journal article·2026-06

Sensitivity analysis of a CNN–LSTM prognostic framework for proton exchange membrane fuel cells: Effects of sliding window size and training data allocation

Qin Lin, Liang Hu, Wenmiao Liu, Xiaomin Tang, Yuhao Wang, Zhibin Yang

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OpenAlex topics: Fuel Cells and Related Materials · Advanced Battery Technologies Research · Electrocatalysts for Energy Conversion

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References (30)

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1. C. Raga, A. Barrado, A. Lázaro, et al., Black-box model, identification technique and frequency analysis for PEM fuel cell with overshooted transient response, IEEE Trans. Power Electron., 29(2014), No. 10, p. 5334.10.1109/TPEL.2013.2292599 ↗
2. R. Petrone, Z. Zheng, D. Hissel, et al., A review on model-based diagnosis methodologies for PEMFCs, Int. J. Hydrog. Energy, 38(2013), No. 17, p. 7077.10.1016/j.ijhydene.2013.03.106 ↗
3. X.Y. Li, Z.M. Zhang, X.X. Zhuang, Z.T. Jia, and T. Wang, Electrolyte effects in electrocatalytic kinetics, Chin. J. Chem., 42(2024), No. 24, p. 3533.10.1002/cjoc.202400458 ↗
4. J.M. Liu, Q. Hu, S. Sabola, Y. Zhang, B. Du, and X.Z. Wang, Comparative review of corrosion-resistant coatings on metal bipolar plates of proton exchange membrane fuel cells, Int. J. Miner. Metall. Mater., 31(2024), No. 12, p. 2627.10.1007/s12613-024-2946-0 ↗
5. J.H. Zhang, X.J. Luo, Y.Y. Ding, L.Q. Chang, and C.F. Dong, Effect of bipolar-plates design on corrosion, mass and heat transfer in proton-exchange membrane fuel cells and water electrolyzers: A review, Int. J. Miner. Metall. Mater., 31(2024), No. 7, p. 1599.10.1007/s12613-023-2803-6 ↗
6. X.T. Guo, X.N. Jiang, R. Luo, C. Su, and T. Wei, Bimetallic-MOF derivatives enabled 3D carbon cloth as a lithium sponge for a high-performance lithium-metal anode, Chem. Commun., 61(2025), No. 69, p. 12956.10.1039/D5CC02920A ↗
7. F.Y. Cai, S.S. Cai, and Z.K. Tu, Proton exchange membrane fuel cell (PEMFC) operation in high current density (HCD): Problem, progress and perspective, Energy Convers. Manage., 307(2024), art. No. 118348.10.1016/j.enconman.2024.118348 ↗
8. P. Choi, N.H. Jalani, T.M. Thampan, and R. Datta, Consideration of thermodynamic, transport, and mechanical properties in the design of polymer electrolyte membranes for higher temperature fuel cell operation, J. Polym. Sci. Part B: Polym. Phys., 44(2006), No. 16, p. 2183.10.1002/polb.20858 ↗
9. M. Kamiya, S. Saito, and I. Ohmine, Proton transfer and associated molecular rearrangements in the photocycle of photoactive yellow protein: Role of water molecular migration on the proton transfer reaction, J. Phys. Chem. B, 111(2007), No. 11, p. 2948.10.1021/jp066256u ↗
10. K.D. Kreuer, On the complexity of proton conduction phenomena, Solid State Ionics, 136(2000), p. 149.10.1016/S0167-2738(00)00301-5 ↗
11. D. Seo, S. Park, Y. Jeon, S.W. Choi, and Y.G. Shul, Physical degradation of MEA in PEM fuel cell by on/off operation under nitrogen atmosphere, Korean J. Chem. Eng., 27(2010), No. 1, p. 104.10.1007/s11814-009-0312-6 ↗
12. H.L. Tang, P.K. Shen, S.P. Jiang, F. Wang, and M. Pan, A degradation study of Nafion proton exchange membrane of PEM fuel cells, J. Power Sources, 170(2007), No. 1, p. 85.10.1016/j.jpowsour.2007.03.061 ↗
13. A.V. Virkar and Y.K. Zhou, Mechanism of catalyst degradation in proton exchange membrane fuel cells, J. Electrochem. Soc., 154(2007), No. 6, art. No. B540.10.1149/1.2722563 ↗
