<p>Combustion supervision by evaluating flywheel speed variations is a common approach in the automotive industry. This often involves preliminary measurements. An adequate model for simulating flywheel speed can assist to avoid some of these preliminary measurements. </p><p>A physical nonlinear model for simulating flywheel speed based on cylinder pressure information is investigated in this work. Measurements were conducted at Scania in a test bed and on a chassis dynamometer. The model was implemented in MATLAB/Simulink and simulations are compared to measured data. The first model can not explain all dynamics for the measurements in the test bed so extended models are examined. A model using a dynamically equivalent model of the crank-slider mechanism shows no difference from the simple model, whereas a model including a driveline can explain more from the test-bed measurements. When simulating the setups used at the chassis dynamometer, the simplest model works best. Yet, it is not very accurate and it is proposed that optimization of parameter values might improve the model further. A sensitivity analysis shows that the model is fairly robust to parameter changes.</p><p>A continuation of this work might include optimization to estimate parameter values in the model. Investigating methods for combustion supervision may also be a future issue.</p>

Combustion supervision by evaluating flywheel speed variations is a common approach in the automotive industry. This often involves preliminary measurements. An adequate model for simulating flywheel speed can assist to avoid some of these preliminary measurements. A physical nonlinear model for simulating flywheel speed based on cylinder pressure information is investigated in this work. Measurements were conducted at Scania in a test bed and on a chassis dynamometer. The model was implemented in MATLAB/Simulink and simulations are compared to measured data. The first model can not explain all dynamics for the measurements in the test bed so extended models are examined. A model using a dynamically equivalent model of the crank-slider mechanism shows no difference from the simple model, whereas a model including a driveline can explain more from the test-bed measurements. When simulating the setups used at the chassis dynamometer, the simplest model works best. Yet, it is not very accurate and it is proposed that optimization of parameter values might improve the model further. A sensitivity analysis shows that the model is fairly robust to parameter changes. A continuation of this work might include optimization to estimate parameter values in the model. Investigating methods for combustion supervision may also be a future issue.

To monitor and control the combustion in a diesel engine one can study the speed signal from the flywheel. The idea is that if individual cylinders give different amount of torque this will lead to variations in the flywheel speed. A model which describes the cylinder torque based on flywheel speed can be used to estimate the torque from individual cylinders. With this new knowledge of the individual performance of each cylinder the engine can be balanced. The balancing aim at making the speed of the flywheel more even but also required a model with estimated cylinder torque as input. This model may also be used for testing new control algorithms easily and gaining understanding of the dynamics. In this thesis a time dissolved model is constructed to describe the cylinder pressure-, crankshaft-, flywheel and damper dynamics. The model is based on a physical point of view by approximating the system into nodes containing mass, stiffness and friction. The inputs into the model are injection data from the engine management system (EMS) and a torque from a drive line. Ways to reduce the complexity of the model are investigated in order to invert the model to estimate the injection data based on flywheel speed measurements. Measurementsare done in a test bed to receive data required for model simulation and validation. The result is that the main behavior of the dynamics is caught. The self oscillation behaviors in some operating points are however not caught which indicates that the model can not explain all behaviors. A reduced model works almost as well but of course looses more of the non stiffness behavior. As expected, the model equations can not be solved in real time. The result of the inverted reduced model depends on the flywheel signal. When the signal contains little non stiffness behavior the result is good. An observer model based on the reduced model is suggested and tested in order to estimate the indicated torque from flywheel data. The observer manages to detect errors in the injection.