高速列车动态间隔优化的弹性调整策略
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摘要
为保证列车运行安全性,提高铁路线路运载效能,针对移动闭塞系统,研究了高速列车追踪运行的间隔弹性调整策略和操纵轨迹的动态优化问题;以高速列车运行安全性、效率、能耗和乘客舒适度作为列车运行控制策略曲线的优化目标,研究了列车的追踪运行过程;采用差分进化算法求解了列车运行过程多目标优化模型,设计了离线最优运行控制策略曲线;提出了列车弹性追踪间隔模型,分析了列车运行过程中追踪间隔的实时变化;基于弹性间隔模型设计列车追踪运行控制策略动态调整机制,采集列车实际运行数据,实时监测相邻列车间的实际追踪间隔,评估其是否符合安全性与效率约束条件,并分析了评估结果;依据工况调整原则在线调整追踪列车的运行状态与工况,实时优化列车追踪间隔;应用武广高速铁路赤壁北—长沙南区间的实际运行数据进行了仿真验证。仿真结果表明:与真实区间运行数据相比,采用离线最优运行控制策略曲线后,运行能耗降低了6.86%;与固定追踪时间间隔模型相比,采用基于弹性模型的控制策略动态调整机制有效提升了铁路整体运输效能,将临界安全发车间隔从234 s缩短至161 s,线路整体运行效率由6 434 s缩短至6 376 s,与真实运行数据相比,追踪列车的运行能耗降低了7.194%。
To ensure the train operation safety and improve the carrying efficiency of railway line, the interval elastic adjustment strategy of high-speed train tracking operation and dynamic optimization of manipulating trajectory were researched under the moving block system. The optimal objectives including the operation safety, efficiency, energy consumption of high-speed train and comfort of passengers were taken into account to obtain the train operation control strategy curve, and the train tracking operation process was researched. The multi-objective optimization of high-speed train model of train operation process was solved through the differential evolution algorithm, and the offline optimal operation control strategy curve was obtained. The train elastic tracking interval model was proposed, and the real-time change of tracking interval during the train operation process was analyzed. On the basis of the elastic interval model, a dynamic train tracking operation control strategy adjustment mechanism was designed. The train actual operation data were collected, and the actual tracking interval between adjacent trains was monitored in real-time. The interval was evaluated whether it meets the safety and time-efficiency constraints, and the assessment result was analyzed. The following train's operation state and condition were adjusted online according to the conversion principle of operation phases, and the train tracking interval was optimized in real-time. The numerical simulation using the real operation data of Wuhan-Guangzhou High-speed Railway Line from Chibi North Station to Changsha South Station was conducted. Simulation result indicates that compared with the real section operation data, the energy consumption reduces by 6.86% by adopting the offline optimal operation control strategy curve. Compared with the fixed tracking time interval model, the transport efficiency of the overall railway line is efficiently promoted by adopting the control strategy dynamic adjustment mechanism based on the elastic model, which reduces the critical safety departure interval from 234 s to 161 s. The overall railway line operation efficiency is shortened from 6 434 s to 6 376 s, and the energy consumption of tracking train reduces by 7.194% compared with the actual operation data.
To ensure the train operation safety and improve the carrying efficiency of railway line, the interval elastic adjustment strategy of high-speed train tracking operation and dynamic optimization of manipulating trajectory were researched under the moving block system. The optimal objectives including the operation safety, efficiency, energy consumption of high-speed train and comfort of passengers were taken into account to obtain the train operation control strategy curve, and the train tracking operation process was researched. The multi-objective optimization of high-speed train model of train operation process was solved through the differential evolution algorithm, and the offline optimal operation control strategy curve was obtained. The train elastic tracking interval model was proposed, and the real-time change of tracking interval during the train operation process was analyzed. On the basis of the elastic interval model, a dynamic train tracking operation control strategy adjustment mechanism was designed. The train actual operation data were collected, and the actual tracking interval between adjacent trains was monitored in real-time. The interval was evaluated whether it meets the safety and time-efficiency constraints, and the assessment result was analyzed. The following train's operation state and condition were adjusted online according to the conversion principle of operation phases, and the train tracking interval was optimized in real-time. The numerical simulation using the real operation data of Wuhan-Guangzhou High-speed Railway Line from Chibi North Station to Changsha South Station was conducted. Simulation result indicates that compared with the real section operation data, the energy consumption reduces by 6.86% by adopting the offline optimal operation control strategy curve. Compared with the fixed tracking time interval model, the transport efficiency of the overall railway line is efficiently promoted by adopting the control strategy dynamic adjustment mechanism based on the elastic model, which reduces the critical safety departure interval from 234 s to 161 s. The overall railway line operation efficiency is shortened from 6 434 s to 6 376 s, and the energy consumption of tracking train reduces by 7.194% compared with the actual operation data.
