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Fuel-optimal look-ahead adaptive cruise control for heavy-duty vehicles
KTH, School of Electrical Engineering and Computer Science (EECS), Automatic Control. KTH, School of Electrical Engineering and Computer Science (EECS), Centres, ACCESS Linnaeus Centre.ORCID iD: 0000-0002-7177-0702
KTH, School of Electrical Engineering and Computer Science (EECS), Automatic Control. KTH, School of Electrical Engineering and Computer Science (EECS), Centres, ACCESS Linnaeus Centre. KTH, School of Industrial Engineering and Management (ITM), Centres, Integrated Transport Research Lab, ITRL.
KTH, School of Electrical Engineering and Computer Science (EECS), Automatic Control.ORCID iD: 0000-0001-9940-5929
2018 (English)In: 2018 Annual American Control Conference (ACC), Institute of Electrical and Electronics Engineers (IEEE), 2018, p. 1841-1848, article id 8431494Conference paper, Published paper (Refereed)
Abstract [en]

In this paper, we investigate the problem of how to optimally control a heavy-duty vehicle following another one, commonly referred as ad-hoc or non-cooperative platooning. The problem is formulated as an optimal control problem that exploits road topography information and the knowledge of the preceding vehicle speed trajectory to compute the optimal engine torque and gear request for the vehicle under control. The optimal control problem is implemented by dynamic programming and is tested in a simulation study that compares the performance of multiple longitudinal control strategies. The proposed look-ahead adaptive cruise controller is able to achieve fuel saving up to 7% with respect to the use of a reference vehicle-following controller, by combining the benefits of adjusting the inter-vehicular distance according to the future slope with those of alternating phases of throttling and freewheeling (driving in neutral gear).

Place, publisher, year, edition, pages
Institute of Electrical and Electronics Engineers (IEEE), 2018. p. 1841-1848, article id 8431494
Series
Proceedings of the American Control Conference, ISSN 2378-5861
National Category
Control Engineering
Identifiers
URN: urn:nbn:se:kth:diva-234864DOI: 10.23919/ACC.2018.8431494Scopus ID: 2-s2.0-85052593065ISBN: 9781538654286 (print)OAI: oai:DiVA.org:kth-234864DiVA, id: diva2:1247466
Conference
82018 Annual American Control Conference, ACC 2018, Wisconsin Center / Hilton Milwauke City CenterMilwauke, United States, 27 June 2018 through 29 June 2018
Note

QC 20180912

Available from: 2018-09-12 Created: 2018-09-12 Last updated: 2018-09-12Bibliographically approved

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Valerio, TurriJohansson, Karl H.

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Valerio, TurriMårtensson, JonasJohansson, Karl H.
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