Optimization Control of Energy-Efficient Driving for Trains in Urban Rail Transit Based on GA-PSO Algorithm

Abstract

In recent years, as the development of urban rail transit has effectively slowed down the congestion of urban traffic, more and more cities have focused on promoting the development of urban rail transit. But those trains of urban rail transit have also generated more and more energy consumption. In order to alleviate the energy shortage and promote energy conservation and emission reduction, it is of great significance to reduce the energy consumption of subway trains. The optimization control of energy-efficient driving is to reduce the energy consumption of trains when operating and improve the operation efficiency. The traditional particle swarm optimization algorithm is easy to fall into the local optimum and the global performance is not so good. In this paper, the genetic particle swarm optimization algorithm (GA-PSO) is used, combining the advantages of genetic algorithm and particle swarm algorithm, to optimize the energy consumption of trains. And MATLAB is used to carry out modeling and simulation analysis. Compared with the traditional particle swarm and genetic algorithm, the optimized algorithm is verified that it can effectively reduce the energy consumption of trains. The results show that the energy cost is reduced by 18.8%, which verifies the effectiveness of the developed algorithm.