引文
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[9] LU Shao-feng, WANG Ming-qiang, WESTON P, et al. Partial train speed trajectory optimization using mixed-integer linear programming[J]. IEEE Transactions on Intelligent Transportation Systems, 2016, 17(10): 2911-2920.
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[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.HUANG You-neng, GONG Shao-feng, 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)
[14] 李诚,王小敏.基于粒子群优化的ATO控制策略[J].铁道学报,2017,39(3):53-58.LI Cheng, WANG Xiao-min. An ATO control strategy based on particle swarm optimization[J]. Journal of the China Railway Society, 2017, 39(3): 53-58. (in Chinese)
[15] KESKIN K, KARAMANCIOGLU A. Energy-efficient train operation using nature-inspired algorithms[J]. Journal of Advanced Transportation, 2017, 2017: 1-12.
[16] YIN Jia-teng, CHEN De-wang, LI Ling-xi. Intelligent train operation algorithms for subway by expert system and reinforcement learning[J]. IEEE Transactions on Intelligent Transportation Systems, 2014, 15(6): 2561-2571.
[17] 高浠瑞,董海鹰,杨立霞.高速列车追踪运行的多目标优化研究[J].铁道科学与工程学报,2016,13(12):2335-2340.GAO Xi-rui, DONG Hai-ying, YANG Li-xia. Research on multi-objective optimization for tracking operation of high-speed train[J]. Journal of Railway Science and Engineering, 2016, 13(12): 2335-2340. (in Chinese)
[18] ACIKBAS S, SOYLEMEZ M T. Coasting point optimisation for mass rail transit lines using artificial neural networks and genetic algorithms[J]. IET Electric Power Applications, 2008, 2(3): 172-182.
[19] 金炜东,靳蕃,李崇维,等.列车优化操纵速度模式曲线生成的智能计算研究[J].铁道学报,1998,20(5):47-52.JIN Wei-dong, JIN Fan, LI Chong-wei, et al. Study on intelligent computation of velocity schema curve of optimization operation for train[J]. Journal of the China Railway Society, 1998, 20(5): 47-52. (in Chinese)
[20] 刘海东,毛保华,何天健,等.不同闭塞方式下城轨列车追踪运行过程及其仿真系统的研究[J].铁道学报,2005,27(2):120-125.LIU Hai-dong, MAO Bao-hua, HO Tin-kin, et al. Study on tracking operations between trains of different block modes and simulation system[J]. Journal of the China Railway Society, 2005, 27(2): 120-125. (in Chinese)
[21] 路飞,宋沐民,李晓磊.基于移动闭塞原理的地铁列车追踪运行控制研究[J].系统仿真学报,2005,17(8):1944-1947,1950.LU Fei, SONG Mu-min, LI Xiao-lei. Research on subway train following control system under moving block system[J]. Journal of System Simulation, 2005, 17(8): 1944-1947, 1950. (in Chinese)
[22] 诸蓉萍,吴汶麒.移动闭塞技术及其应用[J].城市轨道交通研究,2004,7(2):81-82.ZHU Rong-ping, WU Wen-qi. Application of moving block technology in UMT[J]. Urban Mass Transit, 2004, 7(2): 81-82. (in Chinese)
[23] WANG Yi-hui, SCHUTTER B D, VAN DEN BOOM T J J, et al. Optimal trajectory planning for trains under fixed and moving signaling systems using mixed integer linear programming[J]. Control Engineering Practice, 2014, 22(1): 44-56.
[24] YE Hong-bo, LIU Rong-hui. A multiphase optimal control method for multi-train control and scheduling on railway lines[J]. Transportation Research Part B: Methodological, 2016, 93: 377-393.
[25] YAN Xi-hui, CAI Bai-gen, NING Bin, et al. Online distributed cooperative model predictive control of energy-saving trajectory planning for multiple high-speed train movements[J]. Transportation Research Part C: Emerging Technologies, 2016, 69: 60-78.
[26] GU Qing, TANG Tao, MA Fei. Energy-efficient train tracking operation based on multiple optimization models[J]. IEEE Transactions on Intelligent Transportation Systems, 2016, 17(3): 882-892.
