrane degradation in pem fuel cells: From experimental results to semi-empirical degradation laws, Int. J. Hydrogen Energy, 42, 8139, 10.1016/j.ijhydene.2017.02.116 · DOI · Paper page
- Ou, 2021, A novel approach based on semi-empirical model for degradation prediction of fuel cells, J. Power Sources, 488, 229435, 10.1016/j.jpowsour.2020.229435 · DOI
- Bressel, 2016, Extended kalman filter for prognostic of proton exchange membrane fuel cell, Appl. Energy, 164, 220, 10.1016/j.apenergy.2015.11.071 · DOI
- Wang, 2022, A novel long short-term memory networks-based data-driven prognostic strategy for proton exchange membrane fuel cells, Int. J. Hydrogen Energy, 47, 10395, 10.1016/j.ijhydene.2022.01.121 · DOI
- Zuo, 2021, Deep learning based prognostic framework towards proton exchange membrane fuel cell for automotive application, Appl. Energy, 281, 115937, 10.1016/j.apenergy.2020.115937 · DOI
- Benaggoune, 2022, A data-driven method for multi-step-ahead prediction and long-term prognostics of proton exchange membrane fuel cell, Appl. Energy, 313, 118835, 10.1016/j.apenergy.2022.118835 · DOI
- Mezzi, 2021, An echo state network for fuel cell lifetime prediction under a dynamic micro-cogeneration load profile, Appl. Energy, 283, 116297, 10.1016/j.apenergy.2020.116297 · DOI
- Li, 2019, Adaptive prognostic of fuel cells by implementing ensemble echo state networks in time-varying model space, IEEE Trans. Ind. Electron., 67, 379, 10.1109/TIE.2019.2893827 · DOI
- Zhou, 2017, Degradation prediction of pem fuel cell using a moving window based hybrid prognostic approach, Energy, 138, 1175, 10.1016/j.energy.2017.07.096 · DOI
- Cheng, 2018, A hybrid remaining useful life prognostic method for proton exchange membrane fuel cell, Int. J. Hydrogen Energy, 43, 12314, 10.1016/j.ijhydene.2018.04.160 · DOI
- Gouriveau, R., Hilairet, M., Hissel, D., Jemei, S., Jouin, M., Lechartier, E., Morando, S., Pahon, E., Pera, M.C., and Zerhouni, N. (2014, January 22–25). IEEE phm 2014 data challenge: Outline, experiments, scoring of results, winners. Proceedings of the IEEE Conference on Prognostics and Health Management, Cheney, WA, USA.
- Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Polosukhin, I., and Kaiser, Ł. (2017). Attention is all you need. Adv. Neural Inf. Process. Syst., 30.
- Tang, X., Shi, L., Zhang, Y., Li, B., Xu, S., and Song, Z. Degradation adaptive energy management strategy for FCHEV based on the Rule-DDPG method: Tailored to the current SOH of the powertrain. IEEE Trans. Transp. Electrif., 2024. in press. · DOI
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Song, Y., Guo, Z., Yin, J., Liu, M., Tolj, I., Grigoriev, S. A., Ge, M., & Sun, C. (2025) · Polymers · doi:10.3390/polym17162181 · Cited by 28
Self-citation
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
- Venue
- Energies
- Publisher
- MDPI AG
- Published
- 2024-06-20
- Volume
- 17
- Issue
- 12
- Pages
- 3050
- ISSN
- 1996-1073 (ISSN-L)
- 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
- gold
- doi.org
- 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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