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Look-ahead speed planning for heavy-duty vehicle platoons using traffic information
KTH, School of Architecture and the Built Environment (ABE), Transport Science, Transport Planning, Economics and Engineering.ORCID iD: 0000-0003-2654-8173
KTH, School of Architecture and the Built Environment (ABE), Transport Science, Transport Planning, Economics and Engineering.ORCID iD: 0000-0002-1375-9054
KTH, School of Architecture and the Built Environment (ABE), Transport Science, Transport Planning, Economics and Engineering.
Scania AB.
2017 (English)In: Transportation Research Procedia, ISSN 2324-9935, E-ISSN 2352-1465, Vol. 22, 561-569 p.Article in journal (Refereed) Published
Abstract [en]

Freight transport is a fast increasing transportation mode due to the economic growth in the world. Heavy-duty vehicles (HDV) have considerably greater fuel consumption, thus making them a suitable target when new policies in road transport emphasize increased energy efficiency and mitigated emission impacts. Intelligent transportation systems, based on emerging V2X communication technology, open new possibilities for developing fuel-efficient driving support functions considering real traffic information. This indicates a large potential of fuel saving and emission reduction for freight transport. This paper studies a dynamic programming-based optimal speed planning considering a maximum acceleration model for HDVs. The optimal speed control is applied for the deceleration case of HDV platoons due to received information on traffic speed reduction ahead. The control can optimize fuel consumption as well as travel time, and theoretical results for the two cases are presented. For maximal fuel saving, a microscopic traffic simulation study is performed for single HDVs and HDV platoons running in real traffic conditions. The results show a decrease in fuel consumption of more than 80% compared to simulations without applying optimal control, while the fuel consumption of other vehicles in the simulation is not significantly affected.

Place, publisher, year, edition, pages
Elsevier, 2017. Vol. 22, 561-569 p.
National Category
Transport Systems and Logistics Control Engineering
Research subject
Transport Science; Computer Science
Identifiers
URN: urn:nbn:se:kth:diva-210173DOI: 10.1016/j.trpro.2017.03.045Scopus ID: 2-s2.0-85019435576OAI: oai:DiVA.org:kth-210173DiVA: diva2:1117167
Note

QC 20170628

Available from: 2017-06-28 Created: 2017-06-28 Last updated: 2017-11-29Bibliographically approved

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