Research On Energy-saving Operation Optimization Of Metro Trains Based On APSO

Urban rail transit has the advantages of large capacity,punctuality,safety,comfort,and environmental protection.It occupies an indispensable and important position in China’s urban public transportation system.As the operating mileage of rail transit continues to increase,passenger traffic is gradually increasing,and operating energy consumption continues to increase.Energy conservation and consumption reduction have become one of the issues that must be resolved for the sustainable development of rail transit.However,train traction energy consumption accounts for the largest proportion of the total energy consumption of rail transit system operation.How to reduce train traction energy consumption is of great practical significance to maximize the energy efficiency of rail transit systems.This paper aims to save energy and reduce consumption.Based on the communicationbased train operation control system,this paper analyzes the impact of train operation energy consumption,operation time,and distance between stations,and establishes a model of train energy consumption,so as to explore the energy efficiency optimization and Energy-saving optimization of rear-vehicle speed curve during multi-train tracking operation.The contents are as follows:1.Analyze the running characteristics of the train and establish a train dynamics calculation model;analyze the running characteristics of the train tracking,establish a distance model of the train tracking interval according to the different tracking scenarios;analyze the energy-saving operating conditions of the distance between the same stations,and study different working conditions Conversion sequence.2.The particle swarm algorithm based on adaptive inertia weight is used to optimize the speed curve of a single train.According to the actual running state of the train,explore the influence of running time and distance between stations on running energy consumption,and establish an energy consumption optimization model.Aiming at the fact that the particle swarm optimization algorithm is easy to fall into a local optimum,a particle swarm optimization algorithm based on inertia weight adaptation is proposed.By comparing the energy consumption of trains under different energy-saving operating conditions,the energy-saving optimized speed curve of trains between multiple stations is solved.3.The dynamic particle swarm algorithm is used to optimize the rear vehicle speed curve during multi-train tracking operation.Analyze the impact of the front vehicle’s running status on the rear vehicle when the front and rear two trains are running under the condition of mobile occlusion,and establish a calculation model for the distance between the two trains.Combined with the characteristics of dynamic particle swarm optimization that can effectively optimize the dynamic environment,The position and speed information of the car is taken into consideration in the optimization of the running curve of the rear car.4.The real data of a subway in Beijing are selected,and the above algorithms are simulated and verified in combination with train and line parameters.The simulation results show that the accuracy of the reconstructed energy consumption operation optimization model is improved by 1.16% compared with the traditional model.The improved particle swarm algorithm is used to solve the operating condition transition point to achieve the energy saving optimization of the train operation curve.The energy saving effect of the entire line can reach 6.92%.When the particle swarm algorithm is used to optimize the running curve of the tracking train,it can reduce the influence of the preceding vehicle operation on the energy consumption of the following vehicle operation,and the energy saving effect can reach 4.89%.