Modeling and identification of the combustion pressure process in internal combustion engines

Abstract

We present a new model relating cylinder combustion pressure to crankshaft angular velocity in an internal combustion engine. There are three aspects to this model. First, by changing the independent variable from time to crankshaft angle, a non-linear differential equation model becomes a linear first-order differential equation. Second, a new stochastic model for combustion pressure uses the sum of a deterministic waveform and a raised cosine window amplitude-modulated by a Bernoulli-Gaussian random sequence, parametrising the pressure by the sample modulating sequence. This results in a state equation for the square of angular velocity sampled every combustion, with the modulating sequence as input. Third, the inverse problem of reconstructing pressure from noisy angular velocity measurements can now be formulated as a state-space deconvolution problem, and solved using a Kalman-filter-based deconvolution algorithm. Simulation results show that the parametrised pressure can be deconvolved at low noise levels, and combustion misfires detected, all in real time. Supporting experimental results are referenced in companion papers.