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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.

By using engine controlled gear shifting, a manual transmission can be automated without using the clutch during the shift event. The main contribution of this paper is a novel extension of the existing system using active handling of driveline resonances. The strategy is based on a model of the transmission torque, which is derived by using experimental data from a heavy truck. A key step is the design of a criterion for a controller that drives the transmission torque to zero with damped resonances and with a control signal realizable by the engine. The proposed solution offers a possibility to optimize the time needed for a gear shift, which is important since the vehicle is free rolling when in gear-shift condition. Furthermore, neutral gear can successfully be engaged also when facing initial driveline oscillations and load disturbances.

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.