{"title":"A predictive optimization method for energy-optimal speed profile generation for trains","authors":"S. Aradi, Tamás Bécsi, P. Gáspár","doi":"10.1109/CINTI.2013.6705179","DOIUrl":null,"url":null,"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.","PeriodicalId":439949,"journal":{"name":"2013 IEEE 14th International Symposium on Computational Intelligence and Informatics (CINTI)","volume":"8 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2013-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"19","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2013 IEEE 14th International Symposium on Computational Intelligence and Informatics (CINTI)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/CINTI.2013.6705179","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 19
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.
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