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Analytical modeling of PEM fuel cell i–V curve

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  • Haji, Shaker

Abstract

The performance of a fuel cell is characterized by its i–V curve. In this study, the performance of a bench scale fuel cell stack, run on hydrogen/air, is measured experimentally for different air flow rates and temperatures. The experimental data, obtained from the 40-W proton exchange membrane fuel cell (PEMFC), are used in estimating the parameters of a completely analytical model that describes the i–V curve. The analytical model consists of the three fundamental losses experienced by a fuel cell, namely: activation, ohmic, and concentration losses. The current loss is also considered in the model. While the Tafel constants, ohmic resistance, and the concentration loss constant are estimated through regression, the limiting current density and the current loss are obtained through measurements. The effect of temperature on the fuel cell performance, exchange current density, and current loss is also investigated. Both the exchange current density and the current loss are plotted against temperature on an Arrhenius-like plot and the related parameters are estimated. The theoretical equations derived in the literature, which model fuel cell performance, are found to reasonably fit the obtained experimental data.

Suggested Citation

  • Haji, Shaker, 2011. "Analytical modeling of PEM fuel cell i–V curve," Renewable Energy, Elsevier, vol. 36(2), pages 451-458.
    • Handle: RePEc:eee:renene:v:36:y:2011:i:2:p:451-458
    DOI: 10.1016/j.renene.2010.07.007
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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. 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).
    2. Pan, Mingzhang & Li, Chao & Liao, Jinyang & Lei, Han & Pan, Chengjie & Meng, Xianpan & Huang, Haozhong, 2020. "Design and modeling of PEM fuel cell based on different flow fields," Energy, Elsevier, vol. 207(C).
    3. Akimoto, Yutaro & Okajima, Keiichi, 2021. "Simple on-board fault-detection method for proton exchange membrane fuel cell stacks using by semi-empirical curve fitting," Applied Energy, Elsevier, vol. 303(C).
    4. Hosseinzadeh, Elham & Rokni, Masoud & Rabbani, Abid & Mortensen, Henrik Hilleke, 2013. "Thermal and water management of low temperature Proton Exchange Membrane Fuel Cell in fork-lift truck power system," Applied Energy, Elsevier, vol. 104(C), pages 434-444.
    5. 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.
    6. Wu, Ziyao & Pei, Pucheng & Xu, Huachi & Jia, Xiaoning & Ren, Peng & Wang, Bozheng, 2019. "Study on the effect of membrane electrode assembly parameters on polymer electrolyte membrane fuel cell performance by galvanostatic charging method," Applied Energy, Elsevier, vol. 251(C), pages 1-1.
    7. Özçelep, Yasin & Sevgen, Selcuk & Samli, Ruya, 2020. "A study on the hydrogen consumption calculation of proton exchange membrane fuel cells for linearly increasing loads: Artificial Neural Networks vs Multiple Linear Regression," Renewable Energy, Elsevier, vol. 156(C), pages 570-578.
    8. 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.
    9. Nima Ahmadi & Sajad Rezazadeh, 2023. "An Innovative Approach to Predict the Diffusion Rate of Reactant’s Effects on the Performance of the Polymer Electrolyte Membrane Fuel Cell," Mathematics, MDPI, vol. 11(19), pages 1-25, September.
    10. Abid Rabbani & Masoud Rokni, 2014. "Modeling and Analysis of Transport Processes and Efficiency of Combined SOFC and PEMFC Systems," Energies, MDPI, vol. 7(9), pages 1-21, August.
    11. Kheirandish, Azadeh & Motlagh, Farid & Shafiabady, Niusha & Dahari, Mahidzal & Khairi Abdul Wahab, Ahmad, 2017. "Dynamic fuzzy cognitive network approach for modelling and control of PEM fuel cell for power electric bicycle system," Applied Energy, Elsevier, vol. 202(C), pages 20-31.
    12. Kim, Jungmyung & Park, Heesung, 2019. "Electrokinetic parameters of a vanadium redox flow battery with varying temperature and electrolyte flow rate," Renewable Energy, Elsevier, vol. 138(C), pages 284-291.
    13. Zhiani, Mohammad & Majidi, Somayeh & Silva, Valter Bruno & Gharibi, Hussein, 2016. "Comparison of the performance and EIS (electrochemical impedance spectroscopy) response of an activated PEMFC (proton exchange membrane fuel cell) under low and high thermal and pressure stresses," Energy, Elsevier, vol. 97(C), pages 560-567.
    14. Kim, Jungmyung & Park, Heesung, 2017. "Experimental analysis of discharge characteristics in vanadium redox flow battery," Applied Energy, Elsevier, vol. 206(C), pages 451-457.

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