Research On Energy-saving Optimal Control Of EMU Trains Based On Genetic Algorithm

High-speed railway is a sustainable green channel,which has the advantages of safety,punctuality,comfort,low energy consumption and high efficiency.However,its huge energy consumption has also received more and more attention,and especially the research on energy-saving operation strategy of EMU(Electric Multiple Units)trains is imminent.At the same time,further exploring the multi-objective optimization control method for EMU trains not only provides a single control scheme,but also has far-reaching significance for reducing train operation energy consumption and saving resources.Based on the study of energy-saving operation of EMU trains at home and abroad,this dissertation extends from minimizing the energy consumption to multi-objective optimization of the train.Combined with the operation characteristics of EMU trains,the energy-saving optimization model and multi-objective optimization model of trains are established respectively,and the solving algorithms aiming at the characteristics of the models are designed.The main contents of this dissertation are as follows:(1)Based on the analysis of the forces on the train operation process,the multi-particle model is used to derive the motion equation of the train.The impact of electrical phases on the train operation process is analyzed,and the operating condition when the train passes through electrical phases areas is set.The factors that affect the energy consumption of the train operation are summarized,and the train energy-saving model in the first optimization is established;then,taking the link of the train passing through electrical phases into account,the train energy-saving model in the second optimization is constructed.Based on the basic theory of multi-objective optimization,a multi-objective optimization model for the train operation is built.(2)The energy-saving optimization of the train is carried out based on the golden ratio genetic algorithm.The basic theory of genetic algorithm is introduced,and its shortcomings in the optimization of specific problems are pointed out.Then,the golden ratio technology with strong local optimization advantages is introduced to form the golden ratio genetic algorithm,and the core steps of the gold proportion genetic algorithm are described in detail.Combined with the characteristics of the first and second energy-saving optimization models,the detailed steps to solve the models using the golden ratio genetic algorithm are given.An example is simulated and analyzed,and the final operation curve of the train is obtained by combining the two energy-saving optimization.The simulation results show that the energy-saving effect is obvious when the models are combined with the proposed algorithm.(3)The multi-objective optimization of the train is carried out based on the golden ratio NSGA-II.The energy-saving optimization of the train is only to optimize its energy consumption,and the final optimization scheme is only one of many feasible schemes,so the multi-objective optimization of the train is studied.The basic NSGA-II algorithm is introduced.Aiming at the problem of uneven distribution of individuals in the solution space during multi-objective optimization,the golden ratio technology is also introduced,and the golden ratio NSGA-II algorithm of multi-objective problems is elaborated in detail.Then,the flow of solving the multi-objective optimization model using the golden ratio NSGA-II algorithm is given,during which the process of the train passing through electrical phases is considered.The performance of the golden ratio NSGA-II algorithm is tested.The simulation results of the actual line verify that the golden ratio NSGA-II algorithm has certain effects on the optimization of the train energy consumption and punctuality,and has a certain reference effect on the actual train operation.