基于Q学习算法的高速铁路列车节能优化研究

A Study on Energy-saving Optimization for High-Speed Railways Train based on Q-learning Algorithm

随着我国高速铁路运营里程的增加,在带动经济增长的同时,也产生了巨大的能源消耗,为优化高速铁路列车节能,实现我国高速铁路可持续发展,阐述高速铁路列车节能现状,以高速列车站间运行全过程的牵引能耗和准时性为目标,综合考虑线路和列车等约束条件,建立列车节能驾驶控制模型。在此基础上,设计一种基于Q学习算法的列车运行能耗优化模型,并以京沈客运专线黑山北-阜新站间线路为例,对节能优化策略进行了仿真验证。结果表明,该算法能够在满足所有约束条件下,有效减少列车站间运行能耗。

With the increase of high-speed railways mileage in China,while driving economic growth,it also generates huge energy consumption.In order to optimize the energy-saving of high-speed railway and realize the sustainable development of high-speed railway in China,this paper expounds the current situation of high-speed railway train energy-saving,aiming at the traction energy consumption and punctuality in the whole process of high-speed railway station operation,and comprehensively considering the constraints such as lines and trains,and establishes the train energy-saving driving control model.On this basis,a Q-learning-based high-speed railway energy-saving driving strategy optimization algorithm is designed,and the energy-saving optimization strategy is simulated and verified by taking the line between Heishan North Railway Station and Fuxin Railway Station of BeijingShenyang passenger dedicated line as an example.The result shows that the algorithm can effectively reduce the energy consumption between railway stations under all constrained conditions.