A predictive optimization method for energy-optimal speed profile generation for trains

Szilard Aradi, Tamas Becsi, Peter Gaspar

Research output: Chapter in Book/Report/Conference proceedingConference contribution

8 Citations (Scopus)

Abstract

Rising energy prices motivate all participants in transportation to pay attention to the possibilities of reducing the amount of energy used. The paper deals with the reduction of energy consumption of trains. The main goal is to generate a speed profile that is more energy efficient than a given reference journey taking the slopes of the track into consideration. The paper shows that by using a predictive optimization approach both journey time keeping and energy consumption reduction can be achieved. The proposed method has been tested by simulation based on a real case study of the SBB. The presented algorithm could be used in drivers' training or as a core algorithm for automatic train operation.

Original languageEnglish
Title of host publicationCINTI 2013 - 14th IEEE International Symposium on Computational Intelligence and Informatics, Proceedings
Pages135-139
Number of pages5
DOIs
Publication statusPublished - Dec 1 2013
Event14th IEEE International Symposium on Computational Intelligence and Informatics, CINTI 2013 - Budapest, Hungary
Duration: Nov 19 2013Nov 21 2013

Publication series

NameCINTI 2013 - 14th IEEE International Symposium on Computational Intelligence and Informatics, Proceedings

Other

Other14th IEEE International Symposium on Computational Intelligence and Informatics, CINTI 2013
CountryHungary
CityBudapest
Period11/19/1311/21/13

ASJC Scopus subject areas

  • Artificial Intelligence
  • Information Systems

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  • Cite this

    Aradi, S., Becsi, T., & Gaspar, P. (2013). A predictive optimization method for energy-optimal speed profile generation for trains. In CINTI 2013 - 14th IEEE International Symposium on Computational Intelligence and Informatics, Proceedings (pp. 135-139). [6705179] (CINTI 2013 - 14th IEEE International Symposium on Computational Intelligence and Informatics, Proceedings). https://doi.org/10.1109/CINTI.2013.6705179