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Performance enhancement of autonomous system comprising proton exchange membrane fuel cells and switched reluctance motor

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  • El-Hay, Enas A.
  • El-Hameed, Mohamed A.
  • El-Fergany, Attia A.

Abstract

It is well-known that switched reluctance motor, as a non-linear load, is a source of current ripples which reduces the life time of proton exchange membrane fuel cells and significantly affects their performances. In this work, optimal operations of autonomous stacked proton membrane fuel cells serving a switched reluctance motor are anticipated. Overall model comprising proton exchange membrane fuel cells stack and switched reluctance motor along with necessary interface and control elements are implemented using MATLAB/SIMULINK. Three key objectives are adopted to be optimized either individually or simultaneously such as: i) proton exchange membrane fuel cells stack efficiency, ii) torque per ampere ratio, and iii) torque smoothness factor. Six decision controlling parameters e.g. fuel cells' temperature, air flow rate, air pressure, fuel pressure, and turn on/off angles of switched reluctance motor represent the search space of dragonfly algorithm. Numerical results indicate that the proposed dragonfly algorithm-based method is capable of increasing proton exchange membrane fuel cells stack energy saving, and reducing of hydrogen consumption, ripples in switched reluctance motor torque and current of proton exchange membrane fuel cells stack. Finally, results generated by the dragonfly algorithm are appraised compared to those obtained by genetic algorithm which signifies its value.

Suggested Citation

  • El-Hay, Enas A. & El-Hameed, Mohamed A. & El-Fergany, Attia A., 2018. "Performance enhancement of autonomous system comprising proton exchange membrane fuel cells and switched reluctance motor," Energy, Elsevier, vol. 163(C), pages 699-711.
    • Handle: RePEc:eee:energy:v:163:y:2018:i:c:p:699-711
    DOI: 10.1016/j.energy.2018.08.104
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    Cited by:

    1. Hachana, Oussama & El-Fergany, Attia A., 2022. "Efficient PEM fuel cells parameters identification using hybrid artificial bee colony differential evolution optimizer," Energy, Elsevier, vol. 250(C).
    2. El-Hay, E.A. & El-Hameed, M.A. & El-Fergany, A.A., 2019. "Optimized Parameters of SOFC for steady state and transient simulations using interior search algorithm," Energy, Elsevier, vol. 166(C), pages 451-461.
    3. Gouda, Eid A. & Kotb, Mohamed F. & El-Fergany, Attia A., 2021. "Jellyfish search algorithm for extracting unknown parameters of PEM fuel cell models: Steady-state performance and analysis," Energy, Elsevier, vol. 221(C).
    4. Guoxiao Xu & Xinwei Dong & Bin Xue & Jianyou Huang & Junli Wu & Weiwei Cai, 2023. "Recent Approaches to Achieve High Temperature Operation of Nafion Membranes," Energies, MDPI, vol. 16(4), pages 1-21, February.
    5. Andrew J. Riad & Hany M. Hasanien & Rania A. Turky & Ahmed H. Yakout, 2023. "Identifying the PEM Fuel Cell Parameters Using Artificial Rabbits Optimization Algorithm," Sustainability, MDPI, vol. 15(5), pages 1-17, March.
    6. Xiaoshu Zan & Ning Wu & Ruidong Xu & Mingliang Cui & Zhikai Jiang & Kai Ni & Mohammed Alkahtani, 2019. "Design and Analysis of a Novel Converter Topology for Photovoltaic Pumps Based on Switched Reluctance Motor," Energies, MDPI, vol. 12(13), pages 1-17, July.
    7. Samuel Raafat Fahim & Hany M. Hasanien & Rania A. Turky & Abdulaziz Alkuhayli & Abdullrahman A. Al-Shamma’a & Abdullah M. Noman & Marcos Tostado-Véliz & Francisco Jurado, 2021. "Parameter Identification of Proton Exchange Membrane Fuel Cell Based on Hunger Games Search Algorithm," Energies, MDPI, vol. 14(16), pages 1-21, August.
    8. Hesham Alhumade & Ahmed Fathy & Abdulrahim Al-Zahrani & Muhyaddin Jamal Rawa & Hegazy Rezk, 2021. "Optimal Parameter Estimation Methodology of Solid Oxide Fuel Cell Using Modern Optimization," Mathematics, MDPI, vol. 9(9), pages 1-19, May.
    9. Seleem, Sameh I. & Hasanien, Hany M. & El-Fergany, Attia A., 2021. "Equilibrium optimizer for parameter extraction of a fuel cell dynamic model," Renewable Energy, Elsevier, vol. 169(C), pages 117-128.

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