Pages 216-221
Year 2024
Issue 3
Volume 13


Author(s): Jie Li, Runkai Hua

Doi: 10.7508/aiem.03.2024.216.221

This is an open access article distributed under the Creative Commons Attribution License CC BY 4.0, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited


With the continuous increase in operational mileage and density of urban rail transit systems, the energy consumption during train operations has become significant. Consequently, research on energy-efficient operation optimization methods for multiple trains has emerged as a popular topic. To better utilize regenerative braking energy and reduce electrical energy consumption, this paper proposes an energyefficient operation method for trains based on multi-agent systems for collaborative control. Initially, utilizing the dynamics and traction power supply system models of subway trains, a multi-agent system is introduced to establish an optimized energy consumption calculation model for multiple trains through information exchange between agents. Then, based on the multi-agent system, a framework for the coordinated operation of trains is proposed, and an energy-efficient operation optimization algorithm for trains based on collaborative evolution of multiple agents is designed. Finally, simulations on actual subway lines verify the effectiveness of this method. Simulation results show that the multi-train energy consumption calculation model established by this method can accurately calculate the energy consumption of train operations, with an error margin of no more than 6% compared to real-world measurements. The proposed energy optimization algorithm can effectively enhance the utilization efficiency of regenerative braking energy and reduce the overall energy consumption of train operations compared to methods that optimize the driving strategies of each train independently.

Urban Rail Transit, Regenerative Braking Energy, Collaborative Control, Multi-Agent System