Look-ahead speed planning for heavy-duty vehicle platoons using traffic information | Zendy

Ingrid Johansson | Zendy; Junchen Jin | Zendy; Xiaoliang Ma | Zendy; Henrik Pettersson | Zendy

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Look-ahead speed planning for heavy-duty vehicle platoons using traffic information

Author(s) -

Ingrid Johansson,

Junchen Jin,

Xiaoliang Ma,

Henrik Pettersson

Publication year - 2017

Publication title -

transportation research procedia

Language(s) - English

Resource type - Journals

SCImago Journal Rank - 0.657

H-Index - 40

eISSN - 2352-1465

pISSN - 2352-1457

DOI - 10.1016/j.trpro.2017.03.045

Subject(s) - fuel efficiency , automotive engineering , acceleration , intelligent transportation system , control (management) , reduction (mathematics) , transport engineering , simulation , computer science , engineering , physics , geometry , mathematics , classical mechanics , artificial intelligence

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.

QC 20170330

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