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Energy Management Control Strategy for Saving Trip Costs of Fuel Cell/Battery Electric Vehicles

Author

Listed:
  • Juhui Gim

    (School of Electrical, Electronic and Control Engineering, Changwon National University, Changwon 51140, Korea)

  • Minsu Kim

    (School of Mechanical Engineering, Pusan National University, Busan 46241, Korea)

  • Changsun Ahn

    (School of Mechanical Engineering, Pusan National University, Busan 46241, Korea)

Abstract

Fuel cell vehicles (FCVs) should control the energy management between two energy sources for fuel economy, using the stored energy in a battery or generation of energy through a fuel cell system. The fuel economy for an FCV includes trip costs for hydrogen consumption and the lifetime of two energy sources. This paper proposes an implementable energy management control strategy for an FCV to reduce trip costs. The concept of the proposed control strategy is first to analyze the allowable current of a fuel cell system from the optimal strategies for various initial battery state of charge (SOC) conditions using dynamic programming (DP), and second, to find a modulation ratio determining the current of a fuel cell system for driving a vehicle using the particle swarm optimization method. The control strategy presents the on/off moment of a fuel cell system and the proper modulation ratio of the turned-on fuel cell system with respect to the battery SOC and the power demand. The proposed strategy reduces trip costs in real-time, similar to the DP-based optimal strategy, and more than the simple energy control strategy of switching a fuel cell system on/off at the battery SOC boundary conditions even for long-term driving cycles.

Suggested Citation

  • Juhui Gim & Minsu Kim & Changsun Ahn, 2022. "Energy Management Control Strategy for Saving Trip Costs of Fuel Cell/Battery Electric Vehicles," Energies, MDPI, vol. 15(6), pages 1-15, March.
    • Handle: RePEc:gam:jeners:v:15:y:2022:i:6:p:2131-:d:771187
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    References listed on IDEAS

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    Cited by:

    1. Gao, Qinxiang & Lei, Tao & Yao, Wenli & Zhang, Xingyu & Zhang, Xiaobin, 2023. "A health-aware energy management strategy for fuel cell hybrid electric UAVs based on safe reinforcement learning," Energy, Elsevier, vol. 283(C).
    2. Mubashir Rasool & Muhammad Adil Khan & Runmin Zou, 2023. "A Comprehensive Analysis of Online and Offline Energy Management Approaches for Optimal Performance of Fuel Cell Hybrid Electric Vehicles," Energies, MDPI, vol. 16(8), pages 1-33, April.
    3. Nikita V. Martyushev & Boris V. Malozyomov & Ilham H. Khalikov & Viktor Alekseevich Kukartsev & Vladislav Viktorovich Kukartsev & Vadim Sergeevich Tynchenko & Yadviga Aleksandrovna Tynchenko & Mengxu , 2023. "Review of Methods for Improving the Energy Efficiency of Electrified Ground Transport by Optimizing Battery Consumption," Energies, MDPI, vol. 16(2), pages 1-39, January.
    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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