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Coming along with the present movement towards the ultimately variable engine, the need for clear and simple models for complex engine systems is rapidly increasing. In this context Common-Rail-Systems cause a special kind of problem due to of the high amount of parameters which cannot be taken into consideration with simple map-based models. For this reason models with a higher amount of complexity are necessary to realize a representative behavior of the simulation. The high computational time of the simulation, which is caused by the increased complexity, makes it nearly impossible to implement this type of model in software in closed loop applications or simulations for control purposes. In this paper a method for decreasing the complexity and accelerating the computing time of automotive engine models is being evaluated which uses an optimized method for each stage of the diesel engine process.

A new procedure is presented, that enables the estimation of the air mass in the cylinder of an SI engine by using a combustion pressure sensor. First of all, a physically based model is developed. Afterwards, the air mass in the cylinder and further physically based parameters are calculated with an adaptive Kalman filter algorithm. The procedure was developed on the basis of real measurement data obtained from test drives. Finally the results of the developed algorithm are compared with current solutions.