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Modelling of proton exchange membrane fuel cell performance based on semi-empirical equations

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  • Al-Baghdadi, Maher A.R. Sadiq

Abstract

Using semi-empirical equations for modeling a proton exchange membrane fuel cell is proposed for providing a tool for the design and analysis of fuel cell total systems. The focus of this study is to derive an empirical model including process variations to estimate the performance of fuel cell without extensive calculations. The model take into account not only the current density but also the process variations, such as the gas pressure, temperature, humidity, and utilization to cover operating processes, which are important factors in determining the real performance of fuel cell. The modelling results are compared well with known experimental results. The comparison shows good agreements between the modeling results and the experimental data. The model can be used to investigate the influence of process variables for design optimization of fuel cells, stacks, and complete fuel cell power system.

Suggested Citation

  • 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.
    • Handle: RePEc:eee:renene:v:30:y:2005:i:10:p:1587-1599
    DOI: 10.1016/j.renene.2004.11.015
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    Cited by:

    1. Gomes, R.S. & De Bortoli, A.L., 2016. "A three-dimensional mathematical model for the anode of a direct ethanol fuel cell," Applied Energy, Elsevier, vol. 183(C), pages 1292-1301.
    2. Rezk, Hegazy & Dousoky, Gamal M., 2016. "Technical and economic analysis of different configurations of stand-alone hybrid renewable power systems – A case study," Renewable and Sustainable Energy Reviews, Elsevier, vol. 62(C), pages 941-953.
    3. Theofilos Kamperidis & Asimina Tremouli & Antonis Peppas & Gerasimos Lyberatos, 2022. "A 2D Modelling Approach for Predicting the Response of a Two-Chamber Microbial Fuel Cell to Substrate Concentration and Electrolyte Conductivity Changes," Energies, MDPI, vol. 15(4), pages 1-15, February.
    4. Valverde-Isorna, L. & Ali, D. & Hogg, D. & Abdel-Wahab, M., 2016. "Modelling the performance of wind–hydrogen energy systems: Case study the Hydrogen Office in Scotland/UK," Renewable and Sustainable Energy Reviews, Elsevier, vol. 53(C), pages 1313-1332.
    5. Haji, Shaker, 2011. "Analytical modeling of PEM fuel cell i–V curve," Renewable Energy, Elsevier, vol. 36(2), pages 451-458.
    6. Rezk, Hegazy & Aly, Mokhtar & Fathy, Ahmed, 2021. "A novel strategy based on recent equilibrium optimizer to enhance the performance of PEM fuel cell system through optimized fuzzy logic MPPT," Energy, Elsevier, vol. 234(C).
    7. Hegazy Rezk & Ahmed Fathy, 2020. "Performance Improvement of PEM Fuel Cell Using Variable Step-Size Incremental Resistance MPPT Technique," Sustainability, MDPI, vol. 12(14), pages 1-16, July.
    8. Zuo, Jian & Lv, Hong & Zhou, Daming & Xue, Qiong & Jin, Liming & Zhou, Wei & Yang, Daijun & Zhang, Cunman, 2021. "Deep learning based prognostic framework towards proton exchange membrane fuel cell for automotive application," Applied Energy, Elsevier, vol. 281(C).
    9. 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.
    10. Hannan, M.A. & Azidin, F.A. & Mohamed, A., 2014. "Hybrid electric vehicles and their challenges: A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 29(C), pages 135-150.
    11. Fathy, Ahmed & Elaziz, Mohamed Abd & Alharbi, Abdullah G., 2020. "A novel approach based on hybrid vortex search algorithm and differential evolution for identifying the optimal parameters of PEM fuel cell," Renewable Energy, Elsevier, vol. 146(C), pages 1833-1845.
    12. Apostolou, Dimitrios, 2020. "Optimisation of a hydrogen production – storage – re-powering system participating in electricity and transportation markets. A case study for Denmark," Applied Energy, Elsevier, vol. 265(C).
    13. Mohamed Tolba & Hegazy Rezk & Ahmed A. Zaki Diab & Mujahed Al-Dhaifallah, 2018. "A Novel Robust Methodology Based Salp Swarm Algorithm for Allocation and Capacity of Renewable Distributed Generators on Distribution Grids," Energies, MDPI, vol. 11(10), pages 1-34, September.
    14. Liu, Jia Xing & Guo, Hang & Ye, Fang & Ma, Chong Fang, 2017. "Two-dimensional analytical model of a proton exchange membrane fuel cell," Energy, Elsevier, vol. 119(C), pages 299-308.
    15. Mohamed Louzazni & Sameer Al-Dahidi & Marco Mussetta, 2020. "Fuel Cell Characteristic Curve Approximation Using the Bézier Curve Technique," Sustainability, MDPI, vol. 12(19), pages 1-23, October.

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