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In modern Diesel engines Exhaust Gas Recirculation (EGR) and Variable Geometry Turbochargers (VGT) have been introduced to meet the new emission requirements. A control structure that coordinates and handles emission limits and low fuel consumption has been developed. This controller has a set of PID controllers with parameters that need to be tuned. To be able to achieve good performance, an optimization based tuning method is developed and tested. In the optimization the control objectives are captured by a cost function. To aid the tuning a systematic method has been developed for selecting representative and significant transients that excite different modes in the controller. The performance is evaluated on the European Transient Cycle. It is demonstrated how weighting factors in the cost function influence control behavior, and that the proposed tuning method gives a significant improvement in control performance compared to standardized tuning methods for PID controllers.

For a fuel optimal gear shift control, when look ahead information is available, the impact of the automated manual transmission (AMT) gear-shifting process is analyzed. For a standard discrete heavy truck transmission, answers are found on when to shift gears, prior to or when in an uphill slope. The gear-shifting process of a standard AMT is modeled in order to capture the fuel and time aspects of the gear shift. A numerical optimization is performed by dynamic programming, minimizing fuel consumption and time by controlling fuel injection and gear. Since a standard AMT does not have look ahead information, it sometimes gears down unnecessarily and thus gives a significantly higher fuel consumption compared to the optimal control. However, if gearing down is inevitable, the AMT gear-shifting strategy, based on engine thresholds, is well-functioning so that the optimal control only gives marginal additional savings.

The problem addressed is how to drive a heavy truck over various road topographies such that the fuel consumption is minimized. Using a realistic model of a truck powertrain, an optimization problem for minimization of fuel consumption is formulated. Through the solutions of this problem optimal speed profiles are found. An advantage here is that explicit analytical solutions can be found, and this is done for a few constructed test roads. The test roads are constructed to be easy enough to enable analytical solutions but still capture the important properties of real roads. In this way the obtained solutions provide explanations to some behaviour obtained by ourselves and others using more elaborate modeling and numeric optimization like dynamic programming. The results show that for level road and in small gradients the optimal solution is to drive with constant speed.

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.