A novel energy-saving train operation strategy based on particle swarm algorithms

To effectively reduce the energy consumption of urban rail transit trains and optimize the operation strategy, based on the dynamic model of the urban rail transit train, the energy consumption model of four-stage manipulation strategy is constructed respectively, and the particle swarm optimization algorithm is used to solve the model. In this paper, an Atype train with four driving and two pallets are selected as the research object, and a case design is carried out on a flat ramp. The operation strategy is analyzed for the difference between vehicle traction energy consumption, regenerative braking energy storage, and total energy consumption. The corresponding speed-distance curve is obtained by simulation. The results show that the four-stage control strategy is generally helpful to achieve better energy-saving optimization effects.