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Dynamic Reliability Assessment of PEM Fuel Cell Systems

Author

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  • Vasilyev, A.
  • Andrews, J.
  • Dunnett, S.J.
  • Jackson, L.M.

Abstract

In this paper, a novel model for the dynamic reliability analysis of a polymer electrolyte membrane fuel cell system is developed to account for multi-state dynamics and ageing. The modelling approach involves the combination of physical and stochastic sub-models with shared variables. The physical model consists of deterministic calculations of the system state described by variables such as temperature, pressure, mass flow rates and voltage output. Additionally, estimated component degradation rates are also taken into account. The non-deterministic model is implemented with stochastic Petri nets which model the failures of the balance of plant components within the fuel cell system. Using this approach, the effects of the operating conditions on the reliability of the system were investigated. Monte Carlo simulations of the process highlighted a clear influence of both purging and load cycles on the longevity of the fuel cell system.

Suggested Citation

  • Vasilyev, A. & Andrews, J. & Dunnett, S.J. & Jackson, L.M., 2021. "Dynamic Reliability Assessment of PEM Fuel Cell Systems," Reliability Engineering and System Safety, Elsevier, vol. 210(C).
    • Handle: RePEc:eee:reensy:v:210:y:2021:i:c:s0951832021000971
    DOI: 10.1016/j.ress.2021.107539
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    References listed on IDEAS

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    2. D'Urso, Diego & Chiacchio, Ferdinando & Cavalieri, Salvatore & Gambadoro, Salvatore & Khodayee, Soheyl Moheb, 2024. "Predictive maintenance of standalone steel industrial components powered by a dynamic reliability digital twin model with artificial intelligence," Reliability Engineering and System Safety, Elsevier, vol. 243(C).
    3. Heng Zhang & Zhongyong Liu & Weilai Liu & Lei Mao, 2022. "Diagnosing Improper Membrane Water Content in Proton Exchange Membrane Fuel Cell Using Two-Dimensional Convolutional Neural Network," Energies, MDPI, vol. 15(12), pages 1-15, June.
    4. Xu, Yanwen & Kohtz, Sara & Boakye, Jessica & Gardoni, Paolo & Wang, Pingfeng, 2023. "Physics-informed machine learning for reliability and systems safety applications: State of the art and challenges," Reliability Engineering and System Safety, Elsevier, vol. 230(C).
    5. Zuo, Jian & Cadet, Catherine & Li, Zhongliang & Bérenguer, Christophe & Outbib, Rachid, 2024. "A deterioration-aware energy management strategy for the lifetime improvement of a multi-stack fuel cell system subject to a random dynamic load," Reliability Engineering and System Safety, Elsevier, vol. 241(C).
    6. Wang, Shunli & Wu, Fan & Takyi-Aninakwa, Paul & Fernandez, Carlos & Stroe, Daniel-Ioan & Huang, Qi, 2023. "Improved singular filtering-Gaussian process regression-long short-term memory model for whole-life-cycle remaining capacity estimation of lithium-ion batteries adaptive to fast aging and multi-curren," Energy, Elsevier, vol. 284(C).
    7. Eapen, Deepa Elizabeth & Suresh, Resmi & Patil, Sairaj & Rengaswamy, Raghunathan, 2021. "A systems engineering perspective on electrochemical energy technologies and a framework for application driven choice of technology," Renewable and Sustainable Energy Reviews, Elsevier, vol. 147(C).

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