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Power Management Optimization of an Experimental Fuel Cell/Battery/Supercapacitor Hybrid System

Author

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  • Farouk Odeim

    (Chair of Energy Technology, University of Duisburg-Essen, Lotharstr. 1, 47057 Duisburg, Germany)

  • Jürgen Roes

    (Chair of Energy Technology, University of Duisburg-Essen, Lotharstr. 1, 47057 Duisburg, Germany)

  • Angelika Heinzel

    (Chair of Energy Technology, University of Duisburg-Essen, Lotharstr. 1, 47057 Duisburg, Germany)

Abstract

In this paper, an experimental fuel cell/battery/supercapacitor hybrid system is investigated in terms of modeling and power management design and optimization. The power management strategy is designed based on the role that should be played by each component of the hybrid power source. The supercapacitor is responsible for the peak power demands. The battery assists the supercapacitor in fulfilling the transient power demand by controlling its state-of-energy, whereas the fuel cell system, with its slow dynamics, controls the state-of-charge of the battery. The parameters of the power management strategy are optimized by a genetic algorithm and Pareto front analysis in a framework of multi-objective optimization, taking into account the hydrogen consumption, the battery loading and the acceleration performance. The optimization results are validated on a test bench composed of a fuel cell system (1.2 kW, 26 V), lithium polymer battery (30 Ah, 37 V), and a supercapacitor (167 F, 48 V).

Suggested Citation

  • Farouk Odeim & Jürgen Roes & Angelika Heinzel, 2015. "Power Management Optimization of an Experimental Fuel Cell/Battery/Supercapacitor Hybrid System," Energies, MDPI, vol. 8(7), pages 1-26, June.
    • Handle: RePEc:gam:jeners:v:8:y:2015:i:7:p:6302-6327:d:51640
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    Cited by:

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    7. Sara Luciani & Andrea Tonoli, 2022. "Control Strategy Assessment for Improving PEM Fuel Cell System Efficiency in Fuel Cell Hybrid Vehicles," Energies, MDPI, vol. 15(6), pages 1-17, March.
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    9. Anggito P. Tetuko & Bahman Shabani & John Andrews, 2018. "Passive Fuel Cell Heat Recovery Using Heat Pipes to Enhance Metal Hydride Canisters Hydrogen Discharge Rate: An Experimental Simulation," Energies, MDPI, vol. 11(4), pages 1-19, April.
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    11. Mojgan Fayyazi & Paramjotsingh Sardar & Sumit Infent Thomas & Roonak Daghigh & Ali Jamali & Thomas Esch & Hans Kemper & Reza Langari & Hamid Khayyam, 2023. "Artificial Intelligence/Machine Learning in Energy Management Systems, Control, and Optimization of Hydrogen Fuel Cell Vehicles," Sustainability, MDPI, vol. 15(6), pages 1-38, March.
    12. Maria Guadalupe Reveles-Miranda & Manuel Israel Flota-Bañuelos & Freddy Chan-Puc & Daniella Pacheco-Catalán, 2017. "Experimental Evaluation of a Switching Matrix Applied in a Bank of Supercapacitors," Energies, MDPI, vol. 10(12), pages 1-12, December.
    13. Mario Marchesoni & Massimiliano Passalacqua & Luis Vaccaro, 2020. "A Refined Loss Evaluation of a Three-Switch Double Input DC-DC Converter for Hybrid Vehicle Applications," Energies, MDPI, vol. 13(1), pages 1-13, January.
    14. Li, Weihan & Cui, Han & Nemeth, Thomas & Jansen, Jonathan & Ünlübayir, Cem & Wei, Zhongbao & Feng, Xuning & Han, Xuebing & Ouyang, Minggao & Dai, Haifeng & Wei, Xuezhe & Sauer, Dirk Uwe, 2021. "Cloud-based health-conscious energy management of hybrid battery systems in electric vehicles with deep reinforcement learning," Applied Energy, Elsevier, vol. 293(C).
    15. Weiwei Yang & Jiejunyi Liang & Jue Yang & Nong Zhang, 2018. "Investigation of a Novel Coaxial Power-Split Hybrid Powertrain for Mining Trucks," Energies, MDPI, vol. 11(1), pages 1-18, January.
    16. Kréhi Serge Agbli & Mickaël Hilairet & Frédéric Gustin, 2020. "Real-Time Control Based on a CAN-Bus of Hybrid Electrical Systems," Energies, MDPI, vol. 13(17), pages 1-14, September.
    17. Xiong, Rui & Cao, Jiayi & Yu, Quanqing, 2018. "Reinforcement learning-based real-time power management for hybrid energy storage system in the plug-in hybrid electric vehicle," Applied Energy, Elsevier, vol. 211(C), pages 538-548.
    18. Aissa Benhammou & Hamza Tedjini & Mohammed Amine Hartani & Rania M. Ghoniem & Ali Alahmer, 2023. "Accurate and Efficient Energy Management System of Fuel Cell/Battery/Supercapacitor/AC and DC Generators Hybrid Electric Vehicles," Sustainability, MDPI, vol. 15(13), pages 1-27, June.
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    20. Cong Zhang & Dai Wang & Bin Wang & Fan Tong, 2020. "Battery Degradation Minimization-Oriented Hybrid Energy Storage System for Electric Vehicles," Energies, MDPI, vol. 13(1), pages 1-21, January.
    21. Duchaud, Jean-Laurent & Notton, Gilles & Darras, Christophe & Voyant, Cyril, 2018. "Power ramp-rate control algorithm with optimal State of Charge reference via Dynamic Programming," Energy, Elsevier, vol. 149(C), pages 709-717.

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