基于改进DE的城轨列车节能速度曲线研究

采用改进交叉策略的差分进化（DE，Differential Evolution）算法，研究城轨列车节能优化的速度曲线生成。该算法以起点到目标停车点的距离和工况状态值作为个体基因，基于改进交叉策略，结合工况转换原则，对变异后的个体基因进行有效化处理。建立末速度、停车位置误差、运行时间和牵引能耗的评价模型，在满足安全运行和舒适度要求下生成最优列车自动运行（ATO，Automatic Train Operation）速度曲线。经数据仿真测试，牵引能耗为28.8 kw·h。生成的速度曲线，在准时到达的前提下，具有较好的节能效果，对城市轨道交通的列车节能优化运行研究具有一定的参考价值。

Research on energy saving speed curve of urban rail train based on improved differential evolution algorithm

This paper used Differential Evolution (DE) algorithm with improved crossover strategy to study the generation of speed curve for energy-saving optimization of urban rail transit trains. The algorithm took the distance from the starting point to the target parking point and the condition value as individual genes. Based on the improved crossover strategy, combined with the principle of working condition conversion, the mutated individual genes were effectively processed. The paper established evaluation model of terminal speed, parking position error, running time and traction energy consumption, and generated the optimal Automatic Train Operation (ATO) speed curve under the requirements of safe operation and comfort. Through the data simulation test, the traction energy consumption is 28.8 kw·h. Under the premise of arriving on time, the generated speed curve has good energy-saving effect, which has certain reference value for the research on energy-saving optimization operation of urban rail transit.