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Simulation of polymer electrolyte membrane fuel cell degradation using an integrated Petri Net and 0D model

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  • Whiteley, M
  • Dunnett, S
  • Jackson, L

Abstract

Establishing accurate predictions for the reliability of fuel cell operation is critical for comparative performance evaluation of these new with existing power generation mechanisms. Fuel cells, in particular polymer electrolyte fuel cells (PEMFCs), are an emerging technology to potential replace internal combustion engines with the benefits of reducing carbon emissions. Current issues relate to modelling of the degradation mechanisms limiting subsequent accurate reliability prediction. Though common reliability techniques such as Failure Mode and Effect Analysis (FMEA) have been used to further the understanding of the failure modes within the fuel cell system and Fault Tree Analysis (FTA) used to quantify the likelihood of a reduction in performance via voltage drop due to failures, modelling system level reliability and degradation still needs more research. The inherent complexity of a PEMFC system assembly, harbouring dependencies between multiple failure modes, limits the accuracy of FTA. This paper presents a comprehensive Petri-Net model integrated with a 0-D fuel cell performance model of the fuel cell system to develop a more accurate degradation model. The results show the applicability of this novel hybrid method for reliability analysis, in this instance with application to PEMFCs, but with merit for application in other degradation domains.

Suggested Citation

  • Whiteley, M & Dunnett, S & Jackson, L, 2020. "Simulation of polymer electrolyte membrane fuel cell degradation using an integrated Petri Net and 0D model," Reliability Engineering and System Safety, Elsevier, vol. 196(C).
    • Handle: RePEc:eee:reensy:v:196:y:2020:i:c:s0951832017311560
    DOI: 10.1016/j.ress.2019.106741
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    References listed on IDEAS

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    1. Andrews, John & Prescott, Darren & De Rozières, Florian, 2014. "A stochastic model for railway track asset management," Reliability Engineering and System Safety, Elsevier, vol. 130(C), pages 76-84.
    2. Tanrioven, M. & Alam, M.S., 2006. "Reliability modeling and analysis of stand-alone PEM fuel cell power plants," Renewable Energy, Elsevier, vol. 31(7), pages 915-933.
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    Cited by:

    1. 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).
    2. Zeng, Zhiguo & Barros, Anne & Coit, David, 2023. "Dependent failure behavior modeling for risk and reliability: A systematic and critical literature review," Reliability Engineering and System Safety, Elsevier, vol. 239(C).
    3. Lorenzo, Charles & Bouquain, David & Hibon, Samuel & Hissel, Daniel, 2021. "Synthesis of degradation mechanisms and of their impacts on degradation rates on proton-exchange membrane fuel cells and lithium-ion nickel–manganese–cobalt batteries in hybrid transport applicati," Reliability Engineering and System Safety, Elsevier, vol. 212(C).

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