控制与信息技术Issue(4)：28-35,8.DOI:10.13889/j.issn.2096-5427.2024.04.004
适应起伏坡道线路的重载列车运行曲线规划技术研究
Research on Operation Curve Planning Technology Adaptive to Undulating Tracks for Heavy-Haul Trains
摘要
Abstract
The operation of heavy-haul trains is susceptible to train impulses and excessive energy consumption due to improper control,which can lead to safety incidents such as coupler jumping and breaking. These issues arise from their long formations and heavy loads,as well as the complex longitudinal profiles of heavy-haul railways. In order to explore an optimal control strategy for heavy-haul trains running on undulating tracks,this paper proposes a curve planning algorithm based on the ant colony algorithm for sectional operation. Firstly,a multi-mass model of train longitudinal dynamics was established,and Zhai's method was applied to determine the model's state variables. Secondly,from the perspective of coupler forces,a safety evaluation index based on coupler force constraints and a stability evaluation index based on changes in coupler force states were established. By integrating the actual operational constraints of heavy-haul trains,a multi-objective optimization mathematical model with constraints was constructed. Finally,the ant colony algorithm was used to solve this model,resulting in the generation of the optimal control curve for heavy-haul trains running on undulating track sections. Based on actual track data,the proposed algorithm was verified through simulations. The results showed that,compared with operation by highly skilled drivers,the algorithm reduced the number of coupler state transitions and the slope index of coupler force changes by more than 3％ on average,demonstrating an effective improvement in operational quality for heavy-haul trains.关键词
重载列车/起伏坡道/操纵优化/多目标优化/蚁群算法Key words
heavy haul train/undulating track/control optimization/multi-objective optimization/ant colony algorithm分类
交通运输引用本文复制引用
蒋杰,张征方,罗源,周黄标,熊佳远..适应起伏坡道线路的重载列车运行曲线规划技术研究[J].控制与信息技术,2024,(4):28-35,8.基金项目
中国国家铁路集团有限公司科技研究开发计划重大课题(SY2023J002) （SY2023J002）
