Energy-saving Optimization And SUMO Simulation Of Urban Rail Transit

With the continuous expansion of the scale of cities in our country,urban rail transit,represented by subway,has become one of the important ways to support urban public transportation,and is known as "urban bloodline".At the same time,the urban rail transit system bring to huge traction energy consumption every year.The energysaving operation of trains is an effective measure to reduce the energy consumption of train traction,which not only responds to the state’s call for "saving electricity and green development",but also improves the economic benefits of urban rail operating companies.Train energy-saving optimization operation is divided into two parts: train target speed curve optimization and train speed curve tracking.Aiming at the optimization problem of train target speed curve,a train dynamics model with position as the independent variable is established.Through the discretization of train position,the optimization problem is transformed into a multistage decision-making process,which is then solved using the dynamic programming reverse order solution method to obtain the train station optimal speed curve.The position is discretized based on the information of line speed limit and track slope,and make full use of line conditions to search for the optimal train station speed curve.Reduce the speed state of the train,improve the search efficiency of the dynamic programming algorithm,and reduce the solution time.Combined with the actual running situation of the train,the discretization rules of the train position are improved,and the interval of the discretization of the train traction phase and the brake phase is appropriately reduced to obtain a better speed curve between train stations.Aiming at the problem of train speed curve tracking,the general steps of model predictive control for solving optimization problems are given.Assuming that the train is subjected to random interference that satisfies a uniform distribution during the actual operation,the position-based train speed curve tracking and the time-based train speed curve tracking are respectively realized.Among them,the position-based train speed curve tracking does not consider the solution time of the optimization problem.Timebased speed curve tracking takes the second control coefficient of the optimal control sequence from the previous step as the control coefficient of the current stage of the train,taking into account the solution time of the optimization problem,and realizes the train speed curve tracking that can be used for actual control.Developed a multi-train operating platform based on SUMO.Download the map of Beijing Subway Yizhuang Line from Open Street Map as a road file,and use the actual train timetable as a demand file to imitate the actual operation of Yizhuang Line trains.Get the current status of the train and control the operation of the train through the Python interface to realize the online control of the train.