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Modern gasoline engines have increased part-load fuel economy and specific power output through technologies such as downsizing, turbocharging, direct injection, and exhaust gas recirculation. These engines tend to have higher sensitivity to driving behavior because of the steady-state efficiency versus output characteristics (e.g., sweet spot at lower output) and the dynamic response characteristics (e.g., turbo lag). It has been observed that the technologies aimed at increased engine efficiency may improve fuel economy for less aggressive cycles and drivers while hurting fuel economy for more aggressive cycles and drivers. The higher degrees of freedom in these engines and the increased sensitivity make controls and calibration more complex and more important at the same time.

Turbocharger compressors are susceptible to surge – the instability phenomena that impose limitations on the operation of turbocharged engines because of undesired noise, engine torque capability constraints, and hardware strain. Turbocharged engines are typically equipped with a binary compressor recirculation valve (CRV) whose primary function is to prevent compressor surge. Calibration of the associated control strategy requires in-vehicle tests and usually employs subjective criteria. This work aims to reduce the calibration effort for the strategy by developing a test procedure and data processing algorithms. An automated calibration for CRV control is developed that will generate a baseline calibration that avoids surge events. The effort to obtain the baseline calibration, which can be further fine-tuned, is thereby significantly reduced.

An experimental study is performed for homogeneous charge compression ignition (HCCI) combustion focusing on late phasing conditions with high cyclic variability (CV) approaching misfire. High CV limits the feasible operating range and the objective is to understand and quantify the dominating effects of the CV in order to enable controls for widening the operating range of HCCI. A combustion analysis method is developed for explaining the dynamic coupling in sequences of combustion cycles where important variables are residual gas temperature, combustion efficiency, heat release during re-compression, and unburned fuel mass. The results show that the unburned fuel mass carries over to the re-compression and to the next cycle creating a coupling between cycles, in addition to the well known temperature coupling, that is essential for understanding and predicting the HCCI behavior at lean conditions with high CV.

Hybridization and velocity management are two important techniques for energy efficiency that mainly have been treated separately. Here they are put in a common framework that from the hybridization perspective can be seen as an extension of the equivalence factor idea in the well known strategy ECMS. From the perspective of look-ahead control, the extension is that energy can be stored not only in kinetic energy, but also electrically. The key idea is to introduce more equivalence factors in a way that enables efficient computations, but also so that the equivalence factors have a physical interpretation. The latter fact makes it easy to formulate a good residual cost to be used at the end of the look-ahead horizon. The formulation has different possible uses, but it is here applied on an evaluation of the size of the electrical system. Previous such studies, for e.g.

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.