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Improving fuel cell performance via optimal parameters identification through fuzzy logic based-modeling and optimization

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  • Tanveer, Waqas Hassan
  • Rezk, Hegazy
  • Nassef, Ahmed
  • Abdelkareem, Mohammad Ali
  • Kolosz, Ben
  • Karuppasamy, K.
  • Aslam, Jawad
  • Gilani, Syed Omer

Abstract

Improving the performance of solid oxide fuel cell via maximizing its available peak power density is a key requirement of research in the field of renewable energy. This could be achieved through identifying the optimal controlling parameters such as, the deposition instrument power (P), the temperature (T), and the electrolyte thickness (Thick). Nickel–Gadolinium Doped Ceria cermet anode films are deposited on one side of the Zirconia electrolyte by radio frequency sputtering. The sputtering plasma power is varied at 50, 100, 150, and 200 W. Lanthanum Strontium Manganite cathodes were screen-printed on the other side of the electrolyte supports to complete the configuration. Cells were electrochemically tested at various intermediate solid oxide fuel cell temperatures of 600, 700 and 800 °C using different electrolyte thicknesses. The cell’s current density, I (A/cm2) and voltage (V) and hence the power density (W/cm2) are recorded in each case. Based on the obtained experimental results, a fuzzy model is built using different control parameters. Then, the particle swarm optimization technique is used for obtaining the best parameters of the cell that maximizes its power density. The results show that by utilizing the optimized conditions, the power density can be increased to 0.39 (W/cm2), which is almost two times higher than the maximum power density obtained experimentally.

Suggested Citation

  • Tanveer, Waqas Hassan & Rezk, Hegazy & Nassef, Ahmed & Abdelkareem, Mohammad Ali & Kolosz, Ben & Karuppasamy, K. & Aslam, Jawad & Gilani, Syed Omer, 2020. "Improving fuel cell performance via optimal parameters identification through fuzzy logic based-modeling and optimization," Energy, Elsevier, vol. 204(C).
    • Handle: RePEc:eee:energy:v:204:y:2020:i:c:s0360544220310835
    DOI: 10.1016/j.energy.2020.117976
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    Cited by:

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    4. 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).
    5. Wang, Erlei & Xia, Jiangying & Li, Jia & Sun, Xianke & Li, Hao, 2022. "Parameters exploration of SOFC for dynamic simulation using adaptive chaotic grey wolf optimization algorithm," Energy, Elsevier, vol. 261(PA).
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    7. Tanveer, Waqas Hassan & Abdelkareem, Mohammad Ali & Kolosz, Ben W. & Rezk, Hegazy & Andresen, John & Cha, Suk Won & Sayed, Enas Taha, 2021. "The role of vacuum based technologies in solid oxide fuel cell development to utilize industrial waste carbon for power production," Renewable and Sustainable Energy Reviews, Elsevier, vol. 142(C).
    8. Fathy, Ahmed & Babu, Thanikanti Sudhakar & Abdelkareem, Mohammad Ali & Rezk, Hegazy & Yousri, Dalia, 2022. "Recent approach based heterogeneous comprehensive learning Archimedes optimization algorithm for identifying the optimal parameters of different fuel cells," Energy, Elsevier, vol. 248(C).
    9. Jinquan, Guo & Hongwen, He & Jianwei, Li & Qingwu, Liu, 2021. "Real-time energy management of fuel cell hybrid electric buses: Fuel cell engines friendly intersection speed planning," Energy, Elsevier, vol. 226(C).
    10. Guarino, Antonio & Trinchero, Riccardo & Canavero, Flavio & Spagnuolo, Giovanni, 2022. "A fast fuel cell parametric identification approach based on machine learning inverse models," Energy, Elsevier, vol. 239(PC).
    11. Nassef, Ahmed M. & Sayed, Enas T. & Rezk, Hegazy & Inayat, Abrar & Yousef, Bashria A.A. & Abdelkareem, Mohammad A. & Olabi, A.G., 2020. "Developing a fuzzy-model with particle swarm optimization-based for improving the conversion and gasification rate of palm kernel shell," Renewable Energy, Elsevier, vol. 166(C), pages 125-135.
    12. Rezk, Hegazy & Olabi, A.G. & Ferahtia, Seydali & Sayed, Enas Taha, 2022. "Accurate parameter estimation methodology applied to model proton exchange membrane fuel cell," Energy, Elsevier, vol. 255(C).
    13. A.G. Olabi & Tabbi Wilberforce & Enas Taha Sayed & Khaled Elsaid & Mohammad Ali Abdelkareem, 2020. "Prospects of Fuel Cell Combined Heat and Power Systems," Energies, MDPI, vol. 13(16), pages 1-20, August.
    14. Olabi, A.G. & Wilberforce, Tabbi & Abdelkareem, Mohammad Ali, 2021. "Fuel cell application in the automotive industry and future perspective," Energy, Elsevier, vol. 214(C).
    15. A. G. Olabi & Tabbi Wilberforce & Khaled Elsaid & Tareq Salameh & Enas Taha Sayed & Khaled Saleh Husain & Mohammad Ali Abdelkareem, 2021. "Selection Guidelines for Wind Energy Technologies," Energies, MDPI, vol. 14(11), pages 1-34, June.
    16. Hasanien, Hany M. & Shaheen, Mohamed A.M. & Turky, Rania A. & Qais, Mohammed H. & Alghuwainem, Saad & Kamel, Salah & Tostado-Véliz, Marcos & Jurado, Francisco, 2022. "Precise modeling of PEM fuel cell using a novel Enhanced Transient Search Optimization algorithm," Energy, Elsevier, vol. 247(C).
    17. Hesham Alhumade & Hegazy Rezk & Abdulrahim A. Al-Zahrani & Sharif F. Zaman & Ahmed Askalany, 2021. "Artificial Intelligence Based Modelling of Adsorption Water Desalination System," Mathematics, MDPI, vol. 9(14), pages 1-13, July.

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