基于粒子群算法的城轨列车节能驾驶优化模型

为了降低城市轨道交通中列车在站间运行的能耗, 研究了列车的站间节能驾驶策略, 在考虑线路限速和坡度的情况下, 建立了时间约束下的列车节能优化模型, 采用粒子群算法优化目标速度序列得出了列车节能驾驶策略。节能驾驶优化方法通过2个阶段来实现, 第1阶段在站间运行时间不变的情况下, 采用粒子群算法优化了列车在站间的节能驾驶策略, 得到了运行时间和能耗的关系, 第2阶段在多站间总运行时间不变的前提下, 将运行时间进行重新分配, 得到了列车在全线运行的节能驾驶策略。以北京地铁亦庄线实际线路数据和车辆参数为基础, 对优化方法进行仿真验证。
仿真结果表明: 经过第1阶段的优化, 列车在万源街-荣京东街的单站间运行能耗降低了6.15%, 经过第2阶段的优化, 列车在多站间总运行能耗降低了14.77%。可见, 优化模型可以有效降低列车的运行能耗, 为列车时刻表的编制提供依据。

Optimization model of energy-efficient driving for train in urban rail transit based on particle swarm algorithm

In order to reduce the interstation operation energy consumption of train in urban rail transit, the interstation energy-efficient driving strategy of train was studied.On the basis of considering speed limit and gradient, a energy-efficient optimization model with the constraint of trip time was established, the optimal energy-efficient driving strategy was proposed by using particle swarm optimization(PSO)to optimize the target speed sequence.The optimization method of energy-efficient driving was realized through two phases.In the first phase, under the condition of constant interstation trip time, the interstation energy-efficient driving strategy of train was optimized with PSO, and the relationship between trip time and energy consumption was obtained.In the second phase, under the condition of the constant total trip time of whole interstations, the trip time was redistributed, and the energy-efficient driving strategy of train for the whole line was obtained.
Based on the real track data and vehicle parameters of Yizhuang Lineof Beijing Subway, the optimization method was simulated and verified.Simulation result shows that after optimization, the interstation operation energy consumption of train reduces by 6.15%in the first phase in Wanyuan Street-Rongjingdong Street, and the total operation energy consumption of whole interstations reduces by 14.77% in the second phase.So the model can effectively reduce the operation energy consumption of train, and provides a basis for the generation of train timetable.