On Estimation of Momentary Average Engine Speed and Acceleration
The engine speed is one of the most important signals in the engine
management system of a combustion engine. The signal is used to
control the fuel injection, estimate the engine torque, and to
generate reference values. Combustions in the cylinders result in the
engine speed oscillating around a momentary average, and many
applications are depending on stable estimates of this average engine
speed and the average acceleration. This thesis provides a signal
model based method to estimate the momentary average engine speed and
acceleration.
The estimation of momentary average engine speed and acceleration is
complicated by imperfections in the process of measuring the engine
speed. Limited accuracy in the measurements causes quantization
distortion in the engine speed signal. The effects of these errors are
investigated and quantified.
A signal model representing the engine speed is developed and used to
estimate the momentary average and acceleration using a Kalman
filter. The regular Kalman filter cannot provide estimates with low
noise levels at steady state and at the same time be fast enough to
track the signal during transient behavior. This problem is overcome
by extending the Kalman filter with a change detection
algorithm. While this signal model based method gives a satisfying
result, it is computationally complex. To evaluate its performance, it
is compared to a moving average FIR filter, which is computationally
less expensive but does not succeed as well as the signal model based
method in filtering out all oscillations.
Per Boström
2013
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