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A Control-Oriented Knock Intensity Estimator
ISSN: 1946-3936, e-ISSN: 1946-3944
Published September 04, 2017 by SAE International in United States
Citation: Corti, E., Forte, C., Bianchi, G., and Zoffoli, L., "A Control-Oriented Knock Intensity Estimator," SAE Int. J. Engines 10(4):2219-2229, 2017, https://doi.org/10.4271/2017-24-0055.
The performance optimization of modern Spark Ignition engines is limited by knock occurrence: heavily downsized engines often are forced to work in the Knock-Limited Spark Advance (KLSA) range. Knock control systems monitor the combustion process, allowing to achieve a proper compromise between performance and reliability. Combustion monitoring is usually carried out by means of accelerometers or ion sensing systems, but recently the use of cylinder pressure sensors is also becoming frequent in motorsport applications. On the other hand, cylinder pressure signals are often available in the calibration stage, where SA feedback-control based on the pressure signal can be used to avoid damages to the engine during automatic calibration.
A predictive real-time combustion model could help optimizing engine performance, without exceeding the allowed knock severity. Several knock models are available in the literature: most of those proposed for real-time applications are single zone or two-zone models, grounded on more complex CFD simulations. However, since the knock phenomenon is influenced by several factors, the real-time determination of KLSA requires the model to be adapted to the engine actual behavior. The approach proposed in the present paper, is based on the constant adaptation of a two-zone model to measured cylinder pressure data: typical results of the indicating analysis, available cycle-by-cycle and cylinder-by-cylinder, are used as inputs for the model, with the aim of predicting KLSA for the current running condition, without exceeding the maximum allowed knock intensity.
The approach has been applied to indicating data referring to non-knocking, light and heavy knocking conditions, showing a good prediction capability.