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Energy Management Strategies for Hybrid Construction Machinery: Evolution, Classification, Comparison and Future Trends

Author

Listed:
  • Wei Zhang

    (School of Mechanical and Aerospace Engineering, Jilin University, Changchun 130025, China)

  • Jixin Wang

    (School of Mechanical and Aerospace Engineering, Jilin University, Changchun 130025, China)

  • Shaofeng Du

    (State Key Laboratory of Smart Manufacturing for Special Vehicles and Transmission System, Baotou 014030, China)

  • Hongfeng Ma

    (School of Mechanical and Aerospace Engineering, Jilin University, Changchun 130025, China)

  • Wenjun Zhao

    (State Key Laboratory of Smart Manufacturing for Special Vehicles and Transmission System, Baotou 014030, China)

  • Haojie Li

    (School of Mechanical and Aerospace Engineering, Jilin University, Changchun 130025, China)

Abstract

Hybrid Construction Machinery (HCM), known as an effective and crucial solution for the issues of environment pollution and energy shortage, has aroused increasing attention from manufacturers and researchers. A suitable energy management strategy is the vital technology to determine the energy saving and emission reduction performance of HCM. In the present paper, the difference between construction machinery and automobiles is first analyzed from the perspective of configuration, and the energy-based HCM configuration classification method is introduced and analyzed. Second, the development of HCM energy management strategy is reviewed along with relevant references, and the HCM energy management strategies are classified and summarized. In the meantime, the characteristics of each strategy are compared and analyzed, and the application of HCM energy management strategy is analyzed based on the relevant research results. Lastly, the state, challenges facing and the trend of HCM energy management strategy are analyzed on the levels of theory, manufacturer and market. According to the analysis, though progress has been achieved in energy management technology of HCM driven by market and policy, many challenges and problems remain in the electrification and intellectualization of HCM and the testing, application and improvement of the strategy. The contribution of this paper can be identified in three points: First, it can be referenced to solve relevant engineering problems. Second, it lays the foundation for the proposal of new ideas. Third, it highlights the state-of-the-art trends and avoids what has already been done.

Suggested Citation

  • Wei Zhang & Jixin Wang & Shaofeng Du & Hongfeng Ma & Wenjun Zhao & Haojie Li, 2019. "Energy Management Strategies for Hybrid Construction Machinery: Evolution, Classification, Comparison and Future Trends," Energies, MDPI, vol. 12(10), pages 1-26, May.
    • Handle: RePEc:gam:jeners:v:12:y:2019:i:10:p:2024-:d:234610
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    References listed on IDEAS

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    Cited by:

    1. Jichao Liu & Yanyan Liang & Zheng Chen & Wenpeng Chen, 2023. "Energy Management Strategies for Hybrid Loaders: Classification, Comparison and Prospect," Energies, MDPI, vol. 16(7), pages 1-23, March.
    2. Ireneusz Pielecha, 2022. "Modeling of Fuel Cells Characteristics in Relation to Real Driving Conditions of FCHEV Vehicles," Energies, MDPI, vol. 15(18), pages 1-18, September.
    3. Xu, Nan & Kong, Yan & Yan, Jinyue & Zhang, Yuanjian & Sui, Yan & Ju, Hao & Liu, Heng & Xu, Zhe, 2022. "Global optimization energy management for multi-energy source vehicles based on “Information layer - Physical layer - Energy layer - Dynamic programming” (IPE-DP)," Applied Energy, Elsevier, vol. 312(C).
    4. Zhang, Wei & Wang, Jixin & Liu, Yong & Gao, Guangzong & Liang, Siwen & Ma, Hongfeng, 2020. "Reinforcement learning-based intelligent energy management architecture for hybrid construction machinery," Applied Energy, Elsevier, vol. 275(C).
    5. Zhang, Wei & Wang, Jixin & Xu, Zhenyu & Shen, Yuying & Gao, Guangzong, 2022. "A generalized energy management framework for hybrid construction vehicles via model-based reinforcement learning," Energy, Elsevier, vol. 260(C).

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