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Multi-Criteria Optimal Design for FUEL Cell Hybrid Power Sources

Author

Listed:
  • Adriano Ceschia

    (ESTACA’LAB, S2ET Department, ESTACA Engineering School–Paris Sacley, 12 Avenue Paul Delouvrier, 78180 Montigny-le-Bretonneux, France)

  • Toufik Azib

    (ESTACA’LAB, S2ET Department, ESTACA Engineering School–Paris Sacley, 12 Avenue Paul Delouvrier, 78180 Montigny-le-Bretonneux, France)

  • Olivier Bethoux

    (GeePs, Group of Electrical Engineering—Paris, UMR CNRS 8507, CentraleSupélec School, University of Paris-Saclay, Sorbonne University, 3 Rue Joliot-Curie, 91192 Gif-sur-Yvette, France)

  • Francisco Alves

    (GeePs, Group of Electrical Engineering—Paris, UMR CNRS 8507, CentraleSupélec School, University of Paris-Saclay, Sorbonne University, 3 Rue Joliot-Curie, 91192 Gif-sur-Yvette, France)

Abstract

This paper presents the development of a global and integrated sizing approach under different performance indexes applied to fuel cell/battery hybrid power systems. The strong coupling between the hardware sizing process and the system supervision (energy management strategy EMS) makes it hard for the design to consider all the possibilities, and today’s methodologies are mostly experience-based approaches that are impervious to technological disruption. With a smart design approach, new technologies are easier to consider, and this approach facilitates the use of new technologies for transport applications with a decision help tool. An automotive application with a hybrid fuel cell (PEMFC)/battery (Li-Ion) is considered to develop this approach. The proposed approach is based on imbricated optimization loops and considers multiple criteria such as the fuel consumption, reliability, and volume of the architecture, in keeping with industry expectations to allow a good trade-off between different performance indexes and explore their design options. This constitutes a low computational time and a very effective support tool that allows limited overconsumption and lifetime reduction for designed architecture in extreme and non-optimal use. We obtain, thanks to this work, a pre-design tool that helps to realize the first conception choice.

Suggested Citation

  • Adriano Ceschia & Toufik Azib & Olivier Bethoux & Francisco Alves, 2022. "Multi-Criteria Optimal Design for FUEL Cell Hybrid Power Sources," Energies, MDPI, vol. 15(9), pages 1-18, May.
  • Handle: RePEc:gam:jeners:v:15:y:2022:i:9:p:3364-:d:808861
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    References listed on IDEAS

    as
    1. Song, Ziyou & Zhang, Xiaobin & Li, Jianqiu & Hofmann, Heath & Ouyang, Minggao & Du, Jiuyu, 2018. "Component sizing optimization of plug-in hybrid electric vehicles with the hybrid energy storage system," Energy, Elsevier, vol. 144(C), pages 393-403.
    2. Rui Yang & Yupeng Yuan & Rushun Ying & Boyang Shen & Teng Long, 2020. "A Novel Energy Management Strategy for a Ship’s Hybrid Solar Energy Generation System Using a Particle Swarm Optimization Algorithm," Energies, MDPI, vol. 13(6), pages 1-14, March.
    3. Tri Cuong Do & Hoai Vu Anh Truong & Hoang Vu Dao & Cong Minh Ho & Xuan Dinh To & Tri Dung Dang & Kyoung Kwan Ahn, 2019. "Energy Management Strategy of a PEM Fuel Cell Excavator with a Supercapacitor/Battery Hybrid Power Source," Energies, MDPI, vol. 12(22), pages 1-24, November.
    4. Junhui Liu & Lei Feng & Zhiwu Li, 2017. "The Optimal Road Grade Design for Minimizing Ground Vehicle Energy Consumption," Energies, MDPI, vol. 10(5), pages 1-31, May.
    5. Zou Yuan & Liu Teng & Sun Fengchun & Huei Peng, 2013. "Comparative Study of Dynamic Programming and Pontryagin’s Minimum Principle on Energy Management for a Parallel Hybrid Electric Vehicle," Energies, MDPI, vol. 6(4), pages 1-14, April.
    6. Mehdi Sellali & Alexandre Ravey & Achour Betka & Abdellah Kouzou & Mohamed Benbouzid & Abdesslem Djerdir & Ralph Kennel & Mohamed Abdelrahem, 2022. "Multi-Objective Optimization-Based Health-Conscious Predictive Energy Management Strategy for Fuel Cell Hybrid Electric Vehicles," Energies, MDPI, vol. 15(4), pages 1-17, February.
    7. Tran, Dai-Duong & Vafaeipour, Majid & El Baghdadi, Mohamed & Barrero, Ricardo & Van Mierlo, Joeri & Hegazy, Omar, 2020. "Thorough state-of-the-art analysis of electric and hybrid vehicle powertrains: Topologies and integrated energy management strategies," Renewable and Sustainable Energy Reviews, Elsevier, vol. 119(C).
    8. Yi Yang & Zhihao Shang & Yao Chen & Yanhua Chen, 2020. "Multi-Objective Particle Swarm Optimization Algorithm for Multi-Step Electric Load Forecasting," Energies, MDPI, vol. 13(3), pages 1-19, January.
    9. Adriano Ceschia & Toufik Azib & Olivier Bethoux & Francisco Alves, 2020. "Optimal Sizing of Fuel Cell Hybrid Power Sources with Reliability Consideration," Energies, MDPI, vol. 13(13), pages 1-18, July.
    10. Pelletier, Samuel & Jabali, Ola & Laporte, Gilbert & Veneroni, Marco, 2017. "Battery degradation and behaviour for electric vehicles: Review and numerical analyses of several models," Transportation Research Part B: Methodological, Elsevier, vol. 103(C), pages 158-187.
    11. Xueying Song & Hongyu Lin & Gejirifu De & Hanfang Li & Xiaoxu Fu & Zhongfu Tan, 2020. "An Energy Optimal Dispatching Model of an Integrated Energy System Based on Uncertain Bilevel Programming," Energies, MDPI, vol. 13(2), pages 1-24, January.
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