A Model-Based Methodology for Real-Time Estimation of Diesel Engine Cylinder Pressure

Abstract

Cylinder pressure is one of the most important parameters characterizing the combustion process in an internal combustion engine. The recent developments in engine control technologies suggest the use of cylinder pressure as a feedback signal for closed-loop combustion control. However, the sensors measuring in-cylinder pressure are typically subject to noise and offset issues, requiring signal processing methods to be applied to obtain a sufficiently accurate pressure trace. The signal conditioning implies a considerable computational burden, which ultimately limits the use of cylinder pressure sensing to laboratory testing, where the signal can be processed off-line. In order to enable closed-loop combustion control through cylinder pressure feedback, a real-time algorithm that extracts the pressure signal from the in-cylinder sensor is proposed in this study. The algorithm is based on a crank-angle based engine combustion of that predicts the in-cylinder pressure from the definition of a burn rate function. The model is then adapted to model-based estimation by applying an extended Kalman filter in conjunction with a recursive least-squares estimation scheme. The estimator is tested on a high-fidelity diesel engine simulator as well as on experimental data obtained at various operating conditions. The results obtained show the effectiveness of the estimator in reconstructing the cylinder pressure on a crank-angle basis and in rejecting measurement noise and modeling errors. Furthermore, a comparative study with a conventional signal processing method shows the advantage of using the derived estimator, especially in the presence of high signal noise (as frequently happens with low-cost sensors).