Application of Extended Kalman Filter to On-Line Diesel Engine Cylinder Pressure Estimation

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.