Stochastic Knock Detection Model for Spark Ignited Engines Michigan Technological Univ

Format:

Conference/Event

Author/Creator:

Lonari, Lonari, author.

Contributor:

Chen, Bo

Naber, Jeffrey

Polonowski, Christopher

Conference Name:

SAE 2011 World Congress & Exhibition (2011-04-12 : Detroit, Michigan, United States)

Language:

English

Physical Description:

1 online resource

Place of Publication:

Warrendale, PA SAE International 2011

Summary:

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

Notes:

Vendor supplied data

Publisher Number:

2011-01-1421

Access Restriction:

Restricted for use by site license