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An Air Over-Stoichiometry Dependent Voltage Model for HT-PEMFC MEAs

Author

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  • Sylvain Rigal

    (Institute of Technology Saint Exupéry (IcmRT Saint Exupéry), 3 Rue Tarfaya, 31400 Toulouse, France
    LAPLACE—Laboratoire Plasma et Conversion d’énergie Université de Toulouse, CNRS—Centre National de la Recherche Scientifique, INPT—Institut National Polytechnique de Toulouse, UPS—Université Paul Sabatier, 31077 Toulouse, France)

  • Amine Jaafar

    (LAPLACE—Laboratoire Plasma et Conversion d’énergie Université de Toulouse, CNRS—Centre National de la Recherche Scientifique, INPT—Institut National Polytechnique de Toulouse, UPS—Université Paul Sabatier, 31077 Toulouse, France)

  • Christophe Turpin

    (LAPLACE—Laboratoire Plasma et Conversion d’énergie Université de Toulouse, CNRS—Centre National de la Recherche Scientifique, INPT—Institut National Polytechnique de Toulouse, UPS—Université Paul Sabatier, 31077 Toulouse, France)

  • Théophile Hordé

    (Airbus, 31703 Blagnac, France)

  • Jean-Baptiste Jollys

    (Alstom, 50 Rue du Dr Guinier, 65600 Tarbes, France)

  • Paul Kreczanik

    (Institute of Technology Saint Exupéry (IcmRT Saint Exupéry), 3 Rue Tarfaya, 31400 Toulouse, France)

Abstract

In this work, three commercially available Membrane Electrode Assemblies (MEAs) from Advent Technology Inc. and Danish Power Systems, developed for a use in High Temperature Proton Exchange Membrane Fuel Cell (HT-PEMFC), were tested under various Operating Conditions (OCs): over-stoichiometry of hydrogen gas (1.05, 1.2, 1.35), over-stoichiometry of air gas (1.5, 2, 2.5), gas oxidant (O 2 or air) and temperature (140 °C, 160 °C, 180 °C). For each set of operating conditions, a polarization curve (V–I curve) was performed. A semi-empirical and macroscopic (0D) model of the fuel cell voltage was established in steady state conditions in order to model some of these experimental data. The proposed parameterization approach for this model (called here the “multi-VI” approach) is based on the sensitivity to the operating conditions specific to each involved physicochemical phenomenon. According to this method, only one set of parameters is used in order to model all the experimental curves (optimization is performed simultaneously on all curves). A model depending on air over-stoichiometry was developed. The main objective is to validate a simple (0D) and fast-running model that considers the impact of air over-stoichiometry on cell voltage regarding all commercially available MEAs. The obtained results are satisfying with Advent PBI MEA and Danish Power Systems MEA: an average error less than 1.5% and a maximum error around 15% between modelled and measured voltages with only nine parameters to identify. However, the model was not as adapted to Advent TPS ® MEA: average error and maximum error around 4% and 21%, respectively. Most of the obtained parameters appear consistent regardless of the OCs. The predictability of the model was also validated in the explored domain during the sensibility study with an interesting accuracy for 27 OCs after being trained on only nine curves. This is attractive for industrial application, since it reduces the number of experiments, hence the cost of model development, and is potentially applicable to all commercial HT-PEMFC MEAs.

Suggested Citation

  • Sylvain Rigal & Amine Jaafar & Christophe Turpin & Théophile Hordé & Jean-Baptiste Jollys & Paul Kreczanik, 2024. "An Air Over-Stoichiometry Dependent Voltage Model for HT-PEMFC MEAs," Energies, MDPI, vol. 17(12), pages 1-17, June.
    • Handle: RePEc:gam:jeners:v:17:y:2024:i:12:p:3002-:d:1417210
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    References listed on IDEAS

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    1. Singdeo, Debanand & Dey, Tapobrata & Gaikwad, Shrihari & Andreasen, Søren Juhl & Ghosh, Prakash C., 2017. "A new modified-serpentine flow field for application in high temperature polymer electrolyte fuel cell," Applied Energy, Elsevier, vol. 195(C), pages 13-22.
    2. Wang, Xuechao & Chen, Jinzhou & Quan, Shengwei & Wang, Ya-Xiong & He, Hongwen, 2020. "Hierarchical model predictive control via deep learning vehicle speed predictions for oxygen stoichiometry regulation of fuel cells," Applied Energy, Elsevier, vol. 276(C).
    3. Cheng, Shan-Jen & Miao, Jr-Ming & Wu, Sheng-Ju, 2012. "Investigating the effects of operational factors on PEMFC performance based on CFD simulations using a three-level full-factorial design," Renewable Energy, Elsevier, vol. 39(1), pages 250-260.
    4. Bing Xu & Dongxu Li & Zheshu Ma & Meng Zheng & Yanju Li, 2021. "Thermodynamic Optimization of a High Temperature Proton Exchange Membrane Fuel Cell for Fuel Cell Vehicle Applications," Mathematics, MDPI, vol. 9(15), pages 1-14, July.
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