Energy Benefits of Urban Platooning with Self-Driving Vehicles
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.
[1] 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
[2] 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.
[3] 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
[4] X.-Y. Lu and S. Shladover, Automated Truck Platoon Control and Field
Test, Road Vehicle Automation. Springer International Publishing, 08
2014.
[5] 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
[6] 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.
[7] 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.
[8] 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.
[9] 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
[10] 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
[11] 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.
[12] 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. [13] 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.
[14] 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.
[15] 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.
[16] 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
[17] R. Akelik and M. Besley, “Acceleration and deceleration models,” in
23rd Conference of Australian Institutes of Transport Research (CAITR
2001), Jan 2001.
[1] 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
[2] 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.
[3] 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
[4] X.-Y. Lu and S. Shladover, Automated Truck Platoon Control and Field
Test, Road Vehicle Automation. Springer International Publishing, 08
2014.
[5] 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
[6] 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.
[7] 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.
[8] 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.
[9] 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
[10] 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
[11] 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.
[12] 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. [13] 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.
[14] 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.
[15] 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.
[16] 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
[17] R. Akelik and M. Besley, “Acceleration and deceleration models,” in
23rd Conference of Australian Institutes of Transport Research (CAITR
2001), Jan 2001.
@article{"International Journal of Mechanical, Industrial and Aerospace Sciences:78405", author = "Eduardo F. Mello and Peter H. Bauer", title = "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.", keywords = "Electric vehicles, energy efficiency, optimization,
platooning, self-driving vehicles, urban traffic.", volume = "13", number = "2", pages = "94-7", }
