动车组节能运行速度优化设定

Speed Optimal-setting of Electric Multiple Units Based on Energy-efficient Operation

摘要: 动车组运行环境复杂、运行工况变化频繁，基于给定速度—位移曲线的运行操控模式难以实现节能优化运行．对动车组牵引能耗和运行时分模型进行分析，采用数据驱动建模方法建立动车组运行能耗、运行时间与惰行点的RBF（radial basis function）神经网络模型；并采用遗传算法寻优惰行点．以站间运行的能耗和时间的权衡为目标函数，最终确定动车组节能、正点运行的速度优化设定曲线．基于CRH380AL型动车组济南—泰安站间实际运行数据的仿真结果验证了本文方法的有效性．

Abstract: The operation process of electric multiple units (EMUs) is characterized by the complex running environment and the frequent changes of running conditions. Based on the traction energy consumption and running time mechanism models,the data-driven RBF (radial basis function) neural network is exploited to build models of energy consumption and running time with coasting points. GA (genetic algorithm) is then used to optimize the RBF neural network model; it obtains the speed optimal-setting curve satisfying the energy-efficient running conditions. Finally,simulation results on CRH380AL running data show the effectiveness of the proposed method.