Dynamical identification and control of combustion engine exhaust

This paper presents a new approach for model based control of combustion engine exhaust. Fast neural networks of the LOLIMOT-type are used to dynamically simulate different emissions from diesel engines. Neuro-models for the exhaust gases and the fuel consumption are integrated into an upper-level optimization tool. The tool calculates the cost function for exhaust vs. consumption and determines an optimal injection angle dependent on the engine´s exhaust performance, its fuel consumption and the current driving situation