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An Optimal Train Speed Profile Planning Method for Induction Motor Traction System

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  • Ziyu Wu

    (School of Electronics and Information Engineering, Beijing Jiaotong University, Beijing 100044, China
    Traffic Control Technology Co., Ltd., Beijing 100070, China
    National Engineering Laboratory for Urban Rail Transit Communication and Operation Control, Beijing 100044, China)

  • Chunhai Gao

    (Traffic Control Technology Co., Ltd., Beijing 100070, China
    National Engineering Laboratory for Urban Rail Transit Communication and Operation Control, Beijing 100044, China)

  • Tao Tang

    (School of Electronics and Information Engineering, Beijing Jiaotong University, Beijing 100044, China)

Abstract

Optimizing the operating speed curve of trains without adding new energy storage facilities is essential in the energy-saving operation of railways. In this paper, we propose an optimal train speed curve planning method for driving trains more energy efficiently. A refined traction energy evaluation model for induction motor propulsion systems is first presented. The proposed model considers the efficiency of the traction motor at different operating points and the efficiency of the inverter and gearbox. Then, the optimal energy-efficient speed profile problem is transformed into a multistep decision problem and solved using dynamic programming (DP). To verify the effectiveness of the proposed method, a case study was conducted on an actual subway line. The results obtained indicate that the speed curve produced by the proposed method results in a 20% energy consumption saving compared with the speed curve for actual operations. Furthermore, the results of comparison with a genetic algorithm indicate that the DP algorithm is better able to satisfy the constraints of the train traction system. Solving the optimal speed curve using the proposed method and programming the onboard controller of the train according to the optimal speed curve enables the train to be driven with greater energy efficiency.

Suggested Citation

  • Ziyu Wu & Chunhai Gao & Tao Tang, 2021. "An Optimal Train Speed Profile Planning Method for Induction Motor Traction System," Energies, MDPI, vol. 14(16), pages 1-14, August.
    • Handle: RePEc:gam:jeners:v:14:y:2021:i:16:p:5153-:d:618418
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    References listed on IDEAS

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    1. Wang, Pengling & Goverde, Rob M.P., 2017. "Multi-train trajectory optimization for energy efficiency and delay recovery on single-track railway lines," Transportation Research Part B: Methodological, Elsevier, vol. 105(C), pages 340-361.
    2. He, Deqiang & Yang, Yanjie & Chen, Yanjun & Deng, Jianxin & Shan, Sheng & Liu, Jianren & Li, Xianwang, 2020. "An integrated optimization model of metro energy consumption based on regenerative energy and passenger transfer," Applied Energy, Elsevier, vol. 264(C).
    3. Huang, Yeran & Yang, Lixing & Tang, Tao & Gao, Ziyou & Cao, Fang, 2017. "Joint train scheduling optimization with service quality and energy efficiency in urban rail transit networks," Energy, Elsevier, vol. 138(C), pages 1124-1147.
    4. Liu, Rongfang (Rachel) & Golovitcher, Iakov M., 2003. "Energy-efficient operation of rail vehicles," Transportation Research Part A: Policy and Practice, Elsevier, vol. 37(10), pages 917-932, December.
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