Volume 16 Issue 2
. 
Turn off MathJax
Article Contents
Article Navigation >  Journal of Traffic and Transportation Engineering  > 2016  >  16(2): 118-124
Next Previous
HUANG You-neng, GONG Shao-feng, CAO Yuan, CHEN Lei. Optimization model of energy-efficient driving for train in urban rail transit based on particle swarm algorithm[J]. Journal of Traffic and Transportation Engineering, 2016, 16(2): 118-124. doi: 10.19818/j.cnki.1671-1637.2016.02.014
Citation: HUANG You-neng, GONG Shao-feng, CAO Yuan, CHEN Lei. Optimization model of energy-efficient driving for train in urban rail transit based on particle swarm algorithm[J]. Journal of Traffic and Transportation Engineering, 2016, 16(2): 118-124. doi: 10.19818/j.cnki.1671-1637.2016.02.014
shu

Optimization model of energy-efficient driving for train in urban rail transit based on particle swarm algorithm

doi: 10.19818/j.cnki.1671-1637.2016.02.014
  • 1.

    National Engineering Research Center for Rail Transportation Operation Control System, Beijing Jiaotong University, Beijing 100044, China

  • 2.

    School of Electronic and Information Engineering, Beijing Jiaotong University, Beijing 100044, China

  • 3.

    School of Electronic, Electrical and Systems Engineering, University of Birmingham, Birmingham B15 2TT, West Midlands, UK

More Information
  • Author Bio:

    HUANG You-neng(1974-), male, associate professor, PhD, +86-10-51685195, ynhuang@bjtu.edu.cn

  • Received Date: 2015-11-21
  • Publish Date: 2016-04-25
    Abstract
  • In order to reduce the interstation operation energy consumption of train in urban rail transit, the interstation energy-efficient driving strategy of train was studied.On the basis of considering speed limit and gradient, a energy-efficient optimization model with the constraint of trip time was established, the optimal energy-efficient driving strategy was proposed by using particle swarm optimization(PSO)to optimize the target speed sequence.The optimization method of energy-efficient driving was realized through two phases.In the first phase, under the condition of constant interstation trip time, the interstation energy-efficient driving strategy of train was optimized with PSO, and the relationship between trip time and energy consumption was obtained.In the second phase, under the condition of the constant total trip time of whole interstations, the trip time was redistributed, and the energy-efficient driving strategy of train for the whole line was obtained.Based on the real track data and vehicle parameters of Yizhuang Lineof Beijing Subway, the optimization method was simulated and verified.Simulation result shows that after optimization, the interstation operation energy consumption of train reduces by 6.15%in the first phase in Wanyuan Street-Rongjingdong Street, and the total operation energy consumption of whole interstations reduces by 14.77% in the second phase.So the model can effectively reduce the operation energy consumption of train, and provides a basis for the generation of train timetable.

     

