Adaptive in-cylinder pressure model for spark ignition engine control

In-cylinder pressure is the most important variable to analyze the combustion process in internal combustion engines, and can be used as feedback signal for closed-loop combustion control and diagnostics. However, pressure sensors are still affected by challenges such as durability and cost, which prevent their use in massproduction vehicles. Therefore, this work presents a model-based approach to estimate the in-cylinder pressure by means of the combination of a control oriented model and information from the set of sensors available in current production engines for automotive applications. Pressure peak location of each cylinder is estimated through the knock sensor signal, and used as feedback to improve the model. An extended Kalman filter is used to adapt the model to the information from the knock sensor signal. The adaptive model is implemented in a four cylinder light-duty engine and compared with the open loop model, ensuring a continuous estimation of incylinder pressure signal, and an improvement for the estimation of different cycle by cycle combustion parameters and cylinder to cylinder variations. Finally, the proposed approach is applied with different fuels showing that the proposed method can be applied independently on the fuel used.

Automated summary

The study proposes a model-based method to estimate in-cylinder pressure by combining a control-oriented model with sensor information available in current production engines for automotive applications. The adaptive model, using an extended Kalman filter, improves the estimation of pressure peak location through knock sensor signal. The approach shows continuous estimation of in-cylinder pressure signal and enhancement in estimation of combustion parameters and cylinder variations in a four-cylinder light-duty engine.