Research on Train Energy-Saving Optimization Based on Parallel Immune Particle Swarm Optimization

Aiming at the optimization problem of subway train energy conservation, this paper proposes a train energy-saving optimization method based on parallel immune particle swarm optimization. The parallel immune particle swarm optimization algorithm is used to optimize the train energy saving in two stages: firstly, the running time of each interval is fixed, the algorithm is used to search for the optimal working condition switching point, and then, the train running time is optimized under the premise of constant running time. Finally, using the real data of Beijing Yizhuang line to simulate, verify the feasibility of the model and algorithm.

• Record URL:
  • https://doi.org/10.1007/978-981-15-2862-0
• Availability:
  • Find a library where document is available. Order URL: http://worldcat.org/isbn/9789811528613
• Supplemental Notes:
  • © Springer Nature Singapore Pte Ltd. 2020.
• Corporate Authors:

  Springer Singapore

  152 Beach Road
  Singapore,     189721
• Authors:
  • Li, Shibo
  • Dai, Wang
  • Fang, Lichao
  • Zhang, Yong
  • Xing, Zongyi
• Conference:
  • 4th International Conference on Electrical and Information Technologies for Rail Transportation (EITRT 2019)
  • Location: Qingdao , China
  • Date: 2019-10-25 to 2019-10-27
• Publication Date: 2020-4

Language

• English

Media Info

• Media Type: Web
• Edition: 1
• Features: References;
• Pagination: pp 33-44
• Monograph Title: Proceedings of the 4th International Conference on Electrical and Information Technologies for Rail Transportation (EITRT) 2019: Novel Traction Drive Technologies of Rail Transportation
• Serial:
  • Lecture Notes in Electrical Engineering
  • Volume: 638
  • Publisher: Springer
  • ISSN: 1876-1100

Subject/Index Terms

• TRT Terms: Energy conservation; Optimization; Rapid transit cars; Subways
• Identifier Terms: Beijing Yizhuang Subway
• Subject Areas: Energy; Public Transportation; Railroads; Vehicles and Equipment;

Filing Info

• Accession Number: 01928287
• Record Type: Publication
• ISBN: 9789811528613
• Files: TRIS
• Created Date: Aug 23 2024 3:26PM