Study on Energy Saving of Multi-vehicle Operation Based on Genetic Optimization Algorithm

被引:0
|
作者
Wang, Xuejin [1 ]
Zhou, Xiangxiang [1 ]
Zhang, Yong [1 ]
Xing, Zongyi [1 ]
机构
[1] Nanjing Univ Sci & Technol, Sch Automat, Nanjing 210094, Peoples R China
基金
国家重点研发计划;
关键词
Urban rail transit; Regenerative braking energy; Genetic algorithm; Multi-vehicle energy saving;
D O I
10.1007/978-981-10-7989-4_54
中图分类号
TP39 [计算机的应用];
学科分类号
081203 ; 0835 ;
摘要
Aiming at the multi-vehicle energy-saving problem of a metro train, this paper presents a research method of multi-vehicle operation energy saving based on genetic algorithm. First, the process of braking energy transfer in multi-train operation is analyzed. Second, taking the least energy consumption, and travel time as the targets, all-day trains, and the high/low peak traffic as the constraints, a multi-vehicle energy-saving model based on a multi-vehicle operation energy saving is established. Finally, the genetic algorithm is used to obtain the optimal stopping time and starting interval, and the total energy consumption, train energy consumption, and line loss are calculated. At the same time, the multi-vehicle energy-saving simulation is carried out by using the short-term of four sections of Rong Jingdong Street Station to Yizhuang Bridge Station of Beijing Yizhuang Line, and it also optimized the stopping time and the starting interval.
引用
收藏
页码:533 / 542
页数:10
相关论文
共 50 条
  • [21] An Energy-saving Operation Strategy for High Speed Trains Based on Genetic Algorithm
    Song, Wenting
    Tan, Mi
    Cai, Wenchuan
    [J]. PROCEEDINGS OF THE 2015 JOINT INTERNATIONAL MECHANICAL, ELECTRONIC AND INFORMATION TECHNOLOGY CONFERENCE (JIMET 2015), 2015, 10 : 799 - 802
  • [22] Multi-Objective Optimization Technology for Building Energy-Saving Renovation Strategy based on Genetic Algorithm
    Deng, Shuibo
    Lv, Lei
    [J]. Decision Making: Applications in Management and Engineering, 2024, 7 (02): : 275 - 293
  • [23] Genetic algorithm-based energy-saving control strategy of vehicle drive
    Wang, Jing
    [J]. Wang, Jing (wangjinghenan2001@163.com), 1600, Cefin Publishing House (01): : 212 - 220
  • [24] An integrated multi-population genetic algorithm for multi-vehicle task assignment in a drift field
    Bai, Xiaoshan
    Yan, Weisheng
    Ge, Shuzhi Sam
    Cao, Ming
    [J]. INFORMATION SCIENCES, 2018, 453 : 227 - 238
  • [25] Multi-vehicle Control and Optimization for Spatiotemporal Sampling
    Sydney, Nitin
    Paley, Derek A.
    [J]. 2011 50TH IEEE CONFERENCE ON DECISION AND CONTROL AND EUROPEAN CONTROL CONFERENCE (CDC-ECC), 2011, : 5607 - 5612
  • [26] Collocation Methods for Multi-Vehicle Trajectory Optimization
    Turnbull, Oliver
    Richards, Arthur
    [J]. 2013 EUROPEAN CONTROL CONFERENCE (ECC), 2013, : 1230 - 1235
  • [27] Road multi-vehicle recognition and tracking algorithm based on computer vision
    Han, Y.B.
    Lei, P.
    Donofrio, A.
    [J]. Advances in Transportation Studies, 2023, 1 (Special Issue): : 137 - 146
  • [28] Multi-vehicle Coordination and Flexible Scheduling based on Simulated Annealing Algorithm
    Zhu, Cheng
    Tang, Luohao
    Zhang, Weiming
    [J]. 26TH CHINESE CONTROL AND DECISION CONFERENCE (2014 CCDC), 2014, : 2686 - 2691
  • [29] Conflict Resolution Model Based on Multi-vehicle Cooperative Optimization at Intersections
    Cheng, Ying
    Zhao, Jian-You
    Wang, Lei
    [J]. Jiaotong Yunshu Xitong Gongcheng Yu Xinxi/Journal of Transportation Systems Engineering and Information Technology, 2020, 20 (06): : 205 - 211
  • [30] Research on Multi-vehicle Positioning Technology Based on Kalman Filter Algorithm
    Sheng, Danting
    Hao Mingrui
    [J]. PROCEEDINGS OF 2022 INTERNATIONAL CONFERENCE ON AUTONOMOUS UNMANNED SYSTEMS, ICAUS 2022, 2023, 1010 : 687 - 697