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Efficient fuel economy strategies for the Fuel Cell Hybrid Power Systems under variable renewable/load power profile

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  • Bizon, Nicu

Abstract

The adaptive energy management strategies proposed in this paper are based on a 2-dimensional (2D) optimization function designed for fuel economy of the fuel cell hybrid power system operating under the variable power profiles of renewable energy sources and loads, which will define the dynamic power profile on the DC bus. The power-following control is designed to mitigate the variability of the DC power based on the DC power flow balance, so the energy management unit generates the power-following reference and two other references for searching the optimum, which are used to control the inputs for the boost power converter and for the air and fuel regulators. All three energy management 2D strategies obtained by selecting the mentioned references to the controlled inputs of the fuel cell system are analyzed and compared with the types 1D and other reference strategies reported in literature using the fuel economy as a performance indicator. For example, the fuel economy for a 6 kW load cycle is of 42 and 18 L for the strategies 2D-RTO1 and 2D-RTO2 compared to the benchmark strategy based on static feed-forward control.

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  • Bizon, Nicu, 2019. "Efficient fuel economy strategies for the Fuel Cell Hybrid Power Systems under variable renewable/load power profile," Applied Energy, Elsevier, vol. 251(C), pages 1-1.
    • Handle: RePEc:eee:appene:v:251:y:2019:i:c:24
    DOI: 10.1016/j.apenergy.2019.113400
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    3. Nicu Bizon & Mircea Raceanu & Emmanouel Koudoumas & Adriana Marinoiu & Emmanuel Karapidakis & Elena Carcadea, 2020. "Renewable/Fuel Cell Hybrid Power System Operation Using Two Search Controllers of the Optimal Power Needed on the DC Bus," Energies, MDPI, vol. 13(22), pages 1-26, November.
    4. Fathy, Ahmed & Yousri, Dalia & Alanazi, Turki & Rezk, Hegazy, 2021. "Minimum hydrogen consumption based control strategy of fuel cell/PV/battery/supercapacitor hybrid system using recent approach based parasitism-predation algorithm," Energy, Elsevier, vol. 225(C).
    5. Li, Qi & Wang, Tianhong & Li, Shihan & Chen, Weirong & Liu, Hong & Breaz, Elena & Gao, Fei, 2021. "Online extremum seeking-based optimized energy management strategy for hybrid electric tram considering fuel cell degradation," Applied Energy, Elsevier, vol. 285(C).
    6. Nicu Bizon & Phatiphat Thounthong, 2020. "Energy Efficiency and Fuel Economy of a Fuel Cell/Renewable Energy Sources Hybrid Power System with the Load-Following Control of the Fueling Regulators," Mathematics, MDPI, vol. 8(2), pages 1-22, January.
    7. Sanghyun Yun & Jinwon Yun & Jaeyoung Han, 2023. "Development of a 470-Horsepower Fuel Cell–Battery Hybrid Xcient Dynamic Model Using Simscape TM," Energies, MDPI, vol. 16(24), pages 1-22, December.
    8. Nicu Bizon & Alin Gheorghita Mazare & Laurentiu Mihai Ionescu & Phatiphat Thounthong & Erol Kurt & Mihai Oproescu & Gheorghe Serban & Ioan Lita, 2019. "Better Fuel Economy by Optimizing Airflow of the Fuel Cell Hybrid Power Systems Using Fuel Flow-Based Load-Following Control," Energies, MDPI, vol. 12(14), pages 1-17, July.
    9. Nicu Bizon & Phatiphat Thounthong, 2021. "A Simple and Safe Strategy for Improving the Fuel Economy of a Fuel Cell Vehicle," Mathematics, MDPI, vol. 9(6), pages 1-29, March.

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