Cylinder pressure is one of the most important parameters characterizing the combustion process in an internal combustion engine. The recent developments in piezoelectric pressure transducers and progress in on-line computational throughput facilitate the use of cylinder pressure as a feedback signal for engine combustion control. However, a typical production cylinder pressure sensor is subject to noise and offset issues that require signal processing methods, including averaging over several engine cycles, in order to extract a pressure trace sufficiently accurate for combustion characterization. This limits the application of cylinder pressure sensing to off-line applications. In order to enable closed-loop combustion control using cylinder pressure feedback, this study proposes a real-time estimation algorithm that extracts the pressure signal on a crank-angle basis. A simplified thermodynamic model for Diesel engine combustion is derived to predict the in-cylinder pressure. The model is then adapted to model-based estimation, by applying an Extended Kalman Filter in conjunction with a recursive least squares estimation. The resulting estimator is tested on a high-fidelity Diesel engine model for different operating conditions. The results obtained show the effectiveness of the estimator in reconstructing the cylinder pressure and in rejecting measurement noise and modeling errors.

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