Coordinated platooning with multiple speeds

Luo, Fengqiao; Larson, Jeffrey; Munson, Todd

doi:10.1016/j.trc.2018.02.011

Coordinated platooning with multiple speeds

Journal Article · Thu Mar 22 00:00:00 EDT 2018 · Transportation Research Part C: Emerging Technologies

DOI:https://doi.org/10.1016/j.trc.2018.02.011· OSTI ID:1429090

Luo, Fengqiao ^[1]; ^[2]; Munson, Todd ^[2]

Northwestern Univ., Evanston, IL (United States)
Argonne National Lab. (ANL), Argonne, IL (United States)

In a platoon, vehicles travel one after another with small intervehicle distances; trailing vehicles in a platoon save fuel because they experience less aerodynamic drag. This work presents a coordinated platooning model with multiple speed options that integrates scheduling, routing, speed selection, and platoon formation/dissolution in a mixed-integer linear program that minimizes the total fuel consumed by a set of vehicles while traveling between their respective origins and destinations. The performance of this model is numerically tested on a grid network and the Chicago-area highway network. We find that the fuel-savings factor of a multivehicle system significantly depends on the time each vehicle is allowed to stay in the network; this time affects vehicles’ available speed choices, possible routes, and the amount of time for coordinating platoon formation. For problem instances with a large number of vehicles, we propose and test a heuristic decomposed approach that applies a clustering algorithm to partition the set of vehicles and then routes each group separately. When the set of vehicles is large and the available computational time is small, the decomposed approach finds significantly better solutions than does the full model.

View Accepted Manuscript (DOE)

Research Organization:: Argonne National Lab. (ANL), Argonne, IL (United States)

Sponsoring Organization:: USDOE Office of Science (SC), Basic Energy Sciences (BES) (SC-22)

Grant/Contract Number:: AC02-06CH11357

OSTI ID:: 1429090

Alternate ID(s):: OSTI ID: 1548570

Journal Information:: Transportation Research Part C: Emerging Technologies, Journal Name: Transportation Research Part C: Emerging Technologies Journal Issue: C Vol. 90; ISSN 0968-090X

Publisher:: ElsevierCopyright Statement

Country of Publication:: United States

Language:: English

References (27)

