Prediction of knock propensity using stochastic modeling in a spark-ignition engine

Abstract

To comply with stringent CO2 regulations, enhanced thermal efficiency has been prioritized in internal combustion engine development; however, this has strongly driven the development of engines with operating conditions more prone to knock. Current knock sensors have its limitations to decompose knock signal by degradation so that it required a cross-referencing signal. In addition, knock control intervention is currently preceded by the occurrence of the knock, leading to decrease in thermal efficiency by retarding spark timing. In the present work, a novel prediction model for knock propensity (incidence) is presented, aiming to enable active control of knock or autoignition, and to support conventional knock sensor for cross-referencing by facilitating virtual knock sensor. A zero-dimensional model-based prediction of the in-cylinder pressure is demonstrated to prevent using in-cylinder pressure transducer, along with other incorporated predictive sub-models for the residual gas fraction, heat loss, burn duration, and heat release rate. Ignition delay correlation and Livengood-Wu relation are used to predict the onset of knock, and a burn point-based criterion is newly proposed for application in stochastic modeling for determining the knock propensity. The predicted knock propensity from the combined holistic model shows a remarkable agreement with experimental results.