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Hybrid Ageing Model of a Proton Exchange Membrane Fuel Cell (PEMFC)

Received: 8 December 2021    Accepted: 21 December 2021    Published: 25 February 2022
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Abstract

Today the world is full of time-dependent phenomena in all fields: physics, chemistry, mechanics and many others. Time acts on the performance of any system whatever its nature is. When a system operates over time, aging becomes a real concern. Regarding fuel cells, several degradation phenomena can occur in a short or long term. Short-term phenomena are generally referred to as reversible degradations, these degradations are of the order of the microsecond and can go up to hours and sometimes to days, such as problems related to water management. The long term degradations are usually called irreversible degradations; it can be defined as aging. This phenomenon is of the order of a day and can increase up to months. In order to mitigate the impact of aging on the fuel cell system performance, good corrective actions must be taken. To do so, the performance prediction during the aging of the fuel cell system must be conducted. In this paper a verified and tested prognosis approach applicable to fuel cells is presented. The novelty in the approach used is linked to its modular structure. In fact, this approach has three phases. The first one aims to identify the internal physical parameters of the stack using an optimization algorithm on experimental data and a static fuel cell model. The second one predicts the temporal evolution of these parameters using a prediction algorithm. Finally, the reconstruction phase consists in the prediction of parameters that are re-injected into the model to reconstruct polarization curves and thus reflect the performance degradation of the fuel cell. The reliability and repeatability of the proposed approach have been successfully validated on two data sets from two different experimental campaigns.

Published in International Journal of Energy and Power Engineering (Volume 11, Issue 1)
DOI 10.11648/j.ijepe.20221101.12
Page(s) 17-29
Creative Commons

This is an Open Access article, distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution and reproduction in any medium or format, provided the original work is properly cited.

Copyright

Copyright © The Author(s), 2024. Published by Science Publishing Group

Keywords

Proton Exchange Membrane Fuel Cell, Aging, Prognosis, ARMA Model, Stack Internal Parameters

References
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Cite This Article
  • APA Style

    Abdelkader Detti, Nadia Yousfi Steiner, Laurent Bouillaut, Allou Badara Samé, Samir Jemei. (2022). Hybrid Ageing Model of a Proton Exchange Membrane Fuel Cell (PEMFC). International Journal of Energy and Power Engineering, 11(1), 17-29. https://doi.org/10.11648/j.ijepe.20221101.12

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    ACS Style

    Abdelkader Detti; Nadia Yousfi Steiner; Laurent Bouillaut; Allou Badara Samé; Samir Jemei. Hybrid Ageing Model of a Proton Exchange Membrane Fuel Cell (PEMFC). Int. J. Energy Power Eng. 2022, 11(1), 17-29. doi: 10.11648/j.ijepe.20221101.12

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    AMA Style

    Abdelkader Detti, Nadia Yousfi Steiner, Laurent Bouillaut, Allou Badara Samé, Samir Jemei. Hybrid Ageing Model of a Proton Exchange Membrane Fuel Cell (PEMFC). Int J Energy Power Eng. 2022;11(1):17-29. doi: 10.11648/j.ijepe.20221101.12

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  • @article{10.11648/j.ijepe.20221101.12,
      author = {Abdelkader Detti and Nadia Yousfi Steiner and Laurent Bouillaut and Allou Badara Samé and Samir Jemei},
      title = {Hybrid Ageing Model of a Proton Exchange Membrane Fuel Cell (PEMFC)},
      journal = {International Journal of Energy and Power Engineering},
      volume = {11},
      number = {1},
      pages = {17-29},
      doi = {10.11648/j.ijepe.20221101.12},
      url = {https://doi.org/10.11648/j.ijepe.20221101.12},
      eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.ijepe.20221101.12},
      abstract = {Today the world is full of time-dependent phenomena in all fields: physics, chemistry, mechanics and many others. Time acts on the performance of any system whatever its nature is. When a system operates over time, aging becomes a real concern. Regarding fuel cells, several degradation phenomena can occur in a short or long term. Short-term phenomena are generally referred to as reversible degradations, these degradations are of the order of the microsecond and can go up to hours and sometimes to days, such as problems related to water management. The long term degradations are usually called irreversible degradations; it can be defined as aging. This phenomenon is of the order of a day and can increase up to months. In order to mitigate the impact of aging on the fuel cell system performance, good corrective actions must be taken. To do so, the performance prediction during the aging of the fuel cell system must be conducted. In this paper a verified and tested prognosis approach applicable to fuel cells is presented. The novelty in the approach used is linked to its modular structure. In fact, this approach has three phases. The first one aims to identify the internal physical parameters of the stack using an optimization algorithm on experimental data and a static fuel cell model. The second one predicts the temporal evolution of these parameters using a prediction algorithm. Finally, the reconstruction phase consists in the prediction of parameters that are re-injected into the model to reconstruct polarization curves and thus reflect the performance degradation of the fuel cell. The reliability and repeatability of the proposed approach have been successfully validated on two data sets from two different experimental campaigns.},
     year = {2022}
    }
    

