Estimating pressure peak position and air-fuel ratio using the ionization current and artificial neural networks

To optimize the performance and reduce the emission levels of an internal combustion engine one needs some measurements of the combustion process duality. We propose two artificial neural network models which use the ionization current for estimation of the position of the pressure peak and the air-fuel ratio. The pressure peak position model produces estimates on a cycle-by-cycle basis for each of the cylinders. These estimates are twice as good as estimates obtained from a linear model. The air-fuel ratio model uses the universal exhaust gas oxygen sensor as reference; it produces estimates that are ten times better than estimates obtained from a linear model