高速列车多区间节能操纵优化研究

交通运输系统工程与信息 ›› 2018, Vol. 18 ›› Issue (5): 178-183.

高速列车多区间节能操纵优化研究

麻存瑞，毛保华^*，柏赟，王敏，李佳杰，杨彦强

北京交通大学城市交通复杂系统理论与技术教育部重点实验室，北京 100044

收稿日期:2018-06-20 修回日期:2018-08-03 出版日期:2018-10-25 发布日期:2018-10-26
作者简介:麻存瑞(1986-)，男，甘肃兰州人，博士生.
基金资助:
中央高校基本科研业务费专项资金/The Fundamental Research Funds for the Central Universities (2018YJS089)；国家自然科学基金/National Natural Science Foundation of China (71390332, 71621001).

Energy-saving Operation Optimization for High-speed Train in Multi-interstation

MA Cun-rui, MAO Bao-hua, BAI Yun, WANG Min, LI Jia-jie, YANG Yan-qiang

MOE Key Laboratory for Urban Transportation Complex Systems Theory and Technology, Beijing Jiaotong University, Beijing 100044, China

Received:2018-06-20 Revised:2018-08-03 Online:2018-10-25 Published:2018-10-26

摘要/Abstract

摘要：

区间运行时间和操纵方法是实现高速列车节能运行的两个重要方面.本文构建了可调整区间运行冗余时间的高速列车多区间节能操纵模型.考虑到高铁枢纽车站和非枢纽站对列车到达时刻准点性的要求程度不同，模型中增加了枢纽车站的列车到达时刻与列车运行图定到达时刻一致性约束，以及非枢纽车站的列车到达时刻在一定时间范围内的约束.为了避免解空间中不满足定时约束的不可行解的数量影响算法效率，设计了一种三层编码的遗传算法来求解模型.通过1条包含3个枢纽车站、3个非枢纽车站的高速铁路线路验证，结果表明，本文所提出的高速列车多区间节能操纵方法能够保证枢纽车站列车到达时刻不变，非枢纽车站列车到达时刻在一定时间范围内变换时，求得多个区间的列车最优节能操纵速度轨迹.与基于牵引—惰行控制方法和单区间节能操纵方法在图定运行时分下的计算结果相比，本文所提出的方法节能率分别超过16%和4%.

关键词: 铁路运输, 节能优化, 遗传算法, 列车操纵, 区间运行冗余时分

Abstract:

The running time and operation method are two important factors for realizing high-speed train energysaving operation. In this paper, a multi-interstation energy-saving operation method for high-speed train with adjustable running time supplement is constructed. Considering the requirement difference in the arrival time of high-speed train arriving at hub station and non-hub station, the model added to the constraint that the train arrival time is the same as the fixed arrival time in timetabling for hub station, and the constraints in the time range of the arrival time of the train arriving at non-hub station. In order to avoid the situation that a number of unfeasible solutions unsatisfied the timing constraints in the solution space might have defect on the algorithm efficiency, a three layer coded genetic algorithm is designed to solve the model in the paper. Through the verification of a highspeed railway line including 3 hub stations and 3 non-hub stations, the results show that the multi-interstation energy-saving operation method for high-speed train proposed in the paper could obtain the optimal multiinterstation energy-saving speed trajectory, when ensure that the arrival time of hub station is the same as the time in timetabling and the arrival time of the non-hub train station is within a certain time range. Compared with the calculation results based on the acceleration-cruising method and single interstation energy-saving operation method in the paper under the running time in timetabling, the proposed method could save more than 16% and 4% energy respectively.

Key words: railway transportation, energy-saving optimization, genetic algorithm, train operation, running time supplement

中图分类号:

U231.92

麻存瑞，毛保华，柏赟，王敏，李佳杰，杨彦强. 高速列车多区间节能操纵优化研究[J]. 交通运输系统工程与信息, 2018, 18(5): 178-183.

MA Cun-rui, MAO Bao-hua, BAI Yun, WANG Min, LI Jia-jie, YANG Yan-qiang. Energy-saving Operation Optimization for High-speed Train in Multi-interstation[J]. Journal of Transportation Systems Engineering and Information Technology, 2018, 18(5): 178-183.

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链接本文: http://www.tseit.org.cn/CN/abstract/abstract19712.shtml

http://www.tseit.org.cn/CN/Y2018/V18/I5/178

参考文献

[1] AlLBRECHT T, OETTICH S. A new integrated approach to dynamic schedule synchronization and energy-saving train control[J]. Computers in Railways VIII, 2002: 847-856.

[2] 丁勇, 刘海东, 柏赟, 等. 地铁列车节能运行的两阶段优化模型算法研究[J]. 交通运输系统工程与信息, 2011, 11(1): 96-101. [DING Y, LIU H D, BAI Y, et al. A two-level optimization model and algorithm for energy- efficient urban train operation[J]. Journal of Transportation Systems Engineering and Information Technology, 2011, 11(1): 96-101.]

[3] LIXING YANG, KEPING LI, ZIYOU GAO, et al. Optimizing trains movement on a railway network[J]. Omega, 2012, 40(5): 619-633.

[4] YANG X, CHEN A, NING B, et al. A stochastic model for the integrated optimization on metro timetable and speed profile with uncertain train mass[J]. Transportation Research Part B: Methodological, 2016 (91): 424-445.

[5] SHUAI SU, XIANG LI, TAO TANG, et al. Optimization of multi- train operations in a subway system[J]. IEEE Transactions on Intelligent Transportation Systems, 2014, 15(2): 673-684.

[6] HOWLETT P G. An optimal strategy for the control of a train[J]. Journal of the Australian Mathematical Society, 1990, 31(4): 454-471.

[7] PUDNEY P, HOWLETT P G. Optimal driving strategies for a train journey with speed limits[J]. Anziam Journal, 1994, 36(1): 38-49.

[8] HOWLETT P G, CHENG J X. Optimal driving strategies for a train on a track with continuously varying gradient[J]. Journal of the Australian Mathematical Society, 1997, 38(3): 388-410.

[9] 丁勇, 毛保华, 刘海东, 等. 定时约束条件下列车节能操纵的仿真算法研究[J]. 系统仿真学报, 2004, 16 (10): 2241-2244. [DING Y, MAO B H, LIU H D, et al. An algorithm for energy-efficient train operation simulation with fixed running time[J]. Journal of System Simulation, 2004, 16(10): 2241-2244.]

[10] 王鹏玲, 林轩, 李跃宗, 等. 自适应遗传算法在列车节能优化中的应用[J]. 计算机仿真, 2012, 29(11): 350-354. [WANG P L, LIN X, LI Y Z, et al. Optimal switching for control conditions of punctual and energy efficient operation of train[J]. Computer Simulation, 2012, 29(11): 350-354.]

[11] 王青元, 冯晓云. 列车准点节能运行的控制工况最优切换研究 [J]. 中国铁道科学, 2016, 37(2): 91-98. [WANG Q Y, FENG X Y. Simulation study on optimal energy- efficient control of high speed train considering regenerative brake energy[J]. China Railway Science, 2016, 37(2): 91-98.]

[12] DE J. An analysis of the behavior: A class of genetic adaptive systems[J]. Doctoral Dissertation University of Michigan, 1975: 76-9381.

[13] GOLDBERG D E. Genetic algorithms in search[J]. Optimization & Machine Learning, 1989(3): 95-99.

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