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A practical model for evaluating the performance of proton exchange membrane fuel cells

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  • Moreira, Marcos V.
  • da Silva, Gisele E.

Abstract

Several models have been proposed in the literature to predict the performance of proton exchange membrane fuel cells (PEMFC). These models have different levels of complexity and can be divided basically into two groups: (i) mechanistic (theoretical); and (ii) semi-empirical models. The mechanistic models are obtained from electrochemical, thermodynamic, and fluid dynamic equations, and describes, with a high level of details, the processes in the operation of the fuel cell. The main drawback of the mechanistic approach is that, in general, the models are very complex, requiring the knowledge of parameters that are difficult to be obtained. Semi-empirical models, on the other hand, are easier to be obtained and can also be used to accurately predict the fuel cell system performance for engineering applications. In this paper, a new semi-empirical model, that is simpler than others presented in the literature, is proposed. It is derived by using semi-empirical equations and the resulting empirical coefficients are calculated through linear least squares. The model can be used in the evaluation of performance of small-distributed electrical generation systems, and also for the design of fuel cell systems for vehicles and portable electronics.

Suggested Citation

  • Moreira, Marcos V. & da Silva, Gisele E., 2009. "A practical model for evaluating the performance of proton exchange membrane fuel cells," Renewable Energy, Elsevier, vol. 34(7), pages 1734-1741.
    • Handle: RePEc:eee:renene:v:34:y:2009:i:7:p:1734-1741
    DOI: 10.1016/j.renene.2009.01.002
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    References listed on IDEAS

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    1. Al-Baghdadi, Maher A.R. Sadiq, 2005. "Modelling of proton exchange membrane fuel cell performance based on semi-empirical equations," Renewable Energy, Elsevier, vol. 30(10), pages 1587-1599.
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    Cited by:

    1. Tavakoli, B. & Roshandel, R., 2011. "The effect of fuel cell operational conditions on the water content distribution in the polymer electrolyte membrane," Renewable Energy, Elsevier, vol. 36(12), pages 3319-3331.
    2. José-Luis Casteleiro-Roca & Francisco José Vivas & Francisca Segura & Antonio Javier Barragán & Jose Luis Calvo-Rolle & José Manuel Andújar, 2020. "Hybrid Intelligent Modelling in Renewable Energy Sources-Based Microgrid. A Variable Estimation of the Hydrogen Subsystem Oriented to the Energy Management Strategy," Sustainability, MDPI, vol. 12(24), pages 1-18, December.
    3. Badreddine Kanouni & Abdelbaset Laib, 2024. "Extracting Accurate Parameters from a Proton Exchange Membrane Fuel Cell Model Using the Differential Evolution Ameliorated Meta-Heuristics Algorithm," Energies, MDPI, vol. 17(10), pages 1-21, May.
    4. S. M. Seyed Mahmoudi & Niloufar Sarabchi & Mortaza Yari & Marc A. Rosen, 2019. "Exergy and Exergoeconomic Analyses of a Combined Power Producing System including a Proton Exchange Membrane Fuel Cell and an Organic Rankine Cycle," Sustainability, MDPI, vol. 11(12), pages 1-25, June.
    5. Bai, Fan & Quan, Hong-Bing & Yin, Ren-Jie & Zhang, Zhuo & Jin, Shu-Qi & He, Pu & Mu, Yu-Tong & Gong, Xiao-Ming & Tao, Wen-Quan, 2022. "Three-dimensional multi-field digital twin technology for proton exchange membrane fuel cells," Applied Energy, Elsevier, vol. 324(C).
    6. Hou, Yongping & Shen, Caoyuan & Hao, Dong & Liu, Yanan & Wang, Hong, 2014. "A dynamic model for hydrogen consumption of fuel cell stacks considering the effects of hydrogen purge operation," Renewable Energy, Elsevier, vol. 62(C), pages 672-678.
    7. Pottmaier, D. & Melo, C.R. & Sartor, M.N. & Kuester, S. & Amadio, T.M. & Fernandes, C.A.H. & Marinha, D. & Alarcon, O.E., 2013. "The Brazilian energy matrix: From a materials science and engineering perspective," Renewable and Sustainable Energy Reviews, Elsevier, vol. 19(C), pages 678-691.
    8. Niknam, Taher & Meymand, Hamed Zeinoddini & Mojarrad, Hasan Doagou, 2011. "A practical multi-objective PSO algorithm for optimal operation management of distribution network with regard to fuel cell power plants," Renewable Energy, Elsevier, vol. 36(5), pages 1529-1544.
    9. Jinrong Yang & Yichun Wu & Xingyang Liu, 2023. "Proton Exchange Membrane Fuel Cell Power Prediction Based on Ridge Regression and Convolutional Neural Network Data-Driven Model," Sustainability, MDPI, vol. 15(14), pages 1-31, July.
    10. José-Luis Casteleiro-Roca & Antonio Javier Barragán & Francisca Segura & José Luis Calvo-Rolle & José Manuel Andújar, 2019. "Fuel Cell Output Current Prediction with a Hybrid Intelligent System," Complexity, Hindawi, vol. 2019, pages 1-10, February.
    11. Andújar, J.M. & Segura, F. & Durán, E. & Rentería, L.A., 2011. "Optimal interface based on power electronics in distributed generation systems for fuel cells," Renewable Energy, Elsevier, vol. 36(11), pages 2759-2770.
    12. Saad S Khan & Hussain Shareef & Addy Wahyudie & SN Khalid & Reza Sirjani, 2019. "Influences of ambient conditions on the performance of proton exchange membrane fuel cell using various models," Energy & Environment, , vol. 30(6), pages 1087-1110, September.

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