The objective of this work is to analyze the signal of a piezoelectric washer
installed under the spark plug and to compare the combustion metrics evaluated
with such signal to the indexes from a standard piezoelectric sensor for the
in-cylinder pressure measurement, considered as the reference.
In the first part of the article, the spectrum analysis of the piezoelectric
washer pressure trace is proposed. It is demonstrated how such a signal can be
used to measure the main combustion and knock indexes. Nevertheless, due to the
intrinsic characteristics of the system, the knock index evaluated from the raw
pressure trace cannot be directly used to estimate the instantaneous knock
intensity. For this reason, a model-based algorithm for Real-Time (RT)
application is developed to calculate a corrective factor of the high-frequency
content of the signal. With such an algorithm, the logarithmic mean value of the
Maximum Amplitude of Pressure Oscillation (MAPO) can be accurately evaluated
through an Artificial Neural Network (ANN) to properly scale the indexes
calculated from the washer signal. The algorithm is further developed with
respect to a previous work of the authors by introducing a new function to
account for the intake air temperature and the fuel quality effects on the knock
indexes, and it needs both some variables provided by the Engine Control Unit
(ECU) and the raw washer combustion indexes as inputs.
In the last part of the work, the algorithm is validated at the engine test bench
under steady-state and transient conditions by reproducing dynamic speed and
load profiles.
