基于启发式遗传算法的列车节能运行目标速度曲线优化算法研究

Algorithm of Target Speed Profile Optimization of Energy-efficient Train Operation Based on Improved Heuristic Genetic Algorithm

针对传统速度曲线优化算法在极限线路条件下鲁棒性差、易陷入局部最优的缺点,提出一种基于启发式遗传算法的速度曲线优化算法。基于列车运行基本模型和相关约束条件,按经典的四阶段法规划速度曲线轮廓,选取巡航速度和惰行点位置作为优化变量,采用启发式遗传算法进行寻优,中途如因限速变化等情况与最短时间运行曲线交汇则强制沿最短时间运行曲线运行。仿真实验结果显示,该算法具有收敛速度快、优化精度高、鲁棒性好的优点。该算法有效克服了进化算法搜索结果不确定性和速度波动性的固有缺点,对该领域以及其他交通工具的节能运行和自动驾驶,具有较好的参考意义和实用价值。

In response to the shortcomings of traditional traction optimization algorithms such as poor robustness and vulnerability to fall into local optimum under limit line conditions,a new energy-saving train operation speed profile optimization algorithm based on heuristic genetic algorithm was proposed.Based on the basic model of train operation and related constraints,as well as the classical four-stage method,the outline of speed curve was planned.The cruising speed and the position of hold-coast switching point were selected as optimization variables,and were optimized by heuristic GA.The train operation was forced to run along the shortest running curve if it intersected with the shortest running curve due to the change of speed limit.The simulation results show that the algorithm has the advantages of fast convergence,high optimization accuracy and good robustness.The algorithm effectively overcomes the inherent shortcomings of the uncertainty of search results and speed fluctuation of evolutionary algorithm,has and provides good reference significance and practical value for energy-saving operation and automatic driving in this field and for other vehicles.