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Optimal Energy Management for Hydrogen Economy in a Hybrid Electric Vehicle

Author

Listed:
  • Seydali Ferahtia

    (Laboratoire de Génie Electrique, Department of Electrical Engineering, University of M’sila, M’sila 28000, Algeria)

  • Hegazy Rezk

    (Department of Electrical Engineering, College of Engineering in Wadi Alddawasir, Prince Sattam bin Abdulaziz University, Wadi Alddawasir 11991, Saudi Arabia
    Electrical Engineering Department, Faculty of Engineering, Minia University, Minia 6111, Egypt)

  • Rania M. Ghoniem

    (Department of Information Technology, College of Computer and Information Sciences, Princess Nourah bint Abdulrahman University, Riyadh 11671, Saudi Arabia)

  • Ahmed Fathy

    (Electrical Engineering Department, Faculty of Engineering, Jouf University, Sakaka 72388, Saudi Arabia
    Electrical Engineering Department, Faculty of Engineering, Zagazig University, Zagazig 44519, Egypt)

  • Reem Alkanhel

    (Department of Information Technology, College of Computer and Information Sciences, Princess Nourah bint Abdulrahman University, Riyadh 11671, Saudi Arabia)

  • Mohamed M. Ghonem

    (Department of Computer, Faculty of Engineering, Mansoura University, Mansoura 35516, Egypt)

Abstract

Fuel cell hybrid electric vehicles (FCEVs) are mainly electrified by the fuel cell (FC) system. As a supplementary power source, a battery or supercapacitor (SC) is employed (besides the FC) to enhance the power response due to the slow dynamics of the FC. Indeed, the performance of the hybrid power system mainly depends on the required power distribution manner among the sources, which is managed by the energy management strategy (EMS). This paper considers an FCEV based on the proton exchange membrane FC (PEMFC)/battery/SC. The energy management strategy is designed to ensure optimum power distribution between the sources considering hydrogen consumption. Its main objective is to meet the electric motor’s required power with economic hydrogen consumption and better electrical efficiency. The proposed EMS combines the external energy maximization strategy (EEMS) and the bald eagle search algorithm (BES). Simulation tests for the Extra-Urban Driving Cycle (EUDC) and New European Driving Cycle (NEDC) profiles were performed. The test is supposed to be performed in typical conditions t = 25 °C on a flat road without no wind effect. In addition, this strategy was compared with the state machine control strategy, classic PI, and equivalent consumption minimization strategy. In terms of optimization, the proposed approach was compared with the original EEMS, particle swarm optimization (PSO)-based EEMS, and equilibrium optimizer (EO)-based EEMS. The results confirm the ability of the proposed strategy to reduce fuel consumption and enhance system efficiency. This strategy provides 26.36% for NEDC and 11.35% for EUDC fuel-saving and efficiency enhancement by 6.74% for NEDC and 36.19% for EUDC.

Suggested Citation

  • Seydali Ferahtia & Hegazy Rezk & Rania M. Ghoniem & Ahmed Fathy & Reem Alkanhel & Mohamed M. Ghonem, 2023. "Optimal Energy Management for Hydrogen Economy in a Hybrid Electric Vehicle," Sustainability, MDPI, vol. 15(4), pages 1-19, February.
    • Handle: RePEc:gam:jsusta:v:15:y:2023:i:4:p:3267-:d:1064550
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    References listed on IDEAS

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    Cited by:

    1. Haibo Huo & Jiajie Chen & Ke Wang & Fang Wang & Guangzhe Jin & Fengxiang Chen, 2023. "State Estimation of Membrane Water Content of PEMFC Based on GA-BP Neural Network," Sustainability, MDPI, vol. 15(11), pages 1-16, June.
    2. Kun He & Dongchen Qin & Jiangyi Chen & Tingting Wang & Hongxia Wu & Peizhuo Wang, 2023. "Adaptive Equivalent Consumption Minimization Strategy for Fuel Cell Buses Based on Driving Style Recognition," Sustainability, MDPI, vol. 15(10), pages 1-17, May.

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