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A short- and long-term prognostic associating with remaining useful life estimation for proton exchange membrane fuel cell

Author

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  • Zhang, Zhendong
  • Wang, Ya-Xiong
  • He, Hongwen
  • Sun, Fengchun

Abstract

Proton exchange membrane fuel cell (PEMFC), as a promising power source, provides a feasible solution for clean and low-carbon energy systems. The durability problem restricts PEMFC application in some scenarios, which can be improved by the prognostic technology indirectly. This paper aims to develop a data-based method to implement the short-term and long-term prognostic simultaneously, and the developed long-term prognostic can be performed without future operation information. First, the short-term prognostics of five multi-step ahead forecasting strategies are proposed and compared based on a long short-term memory (LSTM) network. Results show that the multi-step input and multi-step output (MIMO) with LSTM strategy has a better performance in the short-term prognostics under the test conditions of the stationary and dynamic current. Then, the hyper-parameters of the prediction model are determined by an evolutionary algorithm. Furthermore, in the long-term prognostics regime, the variable-step long-term method is proposed and rectified by the short-term prognostics. Finally, the developed remaining useful life (RUL) prediction is compared with a model-based extended Kalman filter. The average root mean square error results for the short-term prognostics of two conditions are 0.00532 and 0.00538, respectively. The RUL estimations of two PEMFCs named FC1 and FC2 are given with 95% and 90% confidence intervals, respectively. Consequently, the proposed method can achieve acceptable accuracies in the short-term prognostic, the long-term prognostic, and the RUL prediction.

Suggested Citation

  • 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).
    • Handle: RePEc:eee:appene:v:304:y:2021:i:c:s0306261921011661
    DOI: 10.1016/j.apenergy.2021.117841
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    References listed on IDEAS

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    Cited by:

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    2. Zhuang Tian & Zheng Wei & Jinhui Wang & Yinxiang Wang & Yuwei Lei & Ping Hu & S. M. Muyeen & Daming Zhou, 2023. "Research Progress on Aging Prediction Methods for Fuel Cells: Mechanism, Methods, and Evaluation Criteria," Energies, MDPI, vol. 16(23), pages 1-21, November.
    3. Chuang Sheng & Yi Zheng & Rui Tian & Qian Xiang & Zhonghua Deng & Xiaowei Fu & Xi Li, 2023. "A Comparative Study of the Kalman Filter and the LSTM Network for the Remaining Useful Life Prediction of SOFC," Energies, MDPI, vol. 16(9), pages 1-16, April.
    4. 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.
    5. Li, Da & Zhang, Zhaosheng & Zhou, Litao & Liu, Peng & Wang, Zhenpo & Deng, Junjun, 2022. "Multi-time-step and multi-parameter prediction for real-world proton exchange membrane fuel cell vehicles (PEMFCVs) toward fault prognosis and energy consumption prediction," Applied Energy, Elsevier, vol. 325(C).
    6. Liu, Ze & Xu, Sichuan & Zhao, Honghui & Wang, Yupeng, 2022. "Durability estimation and short-term voltage degradation forecasting of vehicle PEMFC system: Development and evaluation of machine learning models," Applied Energy, Elsevier, vol. 326(C).
    7. 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).

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