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Stochastic Knock Detection Model for Spark Ignited Engines
Technical Paper
2011-01-1421
ISSN: 0148-7191, e-ISSN: 2688-3627
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English
Abstract
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.
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Authors
Topic
Citation
Lonari, Y., Polonowski, C., Naber, J., and Chen, B., "Stochastic Knock Detection Model for Spark Ignited Engines," SAE Technical Paper 2011-01-1421, 2011, https://doi.org/10.4271/2011-01-1421.Also In
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