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Optimising energy consumption on straight roads using regression analysis
KTH, School of Electrical Engineering and Computer Science (EECS).
2019 (English)Independent thesis Advanced level (degree of Master (Two Years)), 20 credits / 30 HE creditsStudent thesis
Abstract [en]

Cloud computation together with robotics has opened up possibilitiesto process large amount of data (big data) generated by the greatnumber of robotic systems. Todays vehicles are equipped withhundreds of sensors generating a lot of data that needs to beprocessed. The data can further be analysed and used to obtainmodels predicting the dynamics of the vehicles. It is thereforepossible to optimise the vehicle performance by studying thepredictive behaviour and finding the best combination of the vehicleparameters. In this thesis, the energy efficiency of an electric racingvehicle is studied on straight road whereafter an optimal velocityprofile is to be found. By using a multiple linear regression togetherwith regularization methods on previously recorded data, apredictive model managed to be obtained with an accuracy of 79.1 %.Having used this model in optimisation process, a velocity profilewas obtained which is shown that can enhance the efficiency of thesystem by 4.08%.

Abstract [sv]

Molnprocessering tillsammans med robotteknik har öppnatmöjligheter för att bearbeta stora mängder data som genereras av detökande antalet robotsystem. Dagens fordon är utrustade medhundratals sensorer som genererar mycket data som behöverbearbetas. Sensordata kan vidare analyseras och användas för attmodellera fordonets dynamik. Det är därför möjligt att optimerafordonets prestanda genom att studera det prediktiva beteendet ochhitta den bästa kombinationen av fordonsparametrarna. I dennaavhandling studeras energieffektiviteten hos ett elbil på rak väg,varefter en optimal hastighetsprofil hittas. Genom att använda enmultipellinjär regression tillsammans med regleringsmetoder påtidigare insamlad data lyckades en prediktiv modell erhållas med ennoggrannhet av 79,1 %. Efter att ha använt denna modell ioptimeringsprocessen erhölls en hastighetsprofil som visas som kanförbättra systemets effektivitet med 4,08%.

Place, publisher, year, edition, pages
2019. , p. 36
Series
TRITA-EECS-EX ; 2018:402
National Category
Engineering and Technology
Identifiers
URN: urn:nbn:se:kth:diva-253259OAI: oai:DiVA.org:kth-253259DiVA, id: diva2:1324257
External cooperation
ÅF
Educational program
Master of Science - Systems, Control and Robotics
Examiners
Available from: 2019-06-13 Created: 2019-06-13 Last updated: 2019-06-13Bibliographically approved

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CiteExportLink to record
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Citation style
  • apa
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Output format
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