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The traditional method of engine knock detection is to compare the knock intensity with a predetermined threshold. The calibration of this threshold is complex and difficult. A statistical knock detection method is proposed in this paper to reduce the effort of calibration. This method dynamically calculates the knock threshold to determine the knock event. Theoretically, this method will not only adapt to different fuels but also cope with engine aging and engine-to-engine variation without re-calibration. This method is demonstrated by modeling and evaluation using real-time engine dynamometer test data.

Knock correction is the spark angle retard applied to the optimum ignition timing to eliminate knock. In adaptive knock control, this amount of spark retard at an operating point (i.e. Speed, load) is stored in a speed/load characteristic map. It will be reused when the engine is operated in this range once more. In this paper, a method to learn the knock correction values into a speed/load characteristic map is described. This method proportionally distributes the knock correction into the characteristic map according to the distance between the speed/load of these nodes and the current operating point. The distributed knock correction value is filtered and accumulated in its adjacent nodes. Simulation examples demonstrate that the retrieved values from the map by the proposed method are smoother than those produced by the method of [2][3]. The mathematical basis for this method is developed. The one and two independent variable cases are illustrated.