Towards Greener Rail Transit: Multi-Train Timetable and Speed Profile Optimization for Energy Conservation*

Yunjia Zhang, Dongliang Cui, Weiqiang Wang, Xubin Sun, Zhiming Yuan, Zhongbei Tian, Xuewu Dai

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

Abstract

Aiming at dynamic scheduling in multi-train timetable, a optimization model of multi-train timetable and speed profile optimization for energy conservation is proposed. The proposed approach integrates train operation trajectory optimization with the scheduling of arrival and departure times at stations. At each instance, the highest-priority train operation process is selected, and optimization is performed for the operation of only one train between stations. Various constraints are considered to ensure safe train operation and prevent conflicts with preceding trains. By collaboratively regulating and optimizing train arrival/ departure time, speed profile, traction/braking force, minimizing train traction energy consumption and minimizing power peak can be achieved. The objectives are combined into a single-objective nonlinear optimization problem through weighted summation, which is then solved using the differential evolution algorithm. The simulation verification conducted on the Yizhuang Line demonstrates that this method enables more efficient utilization of energy while also recovery from the delay. The train traction energy consumption was reduced by 11.35%. This work provides an important technical support and guidance for realizing the green, low-carbon and sustainable development of urban rail transit in the future.
Original languageEnglish
Title of host publicationICAC2024
Subtitle of host publicationThe 29th International Conference on Automation and Computing
Place of PublicationPiscataway, US
PublisherIEEE
Pages1-6
Number of pages6
ISBN (Electronic)9798350360882
ISBN (Print)9798350360899
DOIs
Publication statusPublished - 28 Aug 2024

Keywords

  • dynamic scheduling in multi-train timetable
  • energy conservation
  • train operation trajectory optimization
  • differential evolution algorithm

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