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

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.