摘要
针对列车运行节能问题,基于改进多目标粒子群优化算法,对列车节能进行研究。在考虑列车动力学特性、牵引能耗、运行时间的基础上,建立列车区间运行节能优化模型。引入准对立学习策略,对多目标粒子群优化算法进行改进,采用ZDT1测试函数验证改进算法的优越性。应用改进多目标粒子群优化算法,求解基于实际线路数据的列车区间运行节能优化模型,并建立满意度评价方法,得出不同运行策略对应的最优运行曲线。研究结果显示,TCHCB和TCTCB运行策略与传统THCB运行策略相比,增加了惰行工况,在正点到站的前提下使能耗分别降低7.25%和4.4%。基于改进多目标粒子群优化算法的列车节能研究表明,在列车运行中适当增加惰行工况,有助于节能。
Aiming at the problem of energy saving in train operation,the energy saving of train was studied based on the improved multi-objective particle swarm optimization algorithm.Based on the consideration of train dynamic characteristic,traction energy consumption and running time,an energy-saving optimization model of train interval operation was established.The quasi-opposites learning strategy was introduced to improve the multi-objective particle swarm optimization algorithm,and the ZDT1 test function was used to verify the superiority of the improved algorithm.The improved multi-objective particle swarm optimization algorithm was applied to solve the energy-saving optimization model of train interval operation based on actual line data,and a satisfaction evaluation method was established to obtain the optimal operation curves corresponding to different operation strategies.The research results show that compared with the traditional THCB operation strategy,the TCHCB and TCTCB operation strategies increase the idling condition,and reduce the energy consumption by 7.25%and 4.4%,respectively,under the premise of arriving at the station on time.The train energy-saving study based on the improved multi-objective particle swarm optimization algorithm shows that the appropriate increase of idling condition in train operation is helpful to save energy.
出处
《装备机械》
2023年第4期19-23,共5页
The Magazine on Equipment Machinery
基金
江苏省大学生创新创业训练计划项目(编号:11310912205)。
作者简介
第一作者:金瑷瑶(1999-),男,本科,学习专业为城市轨道交通;通信作者:李文强(1999-),男,硕士研究生,主要研究方向为交通信息工程与控制。
