  One of the main factors limiting the efficiency of spark-ignited engines is
the occurrence of engine knock. In high temperature and high pressure
in-cylinder conditions, the fuel-air mixture auto-ignites creating pressure
shock waves in the cylinder. Knock can significantly damage the engine and
hinder its performance; as such, conservative knock control strategies are
generally implemented that avoid such operating conditions at the cost of lower
thermal efficiencies. Significant improvements in the performance of
conventional knock controllers are possible if the properties of the knock
process are better characterized and exploited in knock controller designs. One
of the methods undertaken to better characterize knocking instances is to
employ a probabilistic approach, in which the likelihood of knock is derived
from the statistical distribution of knock intensity. In this paper, it is
shown that knock intensity values at a fixed operating point for single fuel
and dual fuel engines are accurately described using a mixed lognormal
distribution. The fitting accuracy is compared against those for a randomly
generated mixed-lognormally distributed data set, and shown to exceed a 95%
accuracy threshold for almost all of the operating points tested. Additionally,
this paper discusses a stochastic knock control approach that leverages the
mixed lognormal distribution to adjust spark timing based on knock intensity
measurements. This more informed knock control strategy would allow for
improvements in engine performance and fuel efficiency by minimizing knock
occurrences.