14. R.M. Darling and J.P. Meyers, Kinetic model of platinum dissolution in PEMFCs, J. Electrochem. Soc., 150(2003), No. 11, art. No. A1523.10.1149/1.1613669 ↗
15. E. Guilminot, A. Corcella, F. Charlot, F. Maillard, and M. Chatenet, Detection of Ptz+ ions and Pt nanoparticles inside the membrane of a used PEMFC, J. Electrochem. Soc., 154(2007), No. 1, art. No. B96.10.1149/1.2388863 ↗
16. M. Jouin, R. Gouriveau, D. Hissel, M.C. Péra, and N. Zerhouni, Prognostics and health management of PEMFC–State of the art and remaining challenges, Int. J. Hydrogen Energy, 38(2013), No. 35, p. 15307.10.1016/j.ijhydene.2013.09.051 ↗
17. P.V. Varde, J. Tian, and M.G. Pecht, Prognostics and health management based refurbishment for life extension of electronic systems, [in] 2014 IEEE International Conference on Information and Automation (ICIA), Hailar, 2014, p. 1260.10.1109/ICInfA.2014.6932842 ↗
18. Y. Peng and D.T. Liu, Data-driven prognostics and health management: A review of recent advances, Chin. J. Sci. Instrum., 35(2014), No. 3, p. 481.
19. D.D. Zhao, F. Gao, P. Massonnat, M.F. Dou, and A. Miraoui, Parameter sensitivity analysis and local temperature distribution effect for a PEMFC system, IEEE Trans. Energy Convers., 30(2015), No. 3, p. 1008.10.1109/TEC.2015.2404793 ↗
20. Z.G. Hua, Z.X. Zheng, E. Pahon, M.C. Péra, and F. Gao, A review on lifetime prediction of proton exchange membrane fuel cells system, J. Power Sources, 529(2022), art. No. 231256.10.1016/j.jpowsour.2022.231256 ↗
21. K. Chen, S. Laghrouche, and A. Djerdir, Degradation prediction of proton exchange membrane fuel cell based on grey neural network model and particle swarm optimization, Energy Convers. Manage., 195(2019), p. 810.10.1016/j.enconman.2019.05.045 ↗
22. T. Wilberforce and A.G. Olabi, Performance prediction of proton exchange membrane fuel cells (PEMFC) using adaptive neuro inference system (ANFIS), Sustainability, 12(2020), No. 12, art. No. 4952.10.3390/su12124952 ↗
23. R. Ma, Z.L. Li, E. Breaz, et al., Data-fusion prognostics of proton exchange membrane fuel cell degradation, IEEE Trans. Ind. Appl., 55(2019), No. 4, p. 4321.10.1109/TIA.2019.2911846 ↗
24. Z. Tian, Z. Wei, J.H. Wang, et al., Research progress on aging prediction methods for fuel cells: Mechanism, methods, and evaluation criteria, Energies, 16(2023), No. 23, art. No. 7750.10.3390/en16237750 ↗
25. Z.D. Zhang, Y.X. Wang, H.W. He, and F.C. Sun, A short- and long-term prognostic associating with remaining useful life estimation for proton exchange membrane fuel cell, Appl. Energy, 304(2021), art. No. 117841.10.1016/j.apenergy.2021.117841 ↗
26. W.C. Zhu, B.X. Guo, Y. Li, et al., Uncertainty quantification of proton-exchange-membrane fuel cells degradation prediction based on Bayesian-Gated Recurrent Unit, eTransportation, 16(2023), art. No. 100230.10.1016/j.etran.2023.100230 ↗
27. B. Ghojogh and A. Ghodsi, Recurrent neural networks and long short-term memory networks: Tutorial and survey, 2023. https://doi.org/10.48550/arXiv.2304.1146110.48550/arXiv.2304.11461 ↗
28. D.C. Zhao, R.P. Jiang, M.Y. Feng, et al., A deep learning algorithm based on 1D CNN-LSTM for automatic sleep staging, Technol. Health Care, 30(2022), No. 2, p. 323.10.3233/THC-212847 ↗
29. M. Ma and Z. Mao, Deep-convolution-based LSTM network for remaining useful life prediction, IEEE Trans. Ind. Inform., 17(2021), No. 3, p. 1658.10.1109/TII.2020.2991796 ↗
30. Y.Y. Ma, S.T. Li, S.L. Zhou, et al., Performance degradation prediction of proton exchange membrane fuel cells based on CNN-LSTM network with squeeze-and-excitation attention mechanism, Energy, 335(2025), art. No. 138127.10.1016/j.energy.2025.138127 ↗