Urban rail system modeling and simulation based on dynamic train density

Yu, X., Wang, X. and Qin, Y. (2024) Urban rail system modeling and simulation based on dynamic train density. Electronics, 13(5), 853. (doi: 10.3390/electronics13050853)

Abstract

To further improve the simulation calculation ability of urban rail traction systems during the peak operation period and provide an accurate and reliable simulation tool for the subsequent train schedule and energy storage system design, a multi-train circuit model with a bilateral power supply was established in this paper, and a power calculation algorithm based on dynamic train density was designed. The circuit topology in the model can be dynamically adjusted according to the number of trains to improve the operation rate. Based on the spatial and electrical data of a real section of the subway, the urban rail circuit model was built on the MATLAB platform, and the actual operation data of the subway was imported for verification. The experimental results show that the multi-train model can accurately reflect the influence of voltage fluctuations on the traction system under different train running conditions, and the results fit the actual operation conditions. By comparing the influence of different train intervals on the RBE (regenerative braking energy) utilization, the results show that the optimal RBE utilization rate can be achieved by adjusting the train interval in the peak period.

• Item Type: Articles
• Additional Information: Financial support was provided in part by the National Natural Science Foundation of China (grant numbers 62373142, 62033014, and 61903136) and the Natural Science Foundation of Hunan Province (grant numbers 2021JJ50006 and 2022JJ50074), Hunan Engineering Research Center of Electric Drive and Regenerative Energy Storage and Utilization.
• Keywords: Urban rail, peak periods, regenerative braking energy, multi‐train modeling, dynamic train density.
• Status: Published
• Refereed: Yes
• Glasgow Author(s) Enlighten ID: Qin, Yuxin
• Authors: Yu, X., Wang, X., and Qin, Y.
• College/School: College of Science and Engineering > School of Computing Science
• Journal Name: Electronics
• Publisher: MDPI
• ISSN: 2079-9292
• ISSN (Online): 2079-9292
• Published Online: 23 February 2024
• Copyright Holders: Copyright © 2024 by the authors
• First Published: First published in Electronics 13(5):853
• Publisher Policy: Reproduced under a Creative Commons licence