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Remaining useful life prediction of PEMFC systems based on the multi-input echo state network

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  • Hua, Zhiguang
  • Zheng, Zhixue
  • Péra, Marie-Cécile
  • Gao, Fei

Abstract

The limited durability is one of the key barriers of Proton Exchange Membrane Fuel Cell (PEMFC) to large-scale commercial applications. The data-driven prognostic method aims to estimate the Remaining Useful Life (RUL) without the need for complete knowledge about the system’s physical phenomena. As an improved structure of the recurrent neural network, the Echo State Network (ESN) has demonstrated better performances, especially in reducing the computational complexity and accelerating the convergence rate. The traditional prognostic methods utilize only the previous state, e.g. stack voltage, for prediction. Nevertheless, the current operating conditions, such as stack current, stack temperature and the pressures of the reactants (i.e. oxygen and hydrogen) can also contain important degradation information in practice. Especially, the stack current is a crucial operating parameter, since it is normally taken as the scheduling variable and it could reflect the operating conditions. Compared with the single-input and single-output (SISO-ESN) structure, the ESN with multiple inputs and multiple outputs (MIMO-ESN) is proposed in this paper to improve the RUL prediction accuracy. Stack voltage, stack current, stack temperature and the pressures of the reactants are combinedly used to predict the RUL. After the mathematical modeling and the parameter designing, the prediction performance of SISO-ESN and MIMO-ESN are verified and compared on a 1 kW electrical power test bench developed in the laboratory. Results show that the MIMO-ESN method has a better performance than the SISO-ESN method under both static and quasi-dynamic operating conditions.

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  • Hua, Zhiguang & Zheng, Zhixue & Péra, Marie-Cécile & Gao, Fei, 2020. "Remaining useful life prediction of PEMFC systems based on the multi-input echo state network," Applied Energy, Elsevier, vol. 265(C).
    • Handle: RePEc:eee:appene:v:265:y:2020:i:c:s0306261920303032
    DOI: 10.1016/j.apenergy.2020.114791
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    Cited by:

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    7. Tianxiang Wang & Hongliang Zhou & Chengwei Zhu, 2022. "A Short-Term and Long-Term Prognostic Method for PEM Fuel Cells Based on Gaussian Process Regression," Energies, MDPI, vol. 15(13), pages 1-17, July.
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    10. Li, Haolong & Chen, Qihong & Zhang, Liyan & Liu, Li & Xiao, Peng, 2023. "Degradation prediction of proton exchange membrane fuel cell based on the multi-inputs Bi-directional long short-term memory," Applied Energy, Elsevier, vol. 344(C).
    11. Aihua Tang & Yuanhang Yang & Quanqing Yu & Zhigang Zhang & Lin Yang, 2022. "A Review of Life Prediction Methods for PEMFCs in Electric Vehicles," Sustainability, MDPI, vol. 14(16), pages 1-18, August.
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    14. Qian, Zhang & Hongwei, Wang & Chunlei, Liu & Yi, An, 2024. "Establishment and identification of MIMO fractional Hammerstein model with colored noise for PEMFC system," Chaos, Solitons & Fractals, Elsevier, vol. 180(C).
    15. Yue, Meiling & Jemei, Samir & Zerhouni, Noureddine & Gouriveau, Rafael, 2021. "Proton exchange membrane fuel cell system prognostics and decision-making: Current status and perspectives," Renewable Energy, Elsevier, vol. 179(C), pages 2277-2294.
    16. Deng, Huiwen & Hu, Weihao & Cao, Di & Chen, Weirong & Huang, Qi & Chen, Zhe & Blaabjerg, Frede, 2022. "Degradation trajectories prognosis for PEM fuel cell systems based on Gaussian process regression," Energy, Elsevier, vol. 244(PA).
    17. Zhang, Zhendong & Wang, Ya-Xiong & He, Hongwen & Sun, Fengchun, 2021. "A short- and long-term prognostic associating with remaining useful life estimation for proton exchange membrane fuel cell," Applied Energy, Elsevier, vol. 304(C).
    18. Gao, Qinxiang & Lei, Tao & Yao, Wenli & Zhang, Xingyu & Zhang, Xiaobin, 2023. "A health-aware energy management strategy for fuel cell hybrid electric UAVs based on safe reinforcement learning," Energy, Elsevier, vol. 283(C).
    19. Lv, Jianfeng & Shen, Xiaoning & Gao, Yabin & Liu, Jianxing & Sun, Guanghui, 2024. "The seasonal-trend disentangle based prognostic framework for PEM fuel cells," Renewable Energy, Elsevier, vol. 228(C).
    20. Yang, Yang & Yu, Xiaoran & Zhu, Wenchao & Xie, Changjun & Zhao, Bo & Zhang, Leiqi & Shi, Ying & Huang, Liang & Zhang, Ruiming, 2023. "Degradation prediction of proton exchange membrane fuel cells with model uncertainty quantification," Renewable Energy, Elsevier, vol. 219(P2).
    21. Jinrong Yang & Yichun Wu & Xingyang Liu, 2023. "Proton Exchange Membrane Fuel Cell Power Prediction Based on Ridge Regression and Convolutional Neural Network Data-Driven Model," Sustainability, MDPI, vol. 15(14), pages 1-31, July.
    22. Zuo, Jian & Steiner, Nadia Yousfi & Li, Zhongliang & Hissel, Daniel, 2024. "Health management review for fuel cells: Focus on action phase," Renewable and Sustainable Energy Reviews, Elsevier, vol. 201(C).
    23. Chen, Kui & Badji, Abderrezak & Laghrouche, Salah & Djerdir, Abdesslem, 2022. "Polymer electrolyte membrane fuel cells degradation prediction using multi-kernel relevance vector regression and whale optimization algorithm," Applied Energy, Elsevier, vol. 318(C).
    24. Wu, Jinglai & Zhang, Yunqing & Ruan, Jiageng & Liang, Zhaowen & Liu, Kai, 2023. "Rule and optimization combined real-time energy management strategy for minimizing cost of fuel cell hybrid electric vehicles," Energy, Elsevier, vol. 285(C).

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