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Advanced engine control and diagnosis strategies for internal combustion engines need accurate feedback information from the combustion engine. The feedback information can be utilized to control combustion features which allow the improvement of engine's efficiency through real-time control and diagnosis of the combustion process. This article describes a new method for combustion phase and IMEP estimation using one in-cylinder pressure and engine speed. In order to take torsional deflections of the crankshaft into account a gray-box model of the crankshaft is identified by subspace identification. The modeling accuracy is compared to a stiff physical crankshaft model. For combustion feature estimation, the identified MISO (multiple input single output) system is inverted. Experiments for a four-cylinder spark-ignition engine show the superior performance of the new method for combustion feature estimation compared to a stiff model approach.

The resonances of SI engine combustion chambers are slightly excited during normal combustion but strongly excited by knock. In order to avoid knocking combustions extensive knowledge about knock and its effects is necessary. In this paper the combustion chamber of a serial production engine is modeled by finite elements. Modal analyses are performed in order to gain information about the resonances, their frequencies, and their frequency and amplitude modulations. Simulation results are compared to measured data using a high-resolution time-frequency method. Furthermore, a connection between knock origin and the excitation of the resonances is postulated applying transient analyses.

In order to optimize the performance and emission of engines, advanced control and diagnostic systems require detailed feedback information about the combustion process. In this context, cost-effective solutions are of interest. The contribution describes a method for reconstructing cylinder-individual features of each combustion cycle by processing the instantaneous fluctuations of the engine speed and the in-cylinder pressure of one cylinder. Model-based torque estimation, analyzing both of the signals simultaneously, provides an accurate estimation of the mean indicated pressure. Using this method, a new algorithm for advanced misfire detection is presented. Furthermore, a new pressure model with a feasible number of parameters is proposed. It is combined with the torque estimation in order to reconstruct the unknown pressure traces of the cylinders not equipped with sensors.