相对速度模式下的城市轨道交通列车牵引能耗预测

Prediction of Traction Energy Consumption for Urban Rail Transit Trains in Relative Speed Mode

[目的]通过支持向量机回归方法与遗传算法,精确预测相对速度模式下的城市轨道交通列车牵引能耗,以提升列车在运行过程中的能源利用效率。[方法]首先分析了城市轨道交通列车牵引运行动力学特性,获得了反映列车实时运行状态的动力学物理指标;进而针对列车间的相对速度和位置变化,对列车的相对位置与相对速度追踪进行建模,建立了基于相对速度模式的列车运行模型。在此基础上,提取了对列车牵引能耗有直接影响的列车运行指标,并基于支持向量机回归方法与遗传算法对这些指标进行分析,实现了列车牵引能耗的精准预测。[结果及结论]试验结果验证了所设计方法能够有效预测列车牵引能耗。该方法的预测值精度达到92.0%~99.6%,最大相对误差为2.36%,平均相对误差为1.75%,均方根相对误差为1.52%,各项指标数值均优于其他预测方法。该设计方法的预测结果在整个预测范围内的波动较小,呈现了良好的整体预测稳定性及实际应用性能。

[Objective]It is aimed to accurately predict the traction energy consumption of urban rail transit trains operating in relative speed mode using support vector machine(SVM)regression and genetic algorithms,ultimately enhancing energy efficiency during train operation.[Method]First,the dynamics characteristics of urban rail transit train traction operation are analyzed to obtain dynamics physical indicators that reflect the real-time operational state of the trains.It then models the relative speed and position changes between trains to establish a train operation model based on relative speed mode.On this basis,key train operational indicators that directly influence train traction energy consumption are extracted,and SVM regression combined with genetic algorithm is employed to analyze these indicators,enabling precise train traction energy consumption predictions.[Result&Conclusion]The experimental results demonstrate that the proposed method effectively predicts train traction energy consumption.The prediction accuracy ranges from 92.0%to 99.6%,with a maximum relative error of 2.36%,an average relative error of 1.75%,and a root mean square relative error of 1.52%,outperforming other prediction methods by every indicator value.The prediction results of the design method show minimal fluctuation throughout the entire prediction range,indicating excellent overall prediction stability and strong practical applicability.