Real-Time Energy-Saving Optimization for Multiple Trains Based on Multiagent Cooperative Control

Abstract

Nowadays, the energy-saving optimization problem has gradually become a hot spot in the subject of high-speed train control. Traditional static train trajectory planning is designed offline according to a preplanned timetable, but ignored are the uncertainties of parameters because of line conditions and other factors in the process of operation. Thus, the previous operation strategy may not suit the remaining section. The cooperative control strategy for multiple trains was investigated with the aim of conserving energy between successive stations under the constraints of safety and punctuality. First, combined with the train operation strategy under the quasi-moving block system of the Chinese Train Control System, a train cooperative interaction mechanism based on multiagent input is proposed to obtain the real-time running characteristics of trains on the railway via the Radio Block Center (RBC) agent. In the case of unanticipated events of railway line conditions, train operation is often out of accord with the timetable. Therefore, a resilience set related to trip-time deviation and headway variation is introduced to evaluate the robustness of the railway system and achieve balance in the train group. Also, a multigroup parallel differential evolution algorithm is developed to solve the online optimization problem of a high-speed train group according to real-time information. The proposed approach is analyzed by using operational data from the Wuhan–Changsha highspeed railway line in China to assess energy savings and punctuality. Results of the case study show superior performance and effectiveness of the model, and the methodology is illustrated.