Evaluation of a statistical method to use prior information of combustion parameters
Ion current sensing, where information about the combustion process in
an SIengine is gained by applying a voltage over the spark gap, is
currently used to detect and avoid knock and misfire. Several
researchers have pointed out that information on peak pressure
location and air/fuel ratio can be gained from the ion current and
have suggested several ways to estimate these parameters. Here a
simplified Bayesian approach was taken to construct a lowpass-like
filter or estimator that makes use of prior information to improve
estimates in crucial areas. The algorithm is computationally light and
could, if successful, improve estimates enough for production use.
The filter was implemented in several variants and evaluated in a
number of simulated cases. It was found that the proposed filter
requires a number of tradeoffs between variance, bias, tracking speed
and accuracy that are difficult to balance. For satisfactory
estimates and trade-off balance the prior information must be more
accurate than was available. It was also found that similar a task,
constructing a general Bayesian estimator, has already been tackled in
the area of particle filtering and that there are promising and
unexplored possibilities there. However, particle filters require
computational power that will not be available to production engines
for some years.
Patrick Rundin
2006
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Last updated: 2021-11-10
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