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  • TY  - JOUR
    T1  - Hybrid Ageing Model of a Proton Exchange Membrane Fuel Cell (PEMFC)
    AU  - Abdelkader Detti
    AU  - Nadia Yousfi Steiner
    AU  - Laurent Bouillaut
    AU  - Allou Badara Samé
    AU  - Samir Jemei
    Y1  - 2022/02/25
    PY  - 2022
    N1  - https://doi.org/10.11648/j.ijepe.20221101.12
    DO  - 10.11648/j.ijepe.20221101.12
    T2  - International Journal of Energy and Power Engineering
    JF  - International Journal of Energy and Power Engineering
    JO  - International Journal of Energy and Power Engineering
    SP  - 17
    EP  - 29
    PB  - Science Publishing Group
    SN  - 2326-960X
    UR  - https://doi.org/10.11648/j.ijepe.20221101.12
    AB  - Today the world is full of time-dependent phenomena in all fields: physics, chemistry, mechanics and many others. Time acts on the performance of any system whatever its nature is. When a system operates over time, aging becomes a real concern. Regarding fuel cells, several degradation phenomena can occur in a short or long term. Short-term phenomena are generally referred to as reversible degradations, these degradations are of the order of the microsecond and can go up to hours and sometimes to days, such as problems related to water management. The long term degradations are usually called irreversible degradations; it can be defined as aging. This phenomenon is of the order of a day and can increase up to months. In order to mitigate the impact of aging on the fuel cell system performance, good corrective actions must be taken. To do so, the performance prediction during the aging of the fuel cell system must be conducted. In this paper a verified and tested prognosis approach applicable to fuel cells is presented. The novelty in the approach used is linked to its modular structure. In fact, this approach has three phases. The first one aims to identify the internal physical parameters of the stack using an optimization algorithm on experimental data and a static fuel cell model. The second one predicts the temporal evolution of these parameters using a prediction algorithm. Finally, the reconstruction phase consists in the prediction of parameters that are re-injected into the model to reconstruct polarization curves and thus reflect the performance degradation of the fuel cell. The reliability and repeatability of the proposed approach have been successfully validated on two data sets from two different experimental campaigns.
    VL  - 11
    IS  - 1
    ER  - 

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Author Information
  • Franche-Comté Electronics Mechanics Thermal Science and Optics – Sciences and Technologies, University of Bourgogne Franche-Comte, Belfort, France; Fuel Cell Laboratory, University of Bourgogne Franche-Comte, Belfort, France

  • Franche-Comté Electronics Mechanics Thermal Science and Optics – Sciences and Technologies, University of Bourgogne Franche-Comte, Belfort, France; Fuel Cell Laboratory, University of Bourgogne Franche-Comte, Belfort, France

  • Engineering of Surface Transportation Networks and Advanced Computing Laboratory, University Gustave Eiffel, Marne-la-Vallée, France

  • Engineering of Surface Transportation Networks and Advanced Computing Laboratory, University Gustave Eiffel, Marne-la-Vallée, France

  • Franche-Comté Electronics Mechanics Thermal Science and Optics – Sciences and Technologies, University of Bourgogne Franche-Comte, Belfort, France; Fuel Cell Laboratory, University of Bourgogne Franche-Comte, Belfort, France

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