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.
Optimal velocity prediction for fuel economy improvement of connected vehicles
IET Intelligent Transport Systems ; 12 , 10 ; 1329-1335
2018-12-01
7 pages
Article (Journal)
Electronic Resource
English
trip distance completion , fuel economy , travel time , rule‐based mode selection , velocity bounds , energy management , fuel economy improvement , vehicle‐to‐vehicle technology , traffic system , fuel consumption reduction , energy management systems , Autonomie , dynamic loss reduction , optimal velocity prediction , vehicular ad hoc networks , prediction horizon , sequential quadratic programming method , quadratic programming , transportation system , optimal velocity trajectory generation , vehicle‐to‐infrastructure technology , powertrain information , connected vehicles , tractive force requirement , nonlinearly constrained optimisation problem
Optimal velocity prediction for fuel economy improvement of connected vehicles
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