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Fuel-Saving Potentials of Platooning Evaluated through Sparse Heavy-Duty Vehicle Position Data
KTH, School of Electrical Engineering (EES), Automatic Control. KTH, School of Electrical Engineering (EES), Centres, ACCESS Linnaeus Centre. Scania CV AB, Sweden.ORCID iD: 0000-0001-5107-2942
KTH, School of Electrical Engineering (EES), Automatic Control. KTH, School of Electrical Engineering (EES), Centres, ACCESS Linnaeus Centre.ORCID iD: 0000-0002-3672-5316
KTH, School of Electrical Engineering (EES), Automatic Control. KTH, School of Electrical Engineering (EES), Centres, ACCESS Linnaeus Centre.ORCID iD: 0000-0001-9940-5929
2014 (English)Conference paper, Published paper (Refereed)
Abstract [en]

Vehicle platooning is important for heavy-duty vehicle manufacturers, due to the reduced aerodynamic drag for the follower vehicles, which gives an overall lower fuel consumption. Heavy-duty vehicle drivers are aware this fact and sometimes drive close to other heavy-duty vehicles. However, it is not currently well known how many vehicles are actually driving in such spontaneous platoons today. This paper studies the platooning rate of 1,800 heavy-duty vehicles by analyzing sparse vehicle position data from a region in Europe during one day. Map-matching and path-inference algorithms are used to determine which paths the vehicles took. The spontaneous platooning rate is found to be 1.2 %, which corresponds to a total fuel saving of 0.07% compared to if none of the vehicles were platooning. Furthermore, we introduce several virtual coordination schemes. We show that coordinations can increase the platooning rate and fuel saving with a factor of ten with minor adjustments from the current travel schedule. The platooning rate and fuel savings can be significantly greater if higher flexibility is allowed.

Place, publisher, year, edition, pages
IEEE conference proceedings, 2014. 1061-1068 p.
Keyword [en]
Fuel economy, Image matching, Inference engines, Intelligent vehicle highway systems, Vehicles
National Category
Computer Science
Identifiers
URN: urn:nbn:se:kth:diva-159049DOI: 10.1109/IVS.2014.6856540ISI: 000346153100169Scopus ID: 2-s2.0-84905394246ISBN: 978-147993638-0 (print)OAI: oai:DiVA.org:kth-159049DiVA: diva2:782989
Conference
IEEE Intelligent Vehicles Symposium (IV), JUN 08-11, 2014, Dearborn, MI, USA
Note

QC 20150123

Available from: 2015-01-23 Created: 2015-01-20 Last updated: 2016-02-26Bibliographically approved

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fulltext(1522 kB)73 downloads
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Liang, Kuo-YunMårtensson, JonasJohansson, Karl H.
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