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Design and control of a novel hybrid drive swing system for excavators integrating electrical and hydraulic energy recovery systems

Author

Listed:
  • Cui, Jinyuan
  • Quan, Long
  • Liu, Zhiqi
  • Hao, Yunxiao
  • Ge, Lei
  • Huang, Weinan

Abstract

Against the backdrop of the accelerated transition toward energy conservation and low-carbon emissions in construction machinery, energy-saving technologies for excavator swing systems have emerged as a research focus. However, significant challenges remain in improving the energy-saving technologies of swing systems, particularly: high energy consumption, waste of braking energy, and excessive installed power of power components. To address these challenges, this paper proposes a hybrid drive swing system for excavators (E-HDSS) that integrates electrical and hydraulic energy recovery systems. The E-HDSS leverages the respective advantages of the electrical and hydraulic systems, endowing the system with the merits of high energy efficiency, superior control performance, and efficient recovery of braking energy. Furthermore, this study develops a composite control strategy integrating rule-based cooperative control with optimized power allocation. The coordinated operation of the electrical and hydraulic systems is governed by the preset rule control. The localized power distribution is optimized through a multi-objective particle swarm optimization algorithm. Finally, the co-simulation model and experimental platform for a 6-ton excavator are established for testing and analysis. The results indicate that, under no-load conditions, the E-HDSS achieves energy savings of 18.56 % and 63.18 % compared to the electric swing system and hydraulic swing system. Under full-load conditions, the energy savings are further enhanced to 20.86 % and 64.67 %, respectively. Furthermore, the E-HDSS not only improves the permanent magnet synchronous motor efficiency by 3.1 % but also reduces the average swing-back displacement angle by 2.3°, effectively mitigating the swing-back phenomenon.

Suggested Citation

  • Cui, Jinyuan & Quan, Long & Liu, Zhiqi & Hao, Yunxiao & Ge, Lei & Huang, Weinan, 2025. "Design and control of a novel hybrid drive swing system for excavators integrating electrical and hydraulic energy recovery systems," Energy, Elsevier, vol. 336(C).
  • Handle: RePEc:eee:energy:v:336:y:2025:i:c:s036054422504143x
    DOI: 10.1016/j.energy.2025.138501
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    References listed on IDEAS

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    1. Li, Tianyu & Huang, Lingtao & Liu, Huiying, 2019. "Energy management and economic analysis for a fuel cell supercapacitor excavator," Energy, Elsevier, vol. 172(C), pages 840-851.
    2. Do, Tri Cuong & Dinh, Truong Quang & Yu, Yingxiao & Ahn, Kyoung Kwan, 2023. "Innovative powertrain and advanced energy management strategy for hybrid hydraulic excavators," Energy, Elsevier, vol. 282(C).
    3. Qi, Chunyang & Zhu, Yiwen & Song, Chuanxue & Yan, Guangfu & Xiao, Feng & Da wang, & Zhang, Xu & Cao, Jingwei & Song, Shixin, 2022. "Hierarchical reinforcement learning based energy management strategy for hybrid electric vehicle," Energy, Elsevier, vol. 238(PA).
    4. Yang, Jibin & Xu, Xiaohui & Peng, Yiqiang & Deng, Pengyi & Wu, Xiaohua & Zhang, Jiye, 2022. "Hierarchical energy management of a hybrid propulsion system considering speed profile optimization," Energy, Elsevier, vol. 244(PB).
    5. 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.
    6. Yang, Jian & Zhang, Tiezhu & Hong, Jichao & Zhang, Hongxin & Zhao, Qinghai & Meng, Zewen, 2021. "Research on driving control strategy and Fuzzy logic optimization of a novel mechatronics-electro-hydraulic power coupling electric vehicle," Energy, Elsevier, vol. 233(C).
    7. Peng Hu & Jianxin Zhu & Jun Gong & Daqing Zhang & Changsheng Liu & Yuming Zhao & Yong Guo, 2021. "Development of a Comprehensive Driving Cycle for Construction Machinery Used for Energy Recovery System Evaluation: A Case Study of Medium Hydraulic Excavators," Mathematical Problems in Engineering, Hindawi, vol. 2021, pages 1-13, February.
    8. Kwangman An & Hyehyun Kang & Youngkuk An & Jinil Park & Jonghwa Lee, 2020. "Methodology of Excavator System Energy Flow-Down," Energies, MDPI, vol. 13(4), pages 1-19, February.
    9. Gong, Jun & Zhang, Daqing & Guo, yong & Liu, Changsheng & Zhao, Yuming & Hu, Peng & Quan, weicai, 2019. "Power control strategy and performance evaluation of a novel electro-hydraulic energy-saving system," Applied Energy, Elsevier, vol. 233, pages 724-734.
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