Research On Energy Saving Operation Of Urban Rail Train Based On Multi-Objective Optimization

In recent years,my country has attached great importance to the construction of urban rail transit system,and urban rail transit has become one of the main modes of urban travel.With the continuous construction and large-scale operation of urban rail transit,people are increasingly aware of its many advantages such as punctuality,speed,safety and comfort,and convenient travel,and the resulting huge energy loss has become a key problem that needs to be solved urgently.The energy consumption of urban rail transit is relatively complex,among which the energy consumption of the train traction process accounts for the largest proportion.Therefore,reducing the energy consumption of train traction has become the key to reducing the energy consumption of the urban rail train system.The operation process of urban rail trains is strictly in accordance with the set timetable.How to obtain a more efficient running speed curve to reduce the traction energy consumption of the train on the basis of meeting the train’s on-time running is the focus of research.Therefore,in this paper,the energy consumption of train operation is added to meet the train’s punctuality,and a multi-objective optimization model about train energy saving and punctuality is constructed,and the influence of trains under different control strategies is considered.Finally,on the basis of the speed curve obtained by the above optimization,the utilization of regenerative braking energy between multiple trains is studied.The specific research contents are as follows:(1)Firstly,the force of the train operation process is described in detail,and the dynamic models of the train under single-mass point and multi-mass points are established respectively,and the two modeling methods are compared.At the same time,the principle and optimization steps of the particle swarm optimization algorithm used in this paper are introduced,and the calculation of inertia weight and influence factor is improved.(2)In order to solve the multi-objective optimization model of subway trains and obtain the optimal operation scheme that satisfies the energy-saving and punctuality of trains,this paper adopts the traditional particle swarm optimization algorithm and the multi-objective particle swarm optimization algorithm based on self-adaptive grids respectively.The objective optimization problem is solved,and three common train driving strategies are set.A section of inter-station line is selected for simulation to compare the superiority of the two algorithms,and the optimal speed schemes of the trains under the three control strategies are compared respectively.The results show that the multi-objective particle swarm optimization algorithm based on adaptive grid has obvious advantages in solution diversity,stability and optimization effect.The energy-saving and punctual effect of the operating plan of the company is the best,and a simulation interface about the optimization of subway trains between fixed stations is built.(3)Based on the optimal operation scheme obtained under the above control strategy of "traction-coasting-traction-coasting-braking",by analyzing the principle of train regenerative braking energy generation and utilization,the front and rear trains in the same power supply area are obtained.The relationship between traction-braking overlap time and regenerative braking energy utilization.By optimizing the departure interval and stop time of trains,a maximization model of overlapping time is established,and optimized by particle swarm algorithm.Aiming at the scheme proposed above,the Beijing subway running line is selected for simulation verification.The simulation results show that the overlap time of the optimized trains is increased by 4.6s compared with the unoptimized ones,and the utilization rate of the regenerative braking energy is increased by 8.8%.