基于模拟退火算法的列车节能操纵研究

doi:10.16182/j.issn1004731x.joss.201806040

系统仿真学报 ›› 2018, Vol. 30 ›› Issue (6): 2320-2327.doi: 10.16182/j.issn1004731x.joss.201806040

基于模拟退火算法的列车节能操纵研究

刘炜, 许嘉轩, 王沛沛, 刘瑞龙, 唐靖坤

西南交通大学电气工程学院,四川成都 610031

收稿日期:2016-08-03 修回日期:2016-11-01 出版日期:2018-06-08 发布日期:2018-06-14
作者简介:刘炜(通讯作者1982-),男,湖南益阳,博士,副教授,研究方向为牵引供电系统理论;许嘉轩(1995-),男,甘肃通渭,硕士,研究方向为城市轨道交通电气化与自动化。
基金资助:
国家自然科学基金 (51607168),中国铁路总公司科技研究开发计划 (2015J005-A)

Train Energy Saving Operation Based on Simulated Annealing Algorithm

Liu Wei, Xu Jiaxuan, Wang Peipei, Liu Ruilong, Tang Jingkun

School of Electric Engineering, Southwest Jiaotong University, Chengdu 610031, China

Received:2016-08-03 Revised:2016-11-01 Online:2018-06-08 Published:2018-06-14

摘要/Abstract

摘要： 城市轨道交通列车定时节能算法的研究久热不衰。针对地铁列车定时节能运行要求,通过对列车的运行区间进行分割,详细分析其运行迭代过程。利用微分法,建立了列车运行迭代模型,将定时节能问题转化为列车出力使用系数序列的求解,提出一种基于模拟退火算法的迭代寻优求解方法,并用MFC程序设计实现了该算法。选取上海地铁3号线的3个区间为算例,分别求得定时约束下的列车出力使用系数序列解及牵引能耗,和实际测试结果相比可知,在时间允许误差为的约束下,可节能4.77%~7.04%。

关键词: 城市轨道交通, 定时节能, 区间分割, 微分法, 模拟退火算法

Abstract: For the timing and energy saving algorithm of urban railway, the train operation iterative process was analyzed by dividing the operation intervals to meet the requirements of the subway trains timing energy saving operation. The differentiation was adopted to establish the train operation iterative model, and as a result the timing energy saving problem was transformed into the solution of train output using coefficient sequence. An optimization solving method based on simulated annealing algorithm was proposed and the algorithm was implemented using MFC program. Three intervals of Shanghai Metro Line 3 were selected and the solution of the train output was got using coefficient sequence and traction energy consumption with the timing constraints respectively. Compared with actual test results, it turned out to save energy ranging from 4.77% to 7.04% under the condition that the time allowed error is not more than ±2%.

Key words: urban railway, timing energy saving, dividing the interval, differentiation, simulated annealing algorithm

中图分类号:

U231+.6

刘炜, 许嘉轩, 王沛沛, 刘瑞龙, 唐靖坤. 基于模拟退火算法的列车节能操纵研究[J]. 系统仿真学报, 2018, 30(6): 2320-2327.

Liu Wei, Xu Jiaxuan, Wang Peipei, Liu Ruilong, Tang Jingkun. Train Energy Saving Operation Based on Simulated Annealing Algorithm[J]. Journal of System Simulation, 2018, 30(6): 2320-2327.

