Volume 39 Issue 4
Aug.  2021
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Article Navigation >  Journal of Transport Information and Safety  > 2021  >  39(4): 84-91
MA Yangyang, MENG Xuelei, JIA Baotong, REN Yuanyuan, QIN Yongsheng. An Energy-saving Optimization Method of High-speed Trains Based on Time Deviation Penalty During Train Operation[J]. Journal of Transport Information and Safety, 2021, 39(4): 84-91. doi: 10.3963/j.jssn.1674-4861.2021.04.011
Citation: MA Yangyang, MENG Xuelei, JIA Baotong, REN Yuanyuan, QIN Yongsheng. An Energy-saving Optimization Method of High-speed Trains Based on Time Deviation Penalty During Train Operation[J]. Journal of Transport Information and Safety, 2021, 39(4): 84-91. doi: 10.3963/j.jssn.1674-4861.2021.04.011
shu

An Energy-saving Optimization Method of High-speed Trains Based on Time Deviation Penalty During Train Operation

doi: 10.3963/j.jssn.1674-4861.2021.04.011

  • School of Traffic and Transportation, Lanzhou Jiaotong University, Lanzhou 730070, China

  • Received Date: 2021-04-25
    Abstract
  • Reasonable arranging the operation mode of the train in the section can reduce the energy consumption of train operation. A determination strategy of train operating conditions based on the speed limit of the interval is adopted to determine the operating condition of the train. The energy consumption of the train is used as the optimization objective, and the distance, time, and speed limit of the train are used as the constraints. Time deviation penalty during train operation is added to the objective function to develop a mathematical model of energy-saving optimization for high-speed railway train operation, and the improved artificial bee colony algorithm based on Gaussian mutation and chaotic disturbance is used to solve the optimization model. The model and algorithm are verified with CRH3-350 multi-unit data as an example, the solution results show that the energy consumption can be saved by 2.5% when time deviation penalty during train operation is considered. Compared with the basic artificial bee colony algorithm and particle swarm algorithm, the improved artificial bee colony algorithm has improved the target value by 4.2% and 4.1%, respectively. Adopting the determinative strategy based on the interval speed limit combined with the energy-consumption optimization model can meet the required train operation conditions under different speed limits and different intervals. It shows that the established model and the designed algorithm have good problem-solving efficiency and optimized quality.

     

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    • References(19)
  • [1]
    YANG X, LI X, NING B, et al. A survey on energy-efficient train operation for urban rail transit[J]. IEEE Transactions on Intelligent Transportation Systems, 2015, 17(1): 2-13.
    [2]
    SONG Y, SONG W. A novel dual speed-curve optimization based approach for energy-saving operation of high-speed trains[J]. IEEE Transactions on Intelligent Transportation Systems, 2016, 17(6): 1-12. doi: 10.1109/TITS.2016.2564178
    [3]
    樊葱, 柏赟, 周雨鹤, 等. 考虑过分相区的高速铁路列车操纵节能优化[J]. 交通信息与安全, 2020, 38(4): 25-33. doi: 10.3963/j.jssn.1674-4861.2020.04.004

    FAN Cong, BAI Yun, ZHOU Yuhe, et al. Optimization of highspeed train control considering neutral zone[J]. Journal of Transport Information and Safety, 2020, 38(4): 25-33. (in Chinese) doi: 10.3963/j.jssn.1674-4861.2020.04.004
    [4]
    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
    [5]
    HOWLETT P G. An optimal strategy for the control of a train[J]. Anziam Journal, 1990, 31(4): 454-471. http://pdfs.semanticscholar.org/d1bc/f818883e5c64317a1fabad703b42b05d289f.pdf
    [6]
    HOWLETT P G, CHNEG J. Optimal driving strategies for a train on a track with continuously varying gradient[J]. The Journal of the Australian Mathematical Society Series B: Applied Mathematics, 1997, 38(3): 388-410. doi: 10.1017/S0334270000000746
    [7]
    ASNIS I, DMITRUK A, OSMOLOVSKII N. Solution of the problem of the energetically optimal control of the motion of a train by the maximum principle[J]. USSR Computational Mathematics and Mathematical Physics, 1985, 25(6): 37-44. doi: 10.1016/0041-5553(85)90006-0
    [8]
    曹佳峰. 城市轨道交通列车节能操纵策略研究[J]. 铁道运输与经济, 2019, 41(10): 108-113+118. https://www.cnki.com.cn/Article/CJFDTOTAL-TDYS201910020.htm