[27] 熊烈强,王富,李杰.路段交通流的动力学模型及其仿真[J].中国公路学报,2006,19(2):91-94.XIONG Lie-qiang, WANG Fu, LI Jie. Dynamical model of traffic flow on segment and its simulation [J]. China Journal of Highway and Transport, 2006, 19(2): 91-94. (in Chinese)
[28] 潘登,郑应平.高速列车追踪运行的控制机理研究[J].铁道学报,2013,35(3):53-61.PAN Deng, ZHENG Ying-ping. Study on the mechanism of high-speed train following operation control[J]. Journal of the China Railway Society, 2013, 35(3): 53-61. (in Chinese)
[29] 江靖.新一代高速动车组牵引系统参数匹配设计与研究[J].机车电传动,2011(3):9-12,36.JIANG Jing. Traction system parameter matching design and research of new-generation high-speed EMUs[J]. Electric Drive for Locomotives, 2011(3): 9-12, 36. (in Chinese)
[30] 侯赞,陈德旺,李焱.基于集成模糊推理的列车运行舒适度评价方法及应用[J].铁路计算机应用,2012,21(7):4-7.HOU Zan, CHEN De-wang, LI Yan.Comfort evaluation method and its application for train operation based on ensemble fuzzy reasoning[J]. Railway Computer Application, 2012, 21(7): 4-7. (in Chinese)
[2] GONZáLEZ-GIL A, PALACIN R, BATTY P, et al. A systems approach to reduce urban rail energy consumption[J]. Energy Conversion and Management, 2014, 80: 509-524.
[3] HOWLETT P. An optimal strategy for the control of a train[J]. The Journal of the Australian Mathematical Society, Series B: Applied Mathematics, 1990, 31(4): 454-471.
[4] SU Shuai, TANG Tao, WANG Yi-hui. Evaluation of strategies to reducing traction energy consumption of metro systems using an optimal train control simulation model[J]. Energies, 2016, 9(2): 1-19.
[5] CHANG Che-san, XU D Y, QUEK H B. Pareto-optimal set based multiobjective tuning of fuzzy automatic train operation for mass transit system[J]. IEE Proceedings—Electric Power Applications, 1999, 146(5): 577-583.
[6] 余进,何正友,钱清泉.基于混合微粒群优化的多目标列车控制研究[J].铁道学报,2010,32(1):38-42.YU Jin, HE Zheng-you, QIAN Qing-quan. Study on multi-objecive train control based on hybrid particle swarm optimization[J]. Journal of the China Railway Society, 2010, 32(1): 38-42. (in Chinese)
[7] SHANGGUAN Wei, YAN Xi-hui, CAI Bai-gen, et al. Multiobjective optimization for train speed trajectory in CTCS high-speed railway with hybrid evolutionary algorithm[J]. IEEE Transactions on Intelligent Transportation Systems, 2015, 16(4): 2215-2225.
[8] LIU Rong-fang, GOLOVITCHER I M. Energy-efficient operation of rail vehicles[J]. Transportation Research Part A: Policy and Practice, 2003, 37(10): 917-932.
[9] LU Shao-feng, WANG Ming-qiang, WESTON P, et al. Partial train speed trajectory optimization using mixed-integer linear programming[J]. IEEE Transactions on Intelligent Transportation Systems, 2016, 17(10): 2911-2920.
[10] CHANG Che-san, SIM S S. Optimising train movements through coast control using genetic algorithms[J]. IEE Proceedings—Electric Power Applications, 1997, 144(1): 65-73.
[11] GOODWIN J C J, FLETCHER D I, HARRISON R F.Multi-train trajectory optimisation to maximise rail network energy efficiency under travel-time constraints[J]. Journal of Rail and Rapid Transit, 2016, 230(4): 1-34.
[12] CHUANG Hui-jen, CHEN Chao-shun, LIN Chia-hung, et al. Design of optimal coasting speed for MRT systems using ANN models[J]. IEEE Transactions on Industry Applications, 2009, 45(6): 2090-2097.
[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.HUANG You-neng, GONG Shao-feng, 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)
[14] 李诚,王小敏.基于粒子群优化的ATO控制策略[J].铁道学报,2017,39(3):53-58.LI Cheng, WANG Xiao-min. An ATO control strategy based on particle swarm optimization[J]. Journal of the China Railway Society, 2017, 39(3): 53-58. (in Chinese)
[15] KESKIN K, KARAMANCIOGLU A. Energy-efficient train operation using nature-inspired algorithms[J]. Journal of Advanced Transportation, 2017, 2017: 1-12.
[16] YIN Jia-teng, CHEN De-wang, LI Ling-xi. Intelligent train operation algorithms for subway by expert system and reinforcement learning[J]. IEEE Transactions on Intelligent Transportation Systems, 2014, 15(6): 2561-2571.
[17] 高浠瑞,董海鹰,杨立霞.高速列车追踪运行的多目标优化研究[J].铁道科学与工程学报,2016,13(12):2335-2340.GAO Xi-rui, DONG Hai-ying, YANG Li-xia. Research on multi-objective optimization for tracking operation of high-speed train[J]. Journal of Railway Science and Engineering, 2016, 13(12): 2335-2340. (in Chinese)
[18] ACIKBAS S, SOYLEMEZ M T. Coasting point optimisation for mass rail transit lines using artificial neural networks and genetic algorithms[J]. IET Electric Power Applications, 2008, 2(3): 172-182.
[19] 金炜东,靳蕃,李崇维,等.列车优化操纵速度模式曲线生成的智能计算研究[J].铁道学报,1998,20(5):47-52.JIN Wei-dong, JIN Fan, LI Chong-wei, et al. Study on intelligent computation of velocity schema curve of optimization operation for train[J]. Journal of the China Railway Society, 1998, 20(5): 47-52. (in Chinese)
[20] 刘海东,毛保华,何天健,等.不同闭塞方式下城轨列车追踪运行过程及其仿真系统的研究[J].铁道学报,2005,27(2):120-125.LIU Hai-dong, MAO Bao-hua, HO Tin-kin, et al. Study on tracking operations between trains of different block modes and simulation system[J]. Journal of the China Railway Society, 2005, 27(2): 120-125. (in Chinese)
[21] 路飞,宋沐民,李晓磊.基于移动闭塞原理的地铁列车追踪运行控制研究[J].系统仿真学报,2005,17(8):1944-1947,1950.LU Fei, SONG Mu-min, LI Xiao-lei. Research on subway train following control system under moving block system[J]. Journal of System Simulation, 2005, 17(8): 1944-1947, 1950. (in Chinese)
[22] 诸蓉萍,吴汶麒.移动闭塞技术及其应用[J].城市轨道交通研究,2004,7(2):81-82.ZHU Rong-ping, WU Wen-qi. Application of moving block technology in UMT[J]. Urban Mass Transit, 2004, 7(2): 81-82. (in Chinese)
[23] WANG Yi-hui, SCHUTTER B D, VAN DEN BOOM T J J, et al. Optimal trajectory planning for trains under fixed and moving signaling systems using mixed integer linear programming[J]. Control Engineering Practice, 2014, 22(1): 44-56.
[24] YE Hong-bo, LIU Rong-hui. A multiphase optimal control method for multi-train control and scheduling on railway lines[J]. Transportation Research Part B: Methodological, 2016, 93: 377-393.
[25] YAN Xi-hui, CAI Bai-gen, NING Bin, et al. Online distributed cooperative model predictive control of energy-saving trajectory planning for multiple high-speed train movements[J]. Transportation Research Part C: Emerging Technologies, 2016, 69: 60-78.
[26] GU Qing, TANG Tao, MA Fei. Energy-efficient train tracking operation based on multiple optimization models[J]. IEEE Transactions on Intelligent Transportation Systems, 2016, 17(3): 882-892.
[27] 熊烈强,王富,李杰.路段交通流的动力学模型及其仿真[J].中国公路学报,2006,19(2):91-94.XIONG Lie-qiang, WANG Fu, LI Jie. Dynamical model of traffic flow on segment and its simulation [J]. China Journal of Highway and Transport, 2006, 19(2): 91-94. (in Chinese)
[28] 潘登,郑应平.高速列车追踪运行的控制机理研究[J].铁道学报,2013,35(3):53-61.PAN Deng, ZHENG Ying-ping. Study on the mechanism of high-speed train following operation control[J]. Journal of the China Railway Society, 2013, 35(3): 53-61. (in Chinese)
[29] 江靖.新一代高速动车组牵引系统参数匹配设计与研究[J].机车电传动,2011(3):9-12,36.JIANG Jing. Traction system parameter matching design and research of new-generation high-speed EMUs[J]. Electric Drive for Locomotives, 2011(3): 9-12, 36. (in Chinese)
[30] 侯赞,陈德旺,李焱.基于集成模糊推理的列车运行舒适度评价方法及应用[J].铁路计算机应用,2012,21(7):4-7.HOU Zan, CHEN De-wang, LI Yan.Comfort evaluation method and its application for train operation based on ensemble fuzzy reasoning[J]. Railway Computer Application, 2012, 21(7): 4-7. (in Chinese)