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
2018-10-02
7 pages
Aufsatz (Zeitschrift)
Elektronische Ressource
Englisch
sequential quadratic programming method , vehicle-to-infrastructure technology , energy management systems , optimal velocity trajectory generation , optimal velocity prediction , vehicular ad hoc networks , vehicle-to-vehicle technology , tractive force requirement , velocity bounds , powertrain information , energy management , transportation system , prediction horizon , quadratic programming , travel time , nonlinearly constrained optimisation problem , fuel economy improvement , connected vehicles , rule-based mode selection , fuel economy , traffic system , fuel consumption reduction , dynamic loss reduction , Autonomie , trip distance completion
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