    CAO Jiafeng. A study on the energy-efficient manipulation strategy of urban rail transit train[J]. Railway Transport and Economy, 2019, 41(10): 108-113+118. (in Chinese) https://www.cnki.com.cn/Article/CJFDTOTAL-TDYS201910020.htm
    [9]
    JORGEN T H, DAVID P, MOHAMMAD S. A dynamic programming approach for optimizing train speed profiles with speed restrictions and passage points[J]. Transportation Research Part B: Methodological, 2017.
    [10]
    唐海川, 朱金陵, 王青元, 等. 一种可在线调整的列车正点运行节能操纵控制算法[J]. 中国铁道科学, 2013, 34(4): 89-94. doi: 10.3969/j.issn.1001-4632.2013.04.15

    TANG Haichuan, ZHU Jinling, WANG Qingyuan, et al. An online adjustable control algorithm for on-time and energy saving operations of trains[J]. China Railway Science, 2013, 34(4): 89-94. (in Chinese) doi: 10.3969/j.issn.1001-4632.2013.04.15
    [11]
    MASAFUMI M, HIDEYOSHI K, et al. 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
    [12]
    杨辉, 李莹, 周艳丽. 基于APSO的地铁节能运行速度优化研究[J]. 铁道科学与工程学报, 2020, 17(8): 1926-1934. https://www.cnki.com.cn/Article/CJFDTOTAL-CSTD202008006.htm

    YANG Hui, LI Ying, ZHOU Yanli. Research on optimization of energy-saving operation speed of metro based on APSO[J]. Journal of Railway Science and Engineering, 2020, 17(8): 1926-1934. (in Chinese) https://www.cnki.com.cn/Article/CJFDTOTAL-CSTD202008006.htm
    [13]
    黄友能, 宫少丰, 曹源, 等. 基于粒子群算法的城轨列车节能驾驶优化模型[J]. 交通运输工程学报, 2016, 16(2): 118-124+142. doi: 10.3969/j.issn.1671-1637.2016.02.014

    HUANG Youneng, GONG Shaofeng, CAO Yuan, et al. 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+142. (in Chinese) doi: 10.3969/j.issn.1671-1637.2016.02.014
    [14]
    陈昱, 侯涛, 杨宏阔. 基于双重优化的高速列车节能运行研究[J]. 铁道运输与经济, 2020, 42(7): 103-108. https://www.cnki.com.cn/Article/CJFDTOTAL-TDYS202007019.htm

    CHEN Yu, HOU Tao, YANG Hongkuo. A study on energy-saving operation of high-speed trains based on double optimization[J]. Railway Transport and Economy, 2020, 42(7): 103-108. (in Chinese) https://www.cnki.com.cn/Article/CJFDTOTAL-TDYS202007019.htm
    [15]
    周翔翔, 刘鑫荣, 张永, 等. 基于遗传优化算法的城市轨道交通多列车运行节能计算方法[J]. 城市轨道交通研究, 2018, 21(12): 67-70. https://www.cnki.com.cn/Article/CJFDTOTAL-GDJT201812021.htm

    ZHOU Xiangxiang, LIU Xinrong, ZHANG Yong, et al. Calculation of energy saving in multi-vehicle operation based on genetic optimization algorithm[J]. Urban Mass Transit, 2018, 21(12): 67-70. (in Chinese) https://www.cnki.com.cn/Article/CJFDTOTAL-GDJT201812021.htm
    [16]
    汤旻安, 王茜茜. 基于黄金比例遗传算法的动车组列车节能优化研究[J]. 铁道科学与工程学报, 2020, 17(1): 16-24. https://www.cnki.com.cn/Article/CJFDTOTAL-CSTD202001003.htm

    TANG Minan, WANG Qianqian. Research on energy-saving optimization of EMU trains based on golden ratio genetic algorithm[J]. Journal of Railway Science and Engineering, 2020, 17(1): 16-24. (in Chinese) https://www.cnki.com.cn/Article/CJFDTOTAL-CSTD202001003.htm
    [17]
    王青元. 高速列车准点节能优化操纵研究[D]. 成都: 西南交通大学, 2017.

    WANG Qingyuan. Energy-efficient control of high-speed trains considering punctuality constraint[D]. Chengdu: Southwest Jiaotong University, 2017. (in Chinese)
    [18]
    马晓娜. 基于萤火虫算法的高速列车节能运行优化研究[D]. 兰州: 兰州交通大学, 2019.

    MA Xiaona. Energy-saving optimization of high-speed train operation based on firefly algorithm[D]. Lanzhou: Lanzhou Jiaotong University, 2019. (in Chinese)
    [19]
    李娇杨, 陈光武. 基于改进遗传算法的高速列车节能优化研究[J]. 铁路计算机应用, 2021, 30(3): 5-9. doi: 10.3969/j.issn.1005-8451.2021.03.002

    LI Jiaoyang, CHEN Guangwu. Energy saving optimization of high-speed train based on improved genetic algorithm[J]. Railway Computer Application, 2021, 30(3): 5-9. (in Chinese) doi: 10.3969/j.issn.1005-8451.2021.03.002
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