    • FullText(HTML)
  • loading
    • References(21)
  • [1]
    ICHIKAWA K. Application of optimization theory for bounded state variable problems to the operation of train[J]. Bulletin of JSME, 1968, 11(47): 857-865. doi: 10.1299/jsme1958.11.857
    [2]
    KHMELNITSKY E. On an optimal control problem of train operation[J]. IEEE Transactions on Automatic Control, 2000, 45(7): 1257-1266. doi: 10.1109/9.867018
    [3]
    GOLOVITCHER I M. Energy efficient control of rail vehicles[C]//IEEE. 2001IEEE International Conference on Systems, Man, and Cybernetics. New York: IEEE, 2001: 658-663.
    [4]
    LIU Rong-fang, GOLOVITCHER I M. Energy-efficient operation of rail vehicles[J]. Transportation Research Part A: Policy and Practice, 2003, 37(10): 917-932. doi: 10.1016/j.tra.2003.07.001
    [5]
    HOWLETT P G, PUDNEY P J, VU X. Local energy minimization in optimal train control[J]. Automatica, 2009, 45(11): 2692-2698. doi: 10.1016/j.automatica.2009.07.028
    [6]
    MIYATAKE M, KO H. Optimization of train speed profile for minimum energy consumption[J]. IEEJ Transactions on Electrical and Electronic Engineering, 2010, 5(3): 263-269. doi: 10.1002/tee.20528
    [7]
    LU Shao-feng, HILLMANSEN S, HO T K, et al. Singletrain trajectory optimization[J]. IEEE Transactions on Intelligent Transportation Systems, 2013, 14(2): 743-750. doi: 10.1109/TITS.2012.2234118
    [8]
    CHUANG H J, CHEN C S, LIN C H, et al. Design of optimal coasting speed for saving social cost in mass rapid transit systems[C]//IEEE. Third International Conference on Electric Utility Deregulation and Restructuring and Power Technologies. New York: IEEE, 2008: 2833-2839.
    [9]
    FU Yin-ping. Researeh on modeling and simulations of train tracking operation and saving energy optimization[D]. Beijing: Beijing Jiaotong University, 2009. (in Chinese).
    [10]
    KE B R, CHEN M C, LIN C L. Block-layout design using MAX-MIN ant system for saving energy on mass rapid transit systems[J]. IEEE Transactions on Intelligent Transportation Systems, 2009, 10(2): 226-235. doi: 10.1109/TITS.2009.2018324
    [11]
    KE B R, LIN C L, LAI C W. Optimization of train-speed trajectory and control for mass rapid transit systems[J]. Control Engineering Practice, 2011, 19(7): 675-687. doi: 10.1016/j.conengprac.2011.03.003
    [12]
    KE B R, LIN C L, YANG C C. Optimisation of train energyefficient operation for mass rapid transit systems[J]. IET Intelligent Transport Systems, 2012, 6(1): 58-66. doi: 10.1049/iet-its.2010.0144
    [13]
    YU Xue-song. Energy-efficient optimization and energy consumption evaluation of uran rail transit train[D]. Beijing: Beijing Jiaotong University, 2012. (in Chinese).
    [14]
    WU Yang, WANG Yue-ming, ZENG Li. Method of realtime adjustment of metro trains headway after a delay[J]. Electric Locomotives and Mass Transit Vehicles, 2003, 26(5): 21-23. (in Chinese). doi: 10.3969/j.issn.1672-1187.2003.05.007
    [15]
    WU Yang. Research of train operation adjustment for delay and train speed controlling model[D]. Chengdu: Southwest Jiaotong University, 2004. (in Chinese).
    [16]
    WU Yang, LUO Xia. Tactic for real-time operation adjustment and corresponding dynamic velocity control mode for delayed metro trains[J]. China Railway Science, 2005, 26(6): 113-118. (in Chinese). doi: 10.3321/j.issn:1001-4632.2005.06.023
    [17]
    NASRI A, MOGHADAM M F, MOKHTARI H. Timetable optimization for maximum usage of regenerative energy of braking in electrical railway systems[C]//IEEE. 2010International Symposium on Power Electronics Electrical Drives Automation and Motion. New York: IEEE, 2010: 1218-1221.
    [18]
    YAN Bang-jie, ZHANG Chen-qiu, LIN Zhi-ming, et al. MRT timetable and energy conservation[J]. Journal of Transport Information and Safety, 2011, 29(1): 139-144. (in Chinese). doi: 10.3963/j.ISSN1674-4861.2011.01.033
    [19]
    WONG K K, HO T K. Dwell-time and run-time control for DC mass rapid transit railways[J]. IET Electric Power Applications, 2007, 1(6): 956-966. doi: 10.1049/iet-epa:20060132
    [20]
    LI Xiang, WANG De-chun, LI Ke-ping, et al. A green train scheduling model and fuzzy multi-objective optimization algorithm[J]. Applied Mathematical Modellings, 2013, 37(4): 2063-2073. doi: 10.1016/j.apm.2012.04.046
    [21]
    SU Shuai, LI Xiang, TANG Tao, et al. A subway train timetable optimization approach based on energy-efficient operation strategy[J]. IEEE Transactions on Intelligent Transportation Systems, 2013, 14(2): 883-893.
    • Relative Articles
    • Supplements(0)
    • Cited By

Catalog

    Get Citation
    PDF
    XML

    Article Metrics

    Article views (2113) PDF downloads(1000) Cited by()
    Related

    Website copyright Journal of Traffic and Transportation Engineering Editorial Department陕ICP备05001904号-1

    Address ：Editorial Department of Journal of Traffic and Transportation Engineering, Chang'an University, Middle Section of South 2nd Ring Road, Xi'an, China（710064） Tel：029-82334388 Email：jygc@chd.edu.cn

    All visit:Today's visit:

    Supported by: Beijing Renhe Information Technology Co. Ltd    

    /

    DownLoad:  Full-Size Img  PowerPoint
    Return
    Return