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New and exciting possibilities in vehicle control are revealed by the consideration of topography, for example through the combination of GPS and three dimensional road maps. How information about future road slopes can be utilized in a heavy truck is explored. The aim is set at reducing the fuel consumption over a route without increasing the total travel time. A model predictive control (MPC) scheme is used to control the longitudinal behavior of the vehicle, which entails determining accelerator and brake levels and also which gear to engage. The optimization is accomplished through discrete dynamic programming. A cost function that weighs fuel use, negative deviations from the reference velocity, velocity changes, gear shifts and brake use is used to define the optimization criterion. Computer simulations back and forth on 127 km of a typical highway route in Sweden, show that the fuel consumption in a heavy truck can be reduced with 2.5% with a negligible change in travel time.

Rolling resistance of inflated tires is a factor that contributes to the total load and fuel consumption of a vehicle. Therefore, models of rolling resistance is an important area within computer simulations of vehicles used to predict fuel consumption and emissions. In these applications the coefficient of rolling resistance is usually described as a function of velocity. We have earlier shown that this is not a satisfactory solution [1, 2]. In this paper it is demonstrated that the temperature of the tires is a dominating factor for rolling resistance in real driving. The tires typically start at ambient temperature and are then warmed up by the heat generated in the tire. As the temperature increases the rolling resistance decreases (to some limit). After a long period (2 hours for truck tires) of driving at constant conditions, a stationary temperature (and rolling resistance) is reached.