Cylinder pressure is one of the most important parameters that characterize the combustion process in an internal combustion engine. 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 production grade sensor is proposed in this study. The algorithm is based on a crank-angle based engine combustion 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 at certain operating points 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).
As an extension and further application, this methodology is built upon to cover a wider range of operations as well as transient data. Linear parameter varying techniques using genetic algorithms are utilized to identify the gains of linear spline functions of the LPV-corrector estimator. The LPV-corrector performs well with a relatively small computation burden. The two estimators are examined under both steady state data and transient data, where the comparison criteria include estimation of combustion metrics.
Finally, a model-based estimation methodology that facilitates real-time reconstruction of individual in-cylinder pressure utilizing a minimum sensor set is demonstrated. Based on a derived crankshaft speed model incorporated with the pressure model, a sliding mode observer is implemented, wherein chattering is mitigated and the estimation design is validated. Adding disturbances to the model parameter degrades the performance of the SMO, which motivates the development of an adaptive-SMO based on the certainty equivalence principle, utilizing the cylinder pressure signal from one cylinder. The estimator was derived analytically and a proof of stability is provided.