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Optimization of Energy Management Strategy for Fuel Cell Hybrid Electric Vehicles Based on Dynamic Programming

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  • Changqing Du

    (Hubei Key Laboratory of Advanced Technology for Automotive Components, Wuhan University of Technology, Wuhan 430070, China
    Foshan Xianhu Laboratory of the Advanced Energy Science and Technology Guangdong Laboratory, Foshan 528200, China
    Hubei Research Center for New Energy & Intelligent Connected Vehicle, Wuhan University of Technology, Wuhan 430070, China)

  • Shiyang Huang

    (Hubei Key Laboratory of Advanced Technology for Automotive Components, Wuhan University of Technology, Wuhan 430070, China
    Foshan Xianhu Laboratory of the Advanced Energy Science and Technology Guangdong Laboratory, Foshan 528200, China
    Hubei Research Center for New Energy & Intelligent Connected Vehicle, Wuhan University of Technology, Wuhan 430070, China)

  • Yuyao Jiang

    (School of Customs and Public Administration, Shanghai Customs College, Shanghai 201204, China)

  • Dongmei Wu

    (Hubei Key Laboratory of Advanced Technology for Automotive Components, Wuhan University of Technology, Wuhan 430070, China
    Foshan Xianhu Laboratory of the Advanced Energy Science and Technology Guangdong Laboratory, Foshan 528200, China
    Hubei Research Center for New Energy & Intelligent Connected Vehicle, Wuhan University of Technology, Wuhan 430070, China)

  • Yang Li

    (Technical Center of Dongfeng Commercial Vehicle, Wuhan 430056, China)

Abstract

Fuel cell hybrid electric vehicles have attracted a large amount of attention in recent years owing to their advantages of zero emissions, high efficiency and low noise. To improve the fuel economy and system durability of vehicles, this paper proposes an energy management strategy optimization method for fuel cell hybrid electric vehicles based on dynamic programming. Rule-based and dynamic-programming-based strategies are developed based on building a fuel cell/battery hybrid system model. The rule-based strategy is improved with a power distribution scheme of dynamic programming strategy to improve the fuel economy of the vehicle. Furthermore, a limit on the rate of change of the output power of the fuel cell system is added to the rule-based strategy to avoid large load changes to improve the durability of the fuel cell. The simulation results show that the equivalent 100 km hydrogen consumption of the strategy based on the dynamic programming optimization rules is reduced by 6.46% compared with that before the improvement, and by limiting the rate of change of the output power of the fuel cell system, the times of large load changes are reduced. Therefore, the strategy based on the dynamic programming optimization rules effectively improves the fuel economy and system durability of vehicles.

Suggested Citation

  • Changqing Du & Shiyang Huang & Yuyao Jiang & Dongmei Wu & Yang Li, 2022. "Optimization of Energy Management Strategy for Fuel Cell Hybrid Electric Vehicles Based on Dynamic Programming," Energies, MDPI, vol. 15(12), pages 1-25, June.
    • Handle: RePEc:gam:jeners:v:15:y:2022:i:12:p:4325-:d:837811
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    References listed on IDEAS

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    2. Kunyu Wang & Rong Yang & Yongjian Zhou & Wei Huang & Song Zhang, 2022. "Design and Improvement of SD3-Based Energy Management Strategy for a Hybrid Electric Urban Bus," Energies, MDPI, vol. 15(16), pages 1-21, August.
    3. Laura Zecchi & Giulia Sandrini & Marco Gadola & Daniel Chindamo, 2022. "Modeling of a Hybrid Fuel Cell Powertrain with Power Split Logic for Onboard Energy Management Using a Longitudinal Dynamics Simulation Tool," Energies, MDPI, vol. 15(17), pages 1-18, August.
    4. Li, Renzheng & Hong, Jichao & Zhang, Huaqin & Chen, Xinbo, 2022. "Data-driven battery state of health estimation based on interval capacity for real-world electric vehicles," Energy, Elsevier, vol. 257(C).
    5. Shantanu Pardhi & Sajib Chakraborty & Dai-Duong Tran & Mohamed El Baghdadi & Steven Wilkins & Omar Hegazy, 2022. "A Review of Fuel Cell Powertrains for Long-Haul Heavy-Duty Vehicles: Technology, Hydrogen, Energy and Thermal Management Solutions," Energies, MDPI, vol. 15(24), pages 1-55, December.
    6. 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.
    7. Yongjian Zhou & Rong Yang & Song Zhang & Kejun Lan & Wei Huang, 2023. "Optimization of Power-System Parameters and Energy-Management Strategy Research on Hybrid Heavy-Duty Trucks," Energies, MDPI, vol. 16(17), pages 1-21, August.
    8. Enyong Xu & Mengcheng Ma & Weiguang Zheng & Qibai Huang, 2023. "An Energy Management Strategy for Fuel-Cell Hybrid Commercial Vehicles Based on Adaptive Model Prediction," Sustainability, MDPI, vol. 15(10), pages 1-20, May.
    9. Mohammad Kamrul Hasan & AKM Ahasan Habib & Shayla Islam & Mohammed Balfaqih & Khaled M. Alfawaz & Dalbir Singh, 2023. "Smart Grid Communication Networks for Electric Vehicles Empowering Distributed Energy Generation: Constraints, Challenges, and Recommendations," Energies, MDPI, vol. 16(3), pages 1-20, January.
    10. Kong, Yan & Xu, Nan & Liu, Qiao & Sui, Yan & Yue, Fenglai, 2023. "A data-driven energy management method for parallel PHEVs based on action dependent heuristic dynamic programming (ADHDP) model," Energy, Elsevier, vol. 265(C).

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