The Research Of Train Energy-efficient Operation Strategy Based On Multi-objective Optimization

For the purpose of reducing energy consumption of the train running between the stations,ensuring punctuality and comfortability,this paper studies the train energy-efficient operation strategy.The main work of this paper is:(1)In this paper,a single point train operation model is established after the force analysis of train operation process.Then,aiming at the characteristics of Pareto optimization,the measurement index model of each objective is improved and analyzed based on the existing research;After analysis of the measurement index model,a multi-objective optimization model is proposed to ensure the energy-efficient of the train based on the objective of energy-efficiency,punctuality and comfortability.(2)The Multi-Objective Genetic Algorithm(MOGA)is improved in order to make the application of this algorithm in train energy-efficient operation strategy more suitable.Firstly,the idea of using Multi-Population Genetic Algorithm(MPGA)to generate initial population is proposed in this paper according to the defects of Genetic algorithm(GA),which may have immature convergence.Secondly,a slope equivalent strategy is adopted for the purpose of calculating the train slope more accurately.Thirdly,as for the discretization of variables in this paper,an unequal discretization method is applied which considering the line characteristics.More importantly,for the selection of individuals in this algorithm,a fast non-dominate sorting is used to make the individuals into different layers according to the fitness value;then calculate the crowding distance between individuals in the same layer in order to guarantee the Pareto solution sets can be distributed on the global optimal front with high quality;Finally,elite strategies are used to generate new populations.After this series of operations,the global convergence performance of the algorithm is improved.(3)The simulation system of train energy-efficient operation strategy is established through MATLAB programming.Based on the line data and vehicle parameters of Beijing Yizhuang Metro line,the performance of the improved MOGA is simulated and verified from the aspect of energy-efficiency and using multi-objective optimization to solve the problem of train delay.In the case study,the influence on solving the train energy-efficient operation problem is analyzed from the aspect of discretization and algorithm evolutionary.Besides,the simulation result of weight coefficient method and the improved MOGA are compared with the data of Yizhuang Metro line respectively.Different from previous study,it is the first time to use multi-objective optimization to find the Pareto front of the possible speed profile and update the speed profile when train delays arrived;Scenario analysis and case verification are carried out for using multi-objective optimization to recovery the time delay.Finally,the effectiveness of the simulation system is verified by the simulation of whole line with the improved MOGA.Simulation results show that the energy-efficient rate of the algorithm is stable at about 8%.A train operation control strategy is given and the MOGA is improved in this thesis.Besides a simulation system for the energy-efficient operation strategy is established,and the simulation verification was carried out.