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Optimal Speed Control of a Heavy-Duty Vehicle in the Presence of Traffic Lights
KTH, School of Electrical Engineering and Computer Science (EECS), Automatic Control. KTH, School of Industrial Engineering and Management (ITM), Centres, Integrated Transport Research Lab, ITRL.
Scania CV AB, S-15187 Sodertalje, Sweden..
KTH, School of Electrical Engineering and Computer Science (EECS), Automatic Control. KTH, School of Industrial Engineering and Management (ITM), Centres, Integrated Transport Research Lab, ITRL.ORCID iD: 0000-0002-3672-5316
2018 (English)In: 2018 IEEE Conference on Decision and Control  (CDC), Institute of Electrical and Electronics Engineers (IEEE), 2018, p. 6119-6124, article id 8619463Conference paper, Published paper (Refereed)
Abstract [en]

The fuel consumption of heavy-duty vehicles in urban driving is strongly dependent on the acceleration and braking of the vehicles. In intersections with traffic lights, large amount of fuel can be saved by adapting the velocity to the phases of the lights. In this paper, a heavy-duty vehicle obtains information about the future signals of traffic lights within a specific horizon. In order to minimize the fuel consumption, the driving scenario is formulated as an optimal control problem. The optimal control is found by applying a model predictive controller, solving at each iteration a quadratic program. In such problem formulation, the constraints imposed by the traffic lights are formulated using a linear approximation of time. Since the fuel-optimal velocity can deviate strongly from how vehicles normally drive, constraints on the allowed velocity are imposed. Simulations are performed in order to investigate how the horizon length of the information from the traffic lights influences the fuel consumption. Compared to a benchmark vehicle without knowledge of future light signals, the proposed controller using a control horizon of 1000m saves 26% of energy with similar trip time. Increasing the control horizon further does not improve the results.

Place, publisher, year, edition, pages
Institute of Electrical and Electronics Engineers (IEEE), 2018. p. 6119-6124, article id 8619463
Series
IEEE Conference on Decision and Control, ISSN 0743-1546
National Category
Control Engineering
Identifiers
URN: urn:nbn:se:kth:diva-245115DOI: 10.1109/CDC.2018.8619463ISI: 000458114805103Scopus ID: 2-s2.0-85062176670ISBN: 978-1-5386-1395-5 (print)OAI: oai:DiVA.org:kth-245115DiVA, id: diva2:1294094
Conference
57th IEEE Conference on Decision and Control, CDC 2018; Centre of the Fontainebleau in Miami Beach Miami; United States; 17 December 2018 through 19 December 2018
Note

QC 20190306

Available from: 2019-03-06 Created: 2019-03-06 Last updated: 2019-09-03Bibliographically approved

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Mårtensson, Jonas

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