ATO Recommended Speed Curve Optimization based on Artificial Bee Colony Algorithm

Abstract

Aiming at the problem of high train traction energy consumption in urban rail transit, this paper combined the optimization of train recommended speed curve and ATO driving strategy, and proposed an optimization algorithm of train energy-saving driving strategy based on artificial bee swarm algorithm. Firstly, an optimization model of train recommended speed curve was established. Secondly, an optimization calculation method of train recommended speed curve based on artificial bee swarm algorithm was proposed based on driver's driving experience. Then, an energy-saving cruising driving strategy was designed to improve ATO's original driving strategy. Finally, the algorithm was verified by simulation with actual data. The simulation results show that when the algorithm is used to control the driving, the energy consumption can be reduced by 6.9% and the calculation time is 18.25s under the condition of meeting the constraints of interval running time. The algorithm has fast convergence speed, small calculation time and obvious energy-saving effect, which has certain practical significance for reducing the train traction energy consumption.