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Hierarchical Energy Management and Energy Saving Potential Analysis for Fuel Cell Hybrid Electric Tractors

Author

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  • Shenghui Lei

    (College of Vehicle and Traffic Engineering, Henan University of Science and Technology, Luoyang 471003, China
    State Key Laboratory of Intelligent Agricultural Power Equipment, Luoyang 471039, China)

  • Yanying Li

    (College of Vehicle and Traffic Engineering, Henan University of Science and Technology, Luoyang 471003, China
    State Key Laboratory of Intelligent Agricultural Power Equipment, Luoyang 471039, China)

  • Mengnan Liu

    (College of Vehicle and Traffic Engineering, Henan University of Science and Technology, Luoyang 471003, China
    State Key Laboratory of Intelligent Agricultural Power Equipment, Luoyang 471039, China
    YTO Group Corporation R&D Center, Luoyang 471039, China)

  • Wenshuo Li

    (College of Vehicle and Traffic Engineering, Henan University of Science and Technology, Luoyang 471003, China)

  • Tenglong Zhao

    (College of Vehicle and Traffic Engineering, Henan University of Science and Technology, Luoyang 471003, China)

  • Shuailong Hou

    (College of Vehicle and Traffic Engineering, Henan University of Science and Technology, Luoyang 471003, China)

  • Liyou Xu

    (College of Vehicle and Traffic Engineering, Henan University of Science and Technology, Luoyang 471003, China
    State Key Laboratory of Intelligent Agricultural Power Equipment, Luoyang 471039, China)

Abstract

To address the challenges faced by fuel cell hybrid electric tractors (FCHETs) equipped with a battery and supercapacitor, including the complex coordination of multiple energy sources, low power allocation efficiency, and unclear optimal energy consumption, this paper proposes two energy management strategies (EMSs): one based on hierarchical instantaneous optimization (HIO) and the other based on multi-dimensional dynamic programming with final state constraints (MDDP-FSC). The proposed HIO-based EMS utilizes a low-pass filter and fuzzy logic correction in its upper-level strategy to manage high-frequency dynamic power using the supercapacitor. The lower-level strategy optimizes fuel cell efficiency by allocating low-frequency stable power based on the principle of minimizing equivalent consumption. Validation using a hardware-in-the-loop (HIL) simulation platform and comparative analysis demonstrate that the HIO-based EMS effectively improves the transient operating conditions of the battery and fuel cell, extending their lifespan and enhancing system efficiency. Furthermore, the HIO-based EMS achieves a 95.20% level of hydrogen consumption compared to the MDDP-FSC-based EMS, validating its superiority. The MDDP-FSC-based EMS effectively avoids the extensive debugging efforts required to achieve a final state equilibrium, while providing valuable insights into the global optimal energy consumption potential of multi-energy source FCHETs.

Suggested Citation

  • Shenghui Lei & Yanying Li & Mengnan Liu & Wenshuo Li & Tenglong Zhao & Shuailong Hou & Liyou Xu, 2025. "Hierarchical Energy Management and Energy Saving Potential Analysis for Fuel Cell Hybrid Electric Tractors," Energies, MDPI, vol. 18(2), pages 1-27, January.
    • Handle: RePEc:gam:jeners:v:18:y:2025:i:2:p:247-:d:1562453
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    References listed on IDEAS

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    1. Wu, Jingda & He, Hongwen & Peng, Jiankun & Li, Yuecheng & Li, Zhanjiang, 2018. "Continuous reinforcement learning of energy management with deep Q network for a power split hybrid electric bus," Applied Energy, Elsevier, vol. 222(C), pages 799-811.
    2. Das, Himadry Shekhar & Tan, Chee Wei & Yatim, A.H.M., 2017. "Fuel cell hybrid electric vehicles: A review on power conditioning units and topologies," Renewable and Sustainable Energy Reviews, Elsevier, vol. 76(C), pages 268-291.
    3. Simone Pascuzzi & Katarzyna Łyp-Wrońska & Katarzyna Gdowska & Francesco Paciolla, 2024. "Sustainability Evaluation of Hybrid Agriculture-Tractor Powertrains," Sustainability, MDPI, vol. 16(3), pages 1-17, January.
    4. Ren, Xiaoxia & Ye, Jinze & Xie, Liping & Lin, Xinyou, 2024. "Battery longevity-conscious energy management predictive control strategy optimized by using deep reinforcement learning algorithm for a fuel cell hybrid electric vehicle," Energy, Elsevier, vol. 286(C).
    5. Meng Yang & Xiaoxu Sun & Xiaoting Deng & Zhixiong Lu & Tao Wang, 2023. "Extrapolation of Tractor Traction Resistance Load Spectrum and Compilation of Loading Spectrum Based on Optimal Threshold Selection Using a Genetic Algorithm," Agriculture, MDPI, vol. 13(6), pages 1-20, May.
    6. Francesco Mocera & Aurelio Somà & Salvatore Martelli & Valerio Martini, 2023. "Trends and Future Perspective of Electrification in Agricultural Tractor-Implement Applications," Energies, MDPI, vol. 16(18), pages 1-36, September.
    7. Wu, Jinglai & Zhang, Yunqing & Ruan, Jiageng & Liang, Zhaowen & Liu, Kai, 2023. "Rule and optimization combined real-time energy management strategy for minimizing cost of fuel cell hybrid electric vehicles," Energy, Elsevier, vol. 285(C).
    8. Yifan Zhao & Liyou Xu & Chenhui Zhao & Haigang Xu & Xianghai Yan, 2024. "Research on Energy Management Strategy for Hybrid Tractors Based on DP-MPC," Energies, MDPI, vol. 17(16), pages 1-22, August.
    9. Li, Xian-zhe & Zhang, Ming-zhu & Yan, Xiang-hai & Liu, Meng-nan & Xu, Li-you, 2023. "Power allocation strategy for fuel cell distributed drive electric tractor based on adaptive multi-resolution analysis theory," Energy, Elsevier, vol. 284(C).
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