动车组追踪运行多目标实时优化策略

Multi-objective Real-time Optimization Strategy for Electric Multiple Units Tracking

动车组高速高密度地追踪运行是高速铁路运营模式的发展趋势,但其运行过程复杂、运行环境多变,难以同时满足安全、正点、节能、舒适等多目标要求。依据动车组追踪运行及其环境条件特征,建立了动车组追踪运行特征描述模型、线路特征模型以及多目标操纵优化模型,并实时获取动车组运行限速、速度、位置等信息,采用改进的多目标粒子群优化算法,进行动车组追踪运行多目标实时优化。基于CRH380AL运行过程现场数据的仿真结果验证了该方法的有效性。

High-speed and high-density tracking of electric multiple units （EMU） is the developing tendency of high-speed railway. However, because of the complicated operation process and the changeable operating environment, it is difficult to meet the multi-objective operational requirements of security, punctuality, energy saving and ride comfort simultaneously. Consequently, based on the analysis of tracking features and running conditions of EMU, the descriptive model of EMU＇s tracking process and the characteristics model of running conditions, as well as the multi-objective optimization model are built. Then, the multi-objective real-time optimization of EMU＇s operation strategies are conducted by a modified multi-objective particle swarm optimization algorithm, while obtaining speed restriction, velocity and position of EMU in real time. The simulation results on the field data of CRH380AL （China Railway High-speed EMU type-380AL） running process verify the effectiveness of the proposed method.