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Multi-Objective Optimization-Based Health-Conscious Predictive Energy Management Strategy for Fuel Cell Hybrid Electric Vehicles

Author

Listed:
  • Mehdi Sellali

    (FEMTO-ST Institute (UMR CNRS 6174), University of Technology of Belfort-Montbéliard, 90010 Belfort, France
    LGEB Laboratory, University of Biskra, Biskra 07000, Algeria)

  • Alexandre Ravey

    (FEMTO-ST Institute (UMR CNRS 6174), University of Technology of Belfort-Montbéliard, 90010 Belfort, France)

  • Achour Betka

    (LGEB Laboratory, University of Biskra, Biskra 07000, Algeria)

  • Abdellah Kouzou

    (LAADI Laboratory, Faculty of Sciences and Technology, Ziane Achour University, Djelfa 17000, Algeria
    Electrical and Electronics Engineering Department, Nisantasi University, 34398 Istanbul, Turkey)

  • Mohamed Benbouzid

    (Institut de Recherche Dupuy de Lôme (UMR CNRS 6027 IRDL), University of Brest, 29238 Brest, France
    Logistics Engineering College, Shanghai Maritime University, Shanghai 201306, China)

  • Abdesslem Djerdir

    (FEMTO-ST Institute (UMR CNRS 6174), University of Technology of Belfort-Montbéliard, 90010 Belfort, France)

  • Ralph Kennel

    (Chair of Electrical Drive Systems and Power Electronics, Technical University of Munich (TUM), 80333 Munich, Germany)

  • Mohamed Abdelrahem

    (Chair of Electrical Drive Systems and Power Electronics, Technical University of Munich (TUM), 80333 Munich, Germany
    Faculty of Engineering, Assiut University, Assiut 71516, Egypt)

Abstract

The Energy Management Strategy (EMS) in Fuel Cell Hybrid Electric Vehicles (FCHEVs) is the key part to enhance optimal power distribution. Indeed, the most recent works are focusing on optimizing hydrogen consumption, without taking into consideration the degradation of embedded energy sources. In order to overcome this lack of knowledge, this paper describes a new health-conscious EMS algorithm based on Model Predictive Control (MPC), which aims to minimize the battery degradation to extend its lifetime. In this proposed algorithm, the health-conscious EMS is normalized in order to address its multi-objective optimization. Then, weighting factors are assigned in the objective function to minimize the selected criteria. Compared to most EMSs based on optimization techniques, this proposed approach does not require any information about the speed profile, which allows it to be used for real-time control of FCHEV. The achieved simulation results show that the proposed approach reduces the economic cost up to 50% for some speed profile, keeping the battery pack in a safe range and significantly reducing energy sources degradation. The proposed health-conscious EMS has been validated experimentally and its online operation ability clearly highlighted on a PEMFC delivery postal vehicle.

Suggested Citation

  • 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.
    • Handle: RePEc:gam:jeners:v:15:y:2022:i:4:p:1318-:d:747292
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    References listed on IDEAS

    as
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    Cited by:

    1. Lianghui Huang & Quan Ouyang & Jian Chen & Zhiyang Liu & Xiaohua Wu, 2023. "A Scalable Segmented-Based PEM Fuel Cell Hybrid Power System Model and Its Simulation Applications," Energies, MDPI, vol. 16(17), pages 1-13, August.
    2. 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.
    3. Wenshang Chen & Yang Liu & Ben Chen, 2022. "Numerical Simulation on Pressure Dynamic Response Characteristics of Hydrogen Systems for Fuel Cell Vehicles," Energies, MDPI, vol. 15(7), pages 1-18, March.
    4. Hu, Jianjun & Wang, Yangguang & Zou, Lingbo & Wang, Zhouxin, 2023. "Adaptive rule control strategy for composite energy storage fuel cell vehicle based on vehicle operating state recognition," Renewable Energy, Elsevier, vol. 204(C), pages 166-175.

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