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Minimising Fuel Consumption of a Series Hybrid Electric Railway Vehicle Using Model Predictive Control
Linköping University, Department of Electrical Engineering, Automatic Control.
2017 (English)Independent thesis Advanced level (degree of Master (Two Years)), 20 credits / 30 HE creditsStudent thesis
Abstract [en]

With the increasing demands on making railway systems more environmentally friendly, diesel railcars have been replaced by hybrid electric railway vehicles. A hybrid system holds a number of advantages as it has the possibility of recuperating energy and allows the internal combustion engine (ICE) to be run at optimal efficiency. However, to fully utilise the advantages of a hybrid system the hybrid electric vehicle (HEV) is highly dependent on the used energy management strategy (EMS).

In this thesis, the possibility of minimising the fuel consumption of the series hybrid electric railway vehicle, Ki-Ha E200, has been studied. This has been done by replacing the currently used EMS, based on heuristics, with a model predictive controller (MPC). The heuristic EMS and the MPC have been evaluated by comparing the performance results from three different test cases. The performance of the implemented MPC seems promising as it yields more optimal operation of the ICE and improved control of the battery state of charge (SOC).

Place, publisher, year, edition, pages
2017. , 48 p.
Keyword [en]
Railway, Vehicle, Model, Predictive, Control
National Category
Control Engineering
Identifiers
URN: urn:nbn:se:liu:diva-142526ISRN: LiTH-ISY-EX--17/5095--SEOAI: oai:DiVA.org:liu-142526DiVA: diva2:1153615
Subject / course
Automatic Control
Presentation
2017-09-28, Algoritmen, Linköpings Universitet 581 83, Linköping, 15:15 (English)
Supervisors
Examiners
Available from: 2017-10-31 Created: 2017-10-31 Last updated: 2017-10-31Bibliographically approved

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CiteExportLink to record
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Citation style
  • apa
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Other style
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  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
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More languages
Output format
  • html
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  • asciidoc
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