Commenced in January 2007
Paper Count: 32579
Energy Benefits of Urban Platooning with Self-Driving Vehicles
Abstract:The primary focus of this paper is the generation of energy-optimal speed trajectories for heterogeneous electric vehicle platoons in urban driving conditions. Optimal speed trajectories are generated for individual vehicles and for an entire platoon under the assumption that they can be executed without errors, as would be the case for self-driving vehicles. It is then shown that the optimization for the “average vehicle in the platoon” generates similar transportation energy savings to optimizing speed trajectories for each vehicle individually. The introduced approach only requires the lead vehicle to run the optimization software while the remaining vehicles are only required to have adaptive cruise control capability. The achieved energy savings are typically between 30% and 50% for stop-to-stop segments in cities. The prime motivation of urban platooning comes from the fact that urban platoons efficiently utilize the available space and the minimization of transportation energy in cities is important for many reasons, i.e., for environmental, power, and range considerations.
Digital Object Identifier (DOI): doi.org/10.5281/zenodo.2571879Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 1156
 R. E. Stern, S. Cui, M. L. D. Monache, R. Bhadani, M. Bunting, M. Churchill, N. Hamilton, R. Haulcy, H. Pohlmann, F. Wu, B. Piccoli, B. Seibold, J. Sprinkle, and D. B. Work, “Dissipation of stop-and-go waves via control of autonomous vehicles: Field experiments,” Transportation Research Part C: Emerging Technologies, vol. 89, pp. 205 – 221, 2018. (Online). Available: http://www.sciencedirect.com/science/article/pii/S0968090X18301517
 A. A. Alam, A. Gattami, and K. H. Johansson, “An experimental study on the fuel reduction potential of heavy duty vehicle platooning,” in 13th International IEEE Conference on Intelligent Transportation Systems, Sept 2010, pp. 306–311.
 A. Davila. (2013) Report on fuel consumption. SARTRE, Deliverables. (Accessed: Dec. 10, 2018.). (Online). Available: https://www.sp.se/sv/index/research/dependable systems/Documents/ The%20SARTRE%20project.pdf
 X.-Y. Lu and S. Shladover, Automated Truck Platoon Control and Field Test, Road Vehicle Automation. Springer International Publishing, 08 2014.
 M. Hovgard and O. Jonsson, “Energy-optimal platooning with hybrid vehicles,” Master’s thesis, Chalmers University of Technology, Gothenburg, Sweden, 2017, (Accessed: Dec. 10, 2018.) (Online). Available: http://publications.lib.chalmers.se/records/fulltext/250408/250408.pdf
 H. Q. Le, I. Rashdan, and S. Sand, “Communication protocol for platoon of electric vehicles in mixed traffic scenarios,” in 2016 IEEE 27th Annual International Symposium on Personal, Indoor, and Mobile Radio Communications (PIMRC), Sept 2016, pp. 1–5.
 S. Zhao, T. Zhang, N. Wu, H. Ogai, and S. Tateno, “Vehicle to vehicle communication and platooning for ev with wireless sensor network,” in 2015 54th Annual Conference of the Society of Instrument and Control Engineers of Japan (SICE), July 2015, pp. 1435–1440.
 Y. Choi, D. Kang, S. Lee, and Y. Kim, “The autonomous platoon driving system of the on line electric vehicle,” in 2009 ICCAS-SICE, Aug 2009, pp. 3423–3426.
 J. Hooker, “Optimal driving for single-vehicle fuel economy,” Transportation Research Part A: General, vol. 22, no. 3, pp. 183 – 201, 1988. (Online). Available: http://www.sciencedirect.com/science/article/ pii/0191260788900362
 M. Henriksson, O. Flrdh, and J. Mrtensson, “Optimal speed trajectories under variations in the driving corridor,” IFAC-PapersOnLine, vol. 50, no. 1, pp. 12 551 – 12 556, 2017, 20th IFAC World Congress.
[Online]. Available: http://www.sciencedirect.com/science/article/pii/ S2405896317328641
 S. Mandava, K. Boriboonsomsin, and M. Barth, “Arterial velocity planning based on traffic signal information under light traffic conditions,” in 2009 12th International IEEE Conference on Intelligent Transportation Systems, Oct 2009, pp. 1–6.
 X. Qi, G. Wu, P. Hao, K. Boriboonsomsin, and M. J. Barth, “Integrated-connected eco-driving system for phevs with co-optimization of vehicle dynamics and powertrain operations,” IEEE Transactions on Intelligent Vehicles, vol. 2, no. 1, pp. 2–13, March 2017.
 G. D. Nunzio, C. C. de Wit, P. Moulin, and D. D. Domenico, “Eco-driving in urban traffic networks using traffic signal information,” in 52nd IEEE Conference on Decision and Control, Dec 2013, pp. 892–898.
 Z. Yi and P. H. Bauer, “Effects of environmental factors on electric vehicle energy consumption: a sensitivity analysis,” IET Electrical Systems in Transportation, vol. 7, no. 1, pp. 3–13, 2017.
 M. Ehsani, Y. Gao, and A. Emadi, Modern Electric, Hybrid Electric, and Fuel Cell Vehicles: Fundamentals, Theory, and Design, Second Edition, ser. Power Electronics and Applications Series. CRC Press, 2009.
 Dynamometer drive schedules. United States Environmental Protection Agency (EPA). (Accessed: Aug. 06, 2018) (Online). Available: https://www.epa.gov/vehicle-and-fuel-emissions-testing/dynamometer-drive-schedules
 R. Akelik and M. Besley, “Acceleration and deceleration models,” in 23rd Conference of Australian Institutes of Transport Research (CAITR 2001), Jan 2001.