A Two-Stage Optimization Algorithm of the Train Traction Energy Consumption in Urban Rail Transit

被引:2
|
作者
Guan, Lihe [1 ]
机构
[1] Chongqing Jiaotong Univ, Sch Math & Stat, Chongqing 400074, Peoples R China
基金
中国国家自然科学基金;
关键词
Optimization; Energy consumption; Force; Rails; Resistance; Mathematical models; Machine learning algorithms; SPEED PROFILES; OPERATION; STRATEGY;
D O I
10.1109/MITS.2022.3217076
中图分类号
TM [电工技术]; TN [电子技术、通信技术];
学科分类号
0808 ; 0809 ;
摘要
The optimization of the train traction energy consumption in urban rail transit is a nonlinear optimization problem, and its solution is a very difficult task. This article focuses on the optimization of train traction energy consumption on the line with multiple stations. First, a single-objective nonlinear energy-saving optimization model is established for a given line and total travel time. Second, a two-stage optimization algorithm is proposed to solve this model. In the first stage, for a given interval and travel time, an optimization model of the traction energy consumption is established by using the distance discretization method. A dichotomous iterative algorithm based on energy consumption is proposed to search the optimal traction energy consumption. By calling this algorithm repeatedly, the energy-time curve of each interval can be obtained. In the second stage, according to these energy-time curves, the total travel time of a train on the line is allocated to each interval one by one in the form of time slices. And finally the optimal travel time and optimal traction energy consumption of the train on each interval are obtained. This method does not need to set the train operation mode sequence in advance but adaptively selects the energy-saving operation mode according to the line constraints and the train parameters. The up-direction from Yizhuang to Tongjinan of the Beijing Metro Yizhuang line in China is selected as the test section. The experimental results demonstrate the effectiveness and computational efficiency of our proposed methods and algorithms. Moreover, the dichotomous iterative algorithm runs so fast that it can be used in real-time driver advisory systems or automatic train operation systems.
引用
收藏
页码:26 / 40
页数:15
相关论文
共 50 条
  • [1] Optimization of Train Headway and Traction Energy Consumption in Urban Rail Transit
    Gao, Hao
    Guo, Jin
    Zhang, Ya-Dong
    [J]. Jiaotong Yunshu Xitong Gongcheng Yu Xinxi/Journal of Transportation Systems Engineering and Information Technology, 2020, 20 (06): : 170 - 177
  • [2] Robust optimization of train timetable and energy efficiency in urban rail transit: A two-stage approach
    Qu, Yunchao
    Wang, Huan
    Wu, Jianjun
    Yang, Xin
    Yin, Haodong
    Zhou, Li
    [J]. COMPUTERS & INDUSTRIAL ENGINEERING, 2020, 146
  • [3] Two-Stage Synthetic Optimization of Supercapacitor-Based Energy Storage Systems, Traction Power Parameters and Train Operation in Urban Rail Transit
    Zhu, Feiqin
    Yang, Zhongping
    Zhao, Ziwei
    Lin, Fei
    [J]. IEEE TRANSACTIONS ON VEHICULAR TECHNOLOGY, 2021, 70 (09) : 8590 - 8605
  • [4] Analysis of the Abnormality of Traction Energy Consumption in Urban Rail Transit System
    Gao, Xinjun
    Shi, Xuetao
    [J]. Mathematical Problems in Engineering, 2023, 2023
  • [5] Train Operation Traction Energy Calculation and Saving in Urban Rail Transit System
    Hu, Peng
    Chen, Rongwu
    Li, Haoyu
    Liang, Yi
    [J]. PROCEEDINGS OF THE 2012 SECOND INTERNATIONAL CONFERENCE ON INSTRUMENTATION & MEASUREMENT, COMPUTER, COMMUNICATION AND CONTROL (IMCCC 2012), 2012, : 505 - 507
  • [6] Line-Based Traction Energy Consumption Estimation of Urban Rail Transit
    Tang, Ying
    Yao, Enjian
    Zhang, Rui
    Sun, Xun
    [J]. PROCEEDINGS OF THE 2015 INTERNATIONAL CONFERENCE ON ELECTRICAL AND INFORMATION TECHNOLOGIES FOR RAIL TRANSPORTATION: TRANSPORTATION, 2016, 378 : 529 - 536
  • [7] Urban rail transit planning using a two-stage simulation-based optimization approach
    Hassannayebi, Erfan
    Sajedinejad, Arman
    Mardani, Soheil
    [J]. SIMULATION MODELLING PRACTICE AND THEORY, 2014, 49 : 151 - 166
  • [8] Research on Energy Saving Optimization of Random Traction Strategy for Urban Rail Transit
    Li, Dongzhi
    Meng, Xuelei
    Han, Zheng
    Xu, Shichao
    Zhang, Bo
    An, Lihui
    Wang, Ruidong
    [J]. ENGINEERING LETTERS, 2023, 31 (01) : 19 - 19
  • [9] Research on Energy Saving Optimization of Random Traction Strategy for Urban Rail Transit
    Li, Dongzhi
    Meng, Xuelei
    Han, Zheng
    Xu, Shichao
    Zhang, Bo
    An, Lihui
    Wang, Ruidong
    [J]. Engineering Letters, 2023, 31 (01): : 287 - 294
  • [10] A novel two-stage approach for energy-efficient timetabling for an urban rail transit network
    Huang, Kang
    Liao, Feixiong
    [J]. TRANSPORTATION RESEARCH PART E-LOGISTICS AND TRANSPORTATION REVIEW, 2023, 176