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Energy-saving starting method of electric motor based on the battery-accumulator hybrid drive

Author

Listed:
  • Liu, Huanlong
  • Wang, Xu
  • Tian, Hao
  • Gan, Shicheng
  • Zhou, Jianyi
  • Wang, Jiawei

Abstract

The series electro-hydraulic hybrid powertrain has advantages in improving the dynamic characteristics and increasing the cruising range of battery rail vehicles. In order to reduce the large peak starting current of electric motor, an energy-saving starting method is proposed, which is using the hydraulic pump/motor to reversely drive the electric motor to restart at a speed, based on the energy reverse transfer characteristics between electric motor and hydraulic pump/motor. Firstly, the principle of restarting the electric motor at a speed is proposed based on the designed EH3. Then, PID is adopted to control the starting of electric motor which is driven by hydraulic powertrain. And a speed sensorless control algorithm is designed to control the restarting at a speed. Finally, aiming at the problem that the traditional simulation models of electro-hydraulic hybrid powertrain components are too simple and ideal. The estimation model of battery and SOC, dynamic efficiency model of hydraulic motor and dynamic iron loss model of PMSM are improved and optimized to improve the accuracy of powertrain simulation. The results both of co-simulation and experiments shows that the starting current can be reduced by 69.36 % at most and 64.91 % of battery consumption can be saved by restarting the electric motor at a speed.

Suggested Citation

  • Liu, Huanlong & Wang, Xu & Tian, Hao & Gan, Shicheng & Zhou, Jianyi & Wang, Jiawei, 2024. "Energy-saving starting method of electric motor based on the battery-accumulator hybrid drive," Energy, Elsevier, vol. 286(C).
    • Handle: RePEc:eee:energy:v:286:y:2024:i:c:s0360544223029596
    DOI: 10.1016/j.energy.2023.129565
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    References listed on IDEAS

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    1. Liu, Huanlong & Chen, Guanpeng & Li, Dafa & Wang, Jiawei & Zhou, Jianyi, 2021. "Energy active adjustment and bidirectional transfer management strategy of the electro-hydrostatic hydraulic hybrid powertrain for battery bus," Energy, Elsevier, vol. 230(C).
    2. Yi, Tong & Ma, Fei & Jin, Chun & Huang, Yanjun, 2018. "A novel coupled hydro-pneumatic energy storage system for hybrid mining trucks," Energy, Elsevier, vol. 143(C), pages 704-718.
    3. Geng, Wenran & Lou, Diming & Wang, Chen & Zhang, Tong, 2020. "A cascaded energy management optimization method of multimode power-split hybrid electric vehicles," Energy, Elsevier, vol. 199(C).
    4. He, Xiangyu & Liu, Hao & He, Shanghong & Hu, Bili & Xiao, Guangxin, 2019. "Research on the energy efficiency of energy regeneration systems for a battery-powered hydrostatic vehicle," Energy, Elsevier, vol. 178(C), pages 400-418.
    5. Liu, Huanlong & Jiang, Yue & Li, Shun, 2019. "Design and downhill speed control of an electric-hydrostatic hydraulic hybrid powertrain in battery-powered rail vehicles," Energy, Elsevier, vol. 187(C).
    6. Liu, Huanlong & Chen, Guanpeng & Xie, Chixin & Li, Dafa & Wang, Jiawei & Li, Shun, 2020. "Research on energy-saving characteristics of battery-powered electric-hydrostatic hydraulic hybrid rail vehicles," Energy, Elsevier, vol. 205(C).
    7. Cui, Taowen & Zhao, Wanzhong & Wang, Chunyan, 2019. "Design optimization of vehicle EHPS system based on multi-objective genetic algorithm," Energy, Elsevier, vol. 179(C), pages 100-110.
    8. Wang, Yue & Zeng, Xiaohua & Song, Dafeng, 2020. "Hierarchical optimal intelligent energy management strategy for a power-split hybrid electric bus based on driving information," Energy, Elsevier, vol. 199(C).
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    2. Osman Emre Özçiflikçi & Mikail Koç & Serkan Bahçeci, 2025. "Evaluation of Maximum Torque per Ampere Control Method for Interior Permanent Magnet Machine Drives on dSpace with Emphasis on Potential Practical Issues for High Energy Efficiency," Energies, MDPI, vol. 18(15), pages 1-20, August.

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