Exact stability of a platoon of vehicles by considering time delay and lag Ghasemi, Ali; Kazemi, Reza; Azadi, Shahram Journal of Mechanical Science and Technology, Vol. 29, Issue 2 https://doi.org/10.1007/s12206-015-0142-x	journal	February 2015
Current status of hybrid, battery and fuel cell electric vehicles: From electrochemistry to market prospects Pollet, Bruno G.; Staffell, Iain; Shang, Jin Lei Electrochimica Acta, Vol. 84 https://doi.org/10.1016/j.electacta.2012.03.172	journal	December 2012
Stochastic modeling for vehicle platoons (II): Statistical characteristics Li, Baibing Transportation Research Part B: Methodological, Vol. 95 https://doi.org/10.1016/j.trb.2016.07.017	journal	January 2017
Stochastic modeling for vehicle platoons (I): Dynamic grouping behavior and online platoon recognition Li, Baibing Transportation Research Part B: Methodological, Vol. 95 https://doi.org/10.1016/j.trb.2016.07.019	journal	January 2017
Planning of truck platoons: A literature review and directions for future research Bhoopalam, Anirudh Kishore; Agatz, Niels; Zuidwijk, Rob Transportation Research Part B: Methodological, Vol. 107 https://doi.org/10.1016/j.trb.2017.10.016	journal	January 2018
The identical-path truck platooning problem Boysen, Nils; Briskorn, Dirk; Schwerdfeger, Stefan Transportation Research Part B: Methodological, Vol. 109 https://doi.org/10.1016/j.trb.2018.01.006	journal	March 2018
Optimal routing for automated highway systems Baskar, Lakshmi Dhevi; De Schutter, Bart; Hellendoorn, Hans Transportation Research Part C: Emerging Technologies, Vol. 30 https://doi.org/10.1016/j.trc.2013.01.006	journal	May 2013
On the network connectivity of platoon-based vehicular cyber-physical systems Jia, Dongyao; Lu, Kejie; Wang, Jianping Transportation Research Part C: Emerging Technologies, Vol. 40 https://doi.org/10.1016/j.trc.2013.10.006	journal	March 2014
Modeling cooperative and autonomous adaptive cruise control dynamic responses using experimental data Milanés, Vicente; Shladover, Steven E. Transportation Research Part C: Emerging Technologies, Vol. 48 https://doi.org/10.1016/j.trc.2014.09.001	journal	November 2014
POLARIS: Agent-based modeling framework development and implementation for integrated travel demand and network and operations simulations Auld, Joshua; Hope, Michael; Ley, Hubert Transportation Research Part C: Emerging Technologies, Vol. 64 https://doi.org/10.1016/j.trc.2015.07.017	journal	March 2016
The vehicle platooning problem: Computational complexity and heuristics Larsson, Erik; Sennton, Gustav; Larson, Jeffrey Transportation Research Part C: Emerging Technologies, Vol. 60 https://doi.org/10.1016/j.trc.2015.08.019	journal	November 2015
Platoon based cooperative driving model with consideration of realistic inter-vehicle communication Jia, Dongyao; Ngoduy, Dong Transportation Research Part C: Emerging Technologies, Vol. 68 https://doi.org/10.1016/j.trc.2016.04.008	journal	July 2016
A conceptualization of vehicle platoons and platoon operations Maiti, Santa; Winter, Stephan; Kulik, Lars Transportation Research Part C: Emerging Technologies, Vol. 80 https://doi.org/10.1016/j.trc.2017.04.005	journal	July 2017
Vehicle platoon formation using interpolating control: A laboratory experimental analysis Tuchner, Alon; Haddad, Jack Transportation Research Part C: Emerging Technologies, Vol. 84 https://doi.org/10.1016/j.trc.2017.06.019	journal	November 2017
Economy-oriented vehicle adaptive cruise control with coordinating multiple objectives function Eben Li, Shengbo; Li, Keqiang; Wang, Jianqiang Vehicle System Dynamics, Vol. 51, Issue 1 https://doi.org/10.1080/00423114.2012.708421	journal	January 2013
Applying a structured simulation-based methodology to assess carpooling time–space potential Correia, Gonçalo; Viegas, José Manuel Transportation Planning and Technology, Vol. 33, Issue 6 https://doi.org/10.1080/03081060.2010.505053	journal	August 2010
Fuel-saving potentials of platooning evaluated through sparse heavy-duty vehicle position data Liang, Kuo-Yun; Martensson, Jonas; Johansson, Karl H. 2014 IEEE Intelligent Vehicles Symposium (IV), 2014 IEEE Intelligent Vehicles Symposium Proceedings https://doi.org/10.1109/IVS.2014.6856540	conference	June 2014
The State of the Art of Electric, Hybrid, and Fuel Cell Vehicles Chan, C. C. Proceedings of the IEEE, Vol. 95, Issue 4 https://doi.org/10.1109/JPROC.2007.892489	journal	April 2007
A Distributed Framework for Coordinated Heavy-Duty Vehicle Platooning Larson, Jeffrey; Liang, Kuo-Yun; Johansson, Karl H. IEEE Transactions on Intelligent Transportation Systems, Vol. 16, Issue 1 https://doi.org/10.1109/TITS.2014.2320133	journal	February 2015
A Mathematical Theory for Clustering in Metric Spaces Chang, Cheng-Shang; Liao, Wanjiun; Chen, Yu-Sheng IEEE Transactions on Network Science and Engineering, Vol. 3, Issue 1 https://doi.org/10.1109/TNSE.2016.2516339	journal	January 2016
Improving Fuel Economy in Heavy-Duty Vehicles Harrington, Winston; Krupnick, Alan SSRN Electronic Journal https://doi.org/10.2139/ssrn.2038842	journal	January 2012
Analysis of Strategies for Truck Platooning: Hybrid Strategy Saeednia, Mahnam; Menendez, Monica Transportation Research Record: Journal of the Transportation Research Board, Vol. 2547, Issue 1 https://doi.org/10.3141/2547-07	journal	January 2016
Platooning Strategy for Connected and Autonomous Vehicles: Transition from Light Traffic Bang, Soohyuk; Ahn, Soyoung Transportation Research Record: Journal of the Transportation Research Board, Vol. 2623, Issue 1 https://doi.org/10.3141/2623-08	journal	January 2017
Multiobjective Optimization Framework for Cooperative Adaptive Cruise Control Vehicles in the Automated Vehicle Platooning Environment Zhong, Zijia; Lee, Joyoung; Zhao, Liuhui Transportation Research Record: Journal of the Transportation Research Board, Vol. 2625, Issue 1 https://doi.org/10.3141/2625-04	journal	January 2017
Fuel Consumption Reduction in a Platoon: Experimental Results with two Electronically Coupled Trucks at Close Spacing Bonnet, Christophe; Fritz, Hans Future Transportation Technology Conference & Exposition, SAE Technical Paper Series https://doi.org/10.4271/2000-01-3056	conference	August 2000
Predicting Light-Duty Vehicle Fuel Economy as a Function of Highway Speed Thomas, John; Hwang, Ho-Ling; West, Brian SAE International Journal of Passenger Cars - Mechanical Systems, Vol. 6, Issue 2 https://doi.org/10.4271/2013-01-1113	journal	April 2013
Effect of Platooning on Fuel Consumption of Class 8 Vehicles Over a Range of Speeds, Following Distances, and Mass Lammert, Michael P.; Duran, Adam; Diez, Jeremy SAE International Journal of Commercial Vehicles, Vol. 7, Issue 2 https://doi.org/10.4271/2014-01-2438	journal	May 2014

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