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The parameter identification of the Nexa 1.2 kW PEMFC's model using particle swarm optimization

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  • Salim, Reem
  • Nabag, Mahmoud
  • Noura, Hassan
  • Fardoun, Abbas

Abstract

People's extensive and ignorant lifestyles impose an increasing amount of destruction on the environment, which lead to an increased governmental and research interest towards the development and use of green technology such as fuel cells. Fuel cells are recently receiving a major share of research interest due to their promising features. This paper presents an offline parameter identification approach based on particle swarm optimization (PSO) to identify the mathematical modeling parameters of the Nexa 1.2 kW proton exchange membrane fuel cell (PEMFC) system. The goal of this work is not to get a new technique in modeling, but rather to obtain a very good model of the PEMFC system using a simple and fast heuristic approach that requires minimal mathematical effort. This model can then be utilized to perform further analysis and fault diagnosis studies on PEMFCs. The proposed approach uses basic fitting to determine some of the initial values for the PSO, while the rest of the initial values are set to be chosen randomly. The developed model is then successfully validated using actual experimental data sets.

Suggested Citation

  • Salim, Reem & Nabag, Mahmoud & Noura, Hassan & Fardoun, Abbas, 2015. "The parameter identification of the Nexa 1.2 kW PEMFC's model using particle swarm optimization," Renewable Energy, Elsevier, vol. 82(C), pages 26-34.
    • Handle: RePEc:eee:renene:v:82:y:2015:i:c:p:26-34
    DOI: 10.1016/j.renene.2014.10.012
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    Citations

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    Cited by:

    1. Priya, K. & Sathishkumar, K. & Rajasekar, N., 2018. "A comprehensive review on parameter estimation techniques for Proton Exchange Membrane fuel cell modelling," Renewable and Sustainable Energy Reviews, Elsevier, vol. 93(C), pages 121-144.
    2. Hegazy Rezk & Tabbi Wilberforce & A. G. Olabi & Rania M. Ghoniem & Mohammad Ali Abdelkareem & Enas Taha Sayed, 2023. "Fuzzy Modelling and Optimization to Decide Optimal Parameters of the PEMFC," Energies, MDPI, vol. 16(12), pages 1-16, June.
    3. Bouziane, Khadidja & Khetabi, El Mahdi & Lachat, Rémy & Zamel, Nada & Meyer, Yann & Candusso, Denis, 2020. "Impact of cyclic mechanical compression on the electrical contact resistance between the gas diffusion layer and the bipolar plate of a polymer electrolyte membrane fuel cell," Renewable Energy, Elsevier, vol. 153(C), pages 349-361.
    4. Asensio, F.J. & San Martín, J.I. & Zamora, I. & Saldaña, G. & Oñederra, O., 2019. "Analysis of electrochemical and thermal models and modeling techniques for polymer electrolyte membrane fuel cells," Renewable and Sustainable Energy Reviews, Elsevier, vol. 113(C), pages 1-1.
    5. Chen, Ben & Wang, Jun & Yang, Tianqi & Cai, Yonghua & Zhang, Caizhi & Chan, Siew Hwa & Yu, Yi & Tu, Zhengkai, 2016. "Carbon corrosion and performance degradation mechanism in a proton exchange membrane fuel cell with dead-ended anode and cathode," Energy, Elsevier, vol. 106(C), pages 54-62.
    6. Lv, You & Hong, Feng & Yang, Tingting & Fang, Fang & Liu, Jizhen, 2017. "A dynamic model for the bed temperature prediction of circulating fluidized bed boilers based on least squares support vector machine with real operational data," Energy, Elsevier, vol. 124(C), pages 284-294.
    7. Amamou, A. & Kandidayeni, M. & Boulon, L. & Kelouwani, S., 2018. "Real time adaptive efficient cold start strategy for proton exchange membrane fuel cells," Applied Energy, Elsevier, vol. 216(C), pages 21-30.
    8. Rezk, Hegazy & Ferahtia, Seydali & Djeroui, Ali & Chouder, Aissa & Houari, Azeddine & Machmoum, Mohamed & Abdelkareem, Mohammad Ali, 2022. "Optimal parameter estimation strategy of PEM fuel cell using gradient-based optimizer," Energy, Elsevier, vol. 239(PC).
    9. Liu, Hongwei & Ren, He & Gu, Yajing & Lin, Yonggang & Hu, Weifei & Song, Jiajun & Yang, Jinhong & Zhu, Zengxin & Li, Wei, 2023. "Design and on-site implementation of an off-grid marine current powered hydrogen production system," Applied Energy, Elsevier, vol. 330(PB).
    10. H. Eduardo Ariza & Antonio Correcher & Carlos Sánchez & Ángel Pérez-Navarro & Emilio García, 2018. "Thermal and Electrical Parameter Identification of a Proton Exchange Membrane Fuel Cell Using Genetic Algorithm," Energies, MDPI, vol. 11(8), pages 1-15, August.
    11. Yang, Bo & Liang, Boxiao & Qian, Yucun & Zheng, Ruyi & Su, Shi & Guo, Zhengxun & Jiang, Lin, 2024. "Parameter identification of PEMFC via feedforward neural network-pelican optimization algorithm," Applied Energy, Elsevier, vol. 361(C).
    12. 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.
    13. Xu, Shuhui & Wang, Yong & Wang, Zhi, 2019. "Parameter estimation of proton exchange membrane fuel cells using eagle strategy based on JAYA algorithm and Nelder-Mead simplex method," Energy, Elsevier, vol. 173(C), pages 457-467.
    14. Espinosa-López, Manuel & Darras, Christophe & Poggi, Philippe & Glises, Raynal & Baucour, Philippe & Rakotondrainibe, André & Besse, Serge & Serre-Combe, Pierre, 2018. "Modelling and experimental validation of a 46 kW PEM high pressure water electrolyzer," Renewable Energy, Elsevier, vol. 119(C), pages 160-173.
    15. Fathy, Ahmed & Rezk, Hegazy, 2018. "Multi-verse optimizer for identifying the optimal parameters of PEMFC model," Energy, Elsevier, vol. 143(C), pages 634-644.

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