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Research Progress on Aging Prediction Methods for Fuel Cells: Mechanism, Methods, and Evaluation Criteria

Author

Listed:
  • Zhuang Tian

    (School of Astronautics, Northwestern Polytechnical University, Xi’an, 710072, China)

  • Zheng Wei

    (Shaanxi Province Aerospace and Astronautics Propulsion Research Institute Co., Ltd., National Digital Publishing Base, No. 996, Tiangu 7th Road, High-tech Zone, Xi’an 710100, China)

  • Jinhui Wang

    (Shaanxi Xuqiangrui Clean Energy Co., Ltd., Longmen National Ecological Industry Demonstration Zone in Hancheng City, Xi’an 710100, China)

  • Yinxiang Wang

    (School of Astronautics, Northwestern Polytechnical University, Xi’an, 710072, China)

  • Yuwei Lei

    (Shaanxi Province Aerospace and Astronautics Propulsion Research Institute Co., Ltd., National Digital Publishing Base, No. 996, Tiangu 7th Road, High-tech Zone, Xi’an 710100, China)

  • Ping Hu

    (Shaanxi Polytechnic Institute, 12 Wenhui West Road, Xianyang 712000, China)

  • S. M. Muyeen

    (Department of Electrical Engineering, Qatar University, Doha 2713, Qatar)

  • Daming Zhou

    (School of Astronautics, Northwestern Polytechnical University, Xi’an, 710072, China)

Abstract

Due to the non-renewable nature and pollution associated with fossil fuels, there is widespread research into alternative energy sources. As a novel energy device, a proton exchange membrane fuel cell (PEMFC) is considered a promising candidate for transportation due to its advantages, including zero carbon emissions, low noise, and high energy density. However, the commercialization of fuel cells faces a significant challenge related to aging and performance degradation during operation. In order to comprehensively address the issue of fuel cell aging and performance decline, this paper provides a detailed review of aging mechanisms and influencing factors from the perspectives of both the PEMFC system and the stack. On this basis, this paper offers targeted solutions to degradation issues stemming from various aging factors and presents research on aging prediction methods to proactively mitigate aging-related problems. Furthermore, to enhance prediction accuracy, this paper categorizes and analyzes the degradation index and accuracy evaluation criteria commonly employed in the existing fuel cell aging research. The results indicate that specific factors leading to aging-related failures are often addressed via targeted solving methods, corresponding to specific degradation indexes. The significance of this study lies in the following aspects: (1) investigating the aging factors in fuel cells and elucidating the multiple aging mechanisms occurring within fuel cells; (2) proposing preventive measures, solutions, and aging prediction methods tailored to address fuel cell aging issues comprehensively, thereby mitigating potential harm; and (3) summarizing the degradation index and accuracy evaluation standards for aging prediction, offering new perspectives for resolving fuel cell aging problems.

Suggested Citation

  • 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.
    • Handle: RePEc:gam:jeners:v:16:y:2023:i:23:p:7750-:d:1286917
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    References listed on IDEAS

    as
    1. Zuo, Jian & Lv, Hong & Zhou, Daming & Xue, Qiong & Jin, Liming & Zhou, Wei & Yang, Daijun & Zhang, Cunman, 2021. "Deep learning based prognostic framework towards proton exchange membrane fuel cell for automotive application," Applied Energy, Elsevier, vol. 281(C).
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    3. Bressel, Mathieu & Hilairet, Mickael & Hissel, Daniel & Ould Bouamama, Belkacem, 2016. "Extended Kalman Filter for prognostic of Proton Exchange Membrane Fuel Cell," Applied Energy, Elsevier, vol. 164(C), pages 220-227.
    4. Ma, Rui & Yang, Tao & Breaz, Elena & Li, Zhongliang & Briois, Pascal & Gao, Fei, 2018. "Data-driven proton exchange membrane fuel cell degradation predication through deep learning method," Applied Energy, Elsevier, vol. 231(C), pages 102-115.
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