Study on Energy-Saving Optimization of Tracking Operation High-Speed Trains

Abstract

With the increase of running speed of trains, energy consumption during the operation is getting worse. However, most of the studies of optimization of energy-saving of train operation focus more on reducing the energy consumption of single-trains rather than multi-trains, which can also cause energy consumption due to the interactional tracking. Therefore, aiming at this issue of the high-speed railway, a single-particle train operation simulation model was established. On the above basis, with regenerative braking energy taken into account, the optimization model for multi-train tracking was built for the sake of less consumption, punctuality of train operation and riding comfort of vehicles. Then, by using multi-objective particle swarm optimization algorithm based on dynamic neighborhood with dynamic operator, the operation of the following train interfered by the leading train was optimized. Finally, CRH3 EMU running between Beijing and Shanghai high-speed railway was used in the simulation, producing results compared with the practical running result that the energy consumption, reduced by roughly 15%, the running time, taking 12.2 s longer, and the degree of riding comfort of vehicles, within the range from 0.315 to 0.63, all met expectations. Therefore, the effectiveness of the optimized algorithm was proved.