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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


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