Research On Energy-saving Optimization Algorithm For High-speed Train Tracking

By the end of 2019,the total operating mileage of railways has reached more than 139,000 kilometers in China,of which nearly 35,000 kilometers are high-speed railways.With the rapid development of Chinese high-speed railway technology and the increasing demand for transportation across the country.The development trend of high-speed railway gradually shows that the operating lines are more networked and the driving conditions have become more complicated.The sustainable development of high-speed railways is facing internal challenges of increasing traffic demand.The high-speed railway actively takes advantage of its safety,efficiency,and punctuality.At the same time,it also brings a serious problem,namely the increase in railway energy consumption.Therefore,in the context of the great development of China's railways,reducing energy consumption is still a problem to be solved in railway construction.In this paper,by combining the running process of high-speed trains and the research on the current status and problems of energy-saving trains at home and abroad,a single train energy-saving optimization model for high-speed trains and a tracking train energy-saving optimization model are established.By designing optimization algorithms separately,the energy saving optimization of the speed running curve of single trains and tracking trains of high-speed trains is carried out.The main work of the paper is as follows:(1)Analysis of high-speed train operation process and energy-saving operation model.This part not only analyzes the train operation process,but also introduces and analyzes the operating condition conversion principles and high-speed train control strategies during the high-speed train operation.At the same time,the principle of the high-speed train moving block system and the tracking operation process is analyzed too.In addition,a high-speed train tracking interval operation model based on the mobile block system is established,and a calculation model of high-speed train operation energy consumption is given.(2)Optimization of energy-saving operation of high-speed trains.In this part,the discretization process of the train operation line is first performed;then the multi-objective optimization function based on the train operation energy consumption and train operation punctuality is established,and the constraint conditions of the multi-objective optimization function are given.Next,the differential evolution algorithm is described,combined with the heuristic-based multi-objective particle swarm optimization,a multi-objective particle swarm optimization based on differential evolution is proposed to optimize the running speed curve of high-speed trains.Finally,through the selection of trains and the selection of the running line between the stations,the simulation of the proposed algorithm is analyzed.(3)High-speed train tracking energy-saving operation optimization.This part first discretizes the track of the running train;then,considering the influence of the preceding train on the following train during operation,and a constrained multi-objective optimization function is established to track the train's energy consumption and punctuality.At the same time,since the effect of the preceding train on the trailing train is dynamically changing,a dynamic multi-objective particle swarm optimization algorithm is proposed to optimize the running curve of the tracking train.Finally,the proposed algorithm is simulated and analyzed.The research results show that the multi-objective particle swarm optimization algorithm based on differential evolution proposed in this paper has a significant effect on single-row energy-saving operation.At the same time,the proposed dynamic multi-objective particle swarm algorithm has a significant effect on tracking the energy-saving operation of trains.