Optimal velocity prediction for fuel economy improvement of connected vehicles

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.