基于参数随机分布特性的多质点重载列车安全制动模型研究

Research on Multi-Mass Safety Braking Model for Heavy-Haul Trains Based on Random Distribution of Parameters

列车虚拟联挂技术能够大幅提升线路运输能力,是轨道交通新兴技术的研究热点,其采用动态追踪技术构建新的安全制动模型,但已有研究只考虑了紧急制动、最短追踪距离等边界问题,无法适应重载列车领域,也未解决重载列车线路复杂、空气制动离散性强等问题。针对上述问题,文章提出一种基于参数随机分布特性的多质点重载列车安全制动模型。文章首先基于列车虚拟联挂系统的拓扑结构进行列车多质点动力学分析,建立车钩缓冲器模型,参照CBTC(基于通信的列车控制系统)安全模型,设计出重载列车的安全制动模型;然后,对该模型进行仿真分析,从纵向动力学平稳性角度评估跟随车采用不同导向安全措施的技术指标,并分析典型参数在随机变化时安全制动距离的分布函数特性;最后,进行列车运动学仿真试验。验证结果表明,所提模型在提升列车运行的安全置信度约为1时,能够大大缩短列车间的追踪距离(1.4km以下),提升了列车的运输效率及安全性。

The virtual coupling technology of trains significantly improves the transportation capacity of railway lines,making it a research hotspot among emerging technologies in the rail transit industry.Its key technical feature lies in a new safety braking model developed based on the concept of"dynamic tracking".However,existing research only addresses boundary issues such as emergency braking and minimum headways,lacking applicability in the context of heavy-haul trains,due to the absence of solutions for complex track conditions,strong dispersion in air braking capability,and other unfavorable factors.This paper presents a multi-mass safety braking model specifically for heavy-haul trains,based on the random distribution of parameters,to solve these challenges.Initially,a multi-mass dynamics analysis of trains was conducted based on the topological structure of the virtual coupling system of trains,leading to the establishment of a coupler-draft gear model.Subsequently,a safety braking model for heavy-haul trains was designed by referencing the safety model of the communication-based train control system(CBTC).This model was then utilized for simulation analysis,where technical indexes were evaluated from the perspective of longitudinal dynamics stability,corresponding to different fail-safe measures taken on following trains.In addition,the characteristics of the distribution function for safe braking distances were analyzed in relation to the random variation of typical parameters.Furthermore,verification was performed through the kinematic simulations of trains.Results showed that the proposed model resulted in greatly shortened headways below 1.4 km while improving the safety confidence by about 1 for trains,demonstrating its efficacy in improving both the efficiency and safety of train transportation.