参考文献

[1] Bocharnikov Y V, Tobias A M, Roberts C, et al.Optimal driving strategy for traction energy saving on DC suburban railways[J]. Electric Power Applications, IETE (S1751-8660), 2007, 1(5): 675-682.
[2] 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]// Proceedings of the Third International Conference on Electric Utility Deregulation and Restructuring and Power Technologies, April 6-9, 2008, Nanjuing, China. Piscataway: IEEE Press, 2008: 2833-2839.
[3] Acikbas S, Sylemez M T.Coasting point optimization for mass rail transit lines using artificial neural networks and genetic algorithms[J]. Electric Power Applications, IETE (S1751-8660), 2008, 2(3): 172-182.
[4] Liu R R, Golovitcher I M.Energy-efficient operation of rail vehicles[J]. Transportation Research Part A: Policy and Practice (S0965-8564), 2003, 37(10): 917-932.
[5] 李玉生, 侯忠生. 基于遗传算法的列车节能控制研究[J].系统仿真学报,2007, 19(2):384-387.
LI Yusheng, HOU Zhongsheng.Study on Energy-saving Control for Train Based on Genetic Algorithm[J]. Journal of System simulation, 2007, 19(2):384-387.
[6] 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.
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.
[7] Howlett P G, Pudney P J, Xuan Vu.Local energy minimization in optimal train control[J]. Automatica (S0005-1098), 2009, 45(11): 2692-2698.
[8] 杨利军, 胡用生, 孙丽霞. 城市轨道交通节能线路仿真算法[J]. 同济大学学报(自然科学版),2012, 40(2): 235-240.
YANG Lijun, HU Yongsheng, SUN Lixia.Energy saving Track Profile of Urban Mass Transit[J]. JOURVAL OF TONGJI UVIVERSITY(NATURAL SCIENCE), 2012, 40(2): 235-240.
[9] 荀径, 唐涛, 宋晓美, 等. 再生制动条件下的城轨列车节能驾驶综合模型[J]. 中国铁道科学, 2015, 36(1): 104-110.
XUN Jing, TANG Tao, SONG Xiaomei, et al.Comprehensive Model for Energy-Saving Train Operation of Urban Mass Transit under Regenerative Brake[J]. CHINA RAILWAY SCIENCE, 2015, 36(1): 104-110.
[10] 刘炜, 李群湛, 郭蕾, 等. 基于多种群遗传算法的城轨列车节能运行优化研究[J]. 系统仿真学报, 2010, 22(4): 921-925.
Liu Wei, Li Qunzhan, Guo Lei, et al.Study of Urban Railway Energy Saving Train Control Optimization Based on Multi-population Genetic Algorithm[J]. Journal of System simulation, 2010, 22(4): 921-925.
[11] 王青元, 冯晓云, 朱金陵, 等. 考虑再生制动能量利用的高速列车节能最优控制仿真研究[J]. 中国铁道科学, 2015, 36(1): 96-103.
WANG Qingyuan, FENG Xiaoyun, ZHU Jinling, et al.Simulation Study on Optimal Energy-Efficient Control of High Speed Train Considering Regenerative Brake Energy[J]. CHINA RAILWAY SCIENCE, 2015, 36(1): 96-103.
[12] 罗亚中, 唐国金. 两层非线性规划问题的并行模拟退火全局优化[J]. 系统仿真学报, 2005, 17(5): 1040-1044.
Luo Yazhong, Tang Guojin.Global Optimization of Bilevel Nonlinear Programming Problems by Parallel Simulated Annealing[J]. Journal of System simulation, 2005, 17(5): 1040-1044.
[13] 闫利军, 李宗斌, 卫军胡. 模拟退火算法的一种参数设定方法研究[J]. 系统仿真学报, 2008, 20(1): 245-247.
Yan Lijun, Li Zongbin, Wei Junhu.Study on Parameter Setting Method for Simulated Annealing Algorithm[J]. Journal of System simulation, 2008, 20(1): 245-247.
[14] 刘爱军, 杨育, 李斐, 等. 混沌模拟退火粒子群优化算法研究及应用[J]. 浙江大学学报(工学版), 2013, 47(10): 1722-1730.
LIU Aijun, YANG Yu, LI Fei, et al.Chaotic simulated annealing particle swarm optimization algorithm research and its application[J]. Journal of Zhejiang University (Engineering Science), 2013, 47(10): 1722-1730.
[15] 王联国, 洪毅, 赵付青, 等. 一种模拟退火和粒子群混合优化算法[J]. 计算机仿真, 2008, 25(11): 179-182.
WANG Lianguo, HONG Yi, ZHAO Fuqing, et al.A Hybrid Algorithm of Simulated Annealing and Particle Swarm Optimization[J]. Computer Simulation, 2008, 25(11): 179-182.
[16] 傅文渊, 凌朝东. 布朗运动模拟退火算法[J]. 计算机学报, 2014, 37(6): 1301-1308.
FU Wenyuan, LING Chaodong.Brownian Motion Based Simulated Annealing Algorithm[J]. CHINESE JOURNAL OF COMPUTERS, 2014, 37(6): 1301-1308.

基于模拟退火算法的列车节能操纵研究

Train Energy Saving Operation Based on Simulated Annealing Algorithm

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