Optimal velocity prediction for fuel economy improvement of connected vehicles
Document Type
Article
Publication Date
12-1-2018
Abstract
© The Institution of Engineering and Technology 2018. With the advancement of vehicle-to-vehicle and vehicle-to-infrastructure technologies, more and more real-time information regarding traffic and transportation system will be available to vehicles. This paper presents the development of a novel algorithm that uses available velocity bounds and powertrain information to generate an optimal velocity trajectory over a prediction horizon. When utilised by a vehicle, this optimal velocity trajectory reduces fuel consumption. The objective of this optimisation problem is to reduce dynamic losses, required tractive force, and completing trip distance with a given travel time. Sequential quadratic programming method is employed for this nonlinearly constrained optimisation problem. When applied to a GM Volt-2, the generated velocity trajectory saves fuel compared to a real-world drive cycle. The simulation results confirm the fuel consumption reduction with the rule-based mode selection and the energy management strategy of a GM Volt 2 model in Autonomie.
Publication Title
IET Intelligent Transport Systems
Recommended Citation
Barik, B.,
Bhat, P.,
Oncken, J.,
Chen, B.,
Orlando, J.,
&
Robinette, D.
(2018).
Optimal velocity prediction for fuel economy improvement of connected vehicles.
IET Intelligent Transport Systems,
12(10), 1329-1335.
http://doi.org/10.1049/iet-its.2018.5110
Retrieved from: https://digitalcommons.mtu.edu/michigantech-p/8626
