A Neural Estimator for Cylinder Pressure and Engine Torque

The paper presents a new method based on neural networks to model the dynamic behavior of combustion pressure in SI engine cylinders, