Train adaptive energy saving strategy based on iterative induced genetic algorithm

MEI Wenqing; SHI Ke; ZHANG Zhengfang

doi:10.13890/j.issn.1000-128X.2023.03.015

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Train adaptive energy saving strategy based on iterative induced genetic algorithm

Electric Traction and Control | 更新时间：2024-08-02

- Train adaptive energy saving strategy based on iterative induced genetic algorithm
- Electric Drive for Locomotives Issue 3, Pages: 117-123(2023)
- 作者机构：
  
  株洲中车时代电气股份有限公司，湖南株洲　　412001
- 作者简介：
- 基金信息：
- DOI：10.13890/j.issn.1000-128X.2023.03.015
  CLC： U268.7
- Published：10 May 2023，
  
  Received：13 December 2022，
- 稿件说明：
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梅文庆, 史可, 张征方. 基于迭代诱导遗传算法的列车自适应节能策略研究[J]. 机车电传动, 2023(3): 117-123.

MEI Wenqing, SHI Ke, ZHANG Zhengfang. Train adaptive energy saving strategy based on iterative induced genetic algorithm[J]. Electric Drive for Locomotives,2023(3): 117-123.
梅文庆, 史可, 张征方. 基于迭代诱导遗传算法的列车自适应节能策略研究[J]. 机车电传动, 2023(3): 117-123. DOI： 10.13890/j.issn.1000-128X.2023.03.015.

MEI Wenqing, SHI Ke, ZHANG Zhengfang. Train adaptive energy saving strategy based on iterative induced genetic algorithm[J]. Electric Drive for Locomotives,2023(3): 117-123. DOI： 10.13890/j.issn.1000-128X.2023.03.015.

摘要

针对既有列车节能策略未合理考虑复杂操纵特性约束和动态牵引系统效率问题，文章基于牵引系统效率特性和轮周机械功构建列车运行能耗精细计算模型，提出基于迭代诱导遗传算法的自适应节能策略，设计考虑牵引系统效率的能耗目标函数，并在操纵特性约束下求解目标函数，实现最优节能规划。首先，根据列车线路坡道将优化目标设计为若干个子区间，将子区间的线路特征信息转化为状态约束和控制约束，提高了节能目标对线路信息变化下操纵特性的自适应性；然后，为克服传统遗传算法由于离散独立难以解决连续子区间优化问题的特点，设计迭代诱导遗传算法，在迭代诱导遗传算法中对节能目标函数计算Pareto最优解集；最后，使用MATLAB软件对算法进行仿真验证。不同线路信息的仿真结果表明，基于迭代诱导遗传算法的自适应节能策略在保证运行时间的条件下具有良好的节能性能，相比于恒速和恒力运行，节能指标提高3%~13%。

Abstract

In response to the inefficient consideration of complex manipulation characteristics constraints and dynamic traction system efficiency issues in existing train energy-saving strategies

a detailed energy consumption calculation model for train operation was proposed based on the efficiency characteristics of the traction system and the mechanical work of the wheel rotation. An adaptive energy-saving strategy based on an iterative induced genetic algorithm was presented

and an energy consumption objective function considering the traction system efficiency was designed. The objective function was solved under manipulation characteristic constraints to achieve optimal energy-saving planning. Firstly

the optimization objective was designed as several sub-intervals based on the ramp of the train route. The route characteristic information of the sub-intervals was transformed into state and control constraints

improving the adaptivity of the energy-saving objective to the manipulation characteristics under changes in route information. Then

to overcome the difficulty of traditional genetic algorithms in solving continuous sub-interval optimization problems due to discrete independence

an iterative induced genetic algorithm was designed to calculate the Pareto optimal solution set for the energy-saving objective function. Finally

the algorithm was simulated and validated on the MATLAB platform. The simulation results of different line information show that the adaptive energy-saving strategy based on iterative induced genetic algorithm has good energy-saving performance under the condition of ensuring running time

with an energy-saving index improvement of 3%-13% compared to constant speed and constant force operation.

关键词

节能策略复杂操纵特性约束牵引效率动态特性迭代诱导遗传算法高速列车

Keywords

energy saving strategyconstraints on complex manipulation characteristicsdynamic characteristics of traction efficiencyiterative inductiongenetic algorithmhigh-speed train

references

ICHIKAWA K. Application of optimization theory for bounded state variable problems to the operation of train[J]. Bulletin of JSME, 1968, 11(47): 857-865.

HOWLETT P G, PUDNEY P J, VU X. Local energy minimization in optimal train control[J]. Automatica, 2009, 45(11): 2692-2698.

MILROY I P. Aspects of automatic train control[D]. Loughborough: Loughborough University, 1980.

CHENG Jiaxin, HOWLETT P. Application of critical velocities to the minimisation of fuel consumption in the control of trains[J]. Automatica, 1992, 28(1): 165-169.

KOKOTOVIC P, SINGH G. Minimum-energy control of a traction motor[J]. IEEE Transactions on Automatic Control, 1972, 17(1): 92-95.

KHMELNITSKY E. On an optimal control problem of train operation[J]. IEEE Transactions on Automatic Control, 2000, 45(7): 1257-1266.

LU Shaofeng, HILLMANSEN S, HO T K, et al. Single-train trajectory optimization[J]. IEEE Transactions on Intelligent Transportation Systems, 2013, 14(2): 743-750.

LIN Xuan, WANG Qingyuan, WANG Pengling, et al. The energy-efficient operation problem of a freight train considering long-distance steep downhill sections[J]. Energies, 2017, 10(6): 794.

LU Shaofeng. Optimising power management strategies for railway traction systems[D]. Birmingham: University of Birmingham, 2011.

KE B R, LIN Chunliang, LAI Chiwen. Optimization of train-speed trajectory and control for mass rapid transit systems[J]. Control Engineering Practice, 2011, 19(7): 675-687.

陈万里, 程家兴. 基于模拟退火算法(SAA)求解列车控制问题[J]. 安徽大学学报(自然科学版), 2000, 24(3): 46-49.

CHEN Wanli, CHENG Jiaxing. Simulated annealing algorithm for solving train control problem[J]. Journal of Anhui University (Natural Science Edition), 2000, 24(3): 46-49.

严细辉, 蔡伯根, 宁滨, 等. 基于差分进化的高速列车运行操纵的多目标优化研究[J]. 铁道学报, 2013, 35(9): 65-71.

YAN Xihui, CAI Baigen, NING Bin, et al. Research on multi-objective high-speed train operation optimization based on differential evolution[J]. Journal of the China Railway Society, 2013, 35(9): 65-71.

LIN W S, SHEU J W. Automatic train regulation for metro lines using dual heuristic dynamic programming[J]. Proceedings of the Institution of Mechanical Engineers, Part F: Journal of Rail and Rapid Transit, 2010, 224(1): 15-23.

张勇, 谭南林. 基于遗传算法的内燃机车优化操纵研究[J]. 电子测量与仪器学报, 2013, 27(7): 604-609.

ZHANG Yong, TAN Nanlin. Study on locomotive optimizing control based on genetic algorithm[J]. Journal of Electronic Measurement and Instrument, 2013, 27(7): 604-609.

陈荣武, 刘莉, 郭进. 基于遗传算法的列车运行能耗优化算法[J]. 交通运输工程学报, 2012, 12(1): 108-114.

CHEN Rongwu, LIU Li, GUO Jin. Optimization algorithm of train operation energy consumption based on genetic algorithm[J]. Journal of Traffic and Transportation Engineering, 2012, 12(1): 108-114.

刘亚丽, 马世伟. 基于遗传算法的列车自动运行系统的优化研究[J]. 电子测量技术, 2013, 36(4): 36-39.

LIU Yali, MA Shiwei. Optimized research on the automatic train operation based on genetic algorithm[J]. Electronic Measurement Technology, 2013, 36(4): 36-39.

许立, 王长林. 基于遗传算法列车自动运行速度曲线的优化[J]. 铁路计算机应用, 2013, 22(10): 46-49.

XU Li, WANG Changlin. Optimization of automatic train operation speed curve based on genetic algorithm[J]. Railway Computer Application, 2013, 22(10): 46-49.

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.

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.

纪云霞. 列车节能运行优化的改进遗传算法研究[D]. 成都: 西南交通大学, 2018.

JI Yunxia. Research on improved genetic algorithm for train energy-saving operation optimization[D]. Chengdu: Southwest Jiaotong University, 2018.

WONG K K, HO T K. Coast control of train movement with genetic algorithm[C] //IEEE. The 2003 Congress on Evolutionary Computation. Canberra: IEEE, 2003.

DEB K, PRATAP A, AGARWAL S, et al. A fast and elitist multiobjective genetic algorithm: NSGA-Ⅱ[J]. IEEE Transactions on Evolutionary Computation, 2002, 6(2): 182-197.

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.

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