基于工况分析的城市轨道列车自动行车系统多目标优化策略

传统的自动行车系统(ATO)具有良好的自动行车能力;但系统为达到行驶目的,通常会进行频繁地工况转换,从而导致浪费能源并使乘客舒适度下降。将对能耗和舒适度这两个参数进行目标优化;并在前人的研究基础上,提出以工况为基础的分段分析建模优化参数的ATO多目标优化策略。利用改进的遗传算法进行模型求解。最后整合各段优化得出的运行曲线,得到完整的运行曲线图。通过实例仿真,该方法具有很好的优化效果及可操作性。

The multi-objective optimization strategy of urban railway train automatic operation system based on working condition

Abstract:Traditional automatic train operation (ATO) system has a good ability of automatic driving.But to achieve driving purpose,the system will change working conditions frequently.So it make passengers feel uncomfortable and waste energy consumption.This paper will optimize parameter of driving comfortable and energy consumption and propose a optimization strategy of ATO which base on working condition and establish the model on each condition.The model is solved by genetic algorithm which has been developed.Finally,each speed curve are combined to finished ATO multi-objective optimization speed curve.Through the example simulation, the method has a good optimization effect and maneuverability.