Research on automatic driving strategy optimization method of high-speed train based on artificial fish swarm algorithm

Abstract

The existing automatic driving technology of high-speed railway trains has been able to complete the automatic driving of trains in normal scenes, and it has begun to develop in the direction of improving operational efficiency and optimizing driving strategies. Based on the current automatic driving technology, this paper proposes an optimization method of automatic driving strategy for high-speed trains based on an artificial fish swarm algorithm, aiming at reducing train energy consumption, improving train parking accuracy, train punctuality, passenger comfort, and train speed protection. Firstly, based on train dynamics, the dynamic parameter model of the train in the running process is described. Through the train dynamic parameter model, combined with line information such as speed limit, slope, and phase separation area, an artificial fish swarm algorithm is designed that aims at safety, punctuality, energy saving, comfort, and parking accuracy. The artificial fish swarm algorithm is used to simulate the optimal driving strategy of train running and generate the train driving curve. Finally, the driving curve simulated by the artificial fish swarm algorithm is compared with the driving curve simulated by the genetic algorithm and the actual driving curve of the Beijing-Shenyang high-speed train, which shows that the proposed method is effective and the convergence speed of the artificial fish swarm algorithm is better than that of the genetic algorithm.