Driver Advisory Technology for Energy-Efficient Train Operation
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1.Key Laboratory of Road and Traffic Engineering of the Ministry of Education, Tongji University, Shanghai 201804, China;2.Shanghai GL Traffic Technology Co., Ltd., Shanghai 201804, China;3.Department of Computer Science, City University of Hong Kong, Hong Kong 999077, China;4.Shanghai Metro Line 14 Development Co., Ltd., Shanghai 201103, China

Clc Number:

U239.5

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    Abstract:

    Based on the technical problem and difficulty of the randomness and arbitrariness in the selection of the current train driving speed curve, this paper constructed the structure of the energy-saving driving assistance system, and designed the system information interaction mode. based on which, the energy-saving model algorithm of train running at regular time is established. The method of genetic algorithm model based on cruise speed (CS) was innovatively established, and the energy saving model algorithm of train running at regular time was calculated and analyzed. The optimal speed curve was solved by encoding the energy consumption and CS. The algorithm proposed in this paper does not need to predetermine the sequence table of working conditions, increases the degree of freedom of model optimization, and makes the overall train speed curve appear more stable. The results of simulation experiments verify the effectiveness of the proposed method, and the advantages of the proposed model are proved by comparing with the examples in typical literatures. The optimal speed curve solved in this paper can be finally transmitted to the on-board terminal of driver assistance system(DAS) through driver assistance information for display, which can be used for driving guidance and energy saving effect of train driving.

    Reference
    [1] ICHIKAWA, Kunihiko. Application of optimization theory for bounded state variable problems to the operation of train[J]. Bulletin of the Japanese Society of Mechanical Engineering, 1968, 11(47):857.
    [2] HOWLETT P G, MILROY I P, PUDNEY P J . Eneigy-efficient train control[J]. Control Engineering Practice, 1994(2):193.
    [3] CHANG C S, SIM S S. Optimising train movements through coast control using genetic algorithms[J]. IET Electric Power Applications,1997,144(1):65.
    [4] LU S, HILLMANSEN S, HO T K, et al. Single-train trajectory optimization[J]. IEEE Transactions on Intelligent Transportation Systems,2013,14(2):743.
    [5] DALE Coleman, PHIL Howlett, PETER Pudney, et al. The Freightmiser driver advice system[C]//IET Conference on Railway Traction Systems 2010.[S.L.]:Curran Associates, Inc., 2010:231-235.
    [6] 石红国. 列车运行过程仿真及优化研究[D]. 成都:西南交通大学,2006.
    [7] 付印平. 列车追踪运行与节能优化建模及模拟研究[D]. 北京:北京交通大学,2009.
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ZENG Xiaoqing, XIONG Qipeng, WANG Yizeng, LIU Yuan, ZOU Linfeng, ZHOU Xisheng. Driver Advisory Technology for Energy-Efficient Train Operation[J].同济大学学报(自然科学版),2022,50(6):899~905

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History
  • Received:September 24,2021
  • Online: July 04,2022
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