Stochastic Knock Detection Model for Spark Ignited Engines

This paper presents the development of a Stochastic Knock Detection (SKD) method for combustion knock detection in a spark-ignition engine using a model based design approach. The SKD set consists of a Knock Signal Simulator (KSS) as the plant model for the engine and a Knock Detection Module (KDM). The KSS as the plant model for the engine generates cycle-to-cycle accelerometer knock intensities following a stochastic approach with intensities that are generated using a Monte Carlo method from a lognormal distribution whose parameters have been predetermined from engine tests and dependent upon spark-timing, engine speed and load. The lognormal distribution has been shown to be a good approximation to the distribution of measured knock intensities over a range of engine conditions and spark-timings for multiple engines in previous studies. The KDM processes these signals with a stochastic distribution estimation algorithm which outputs estimates of knock intensity and at a level characteristic of high knock and a referenced level which are then used to determine a calibrated and referenced knock factor. The knock factor is analyzed and compared with a traditional knock detection method to detect engine knock under various engine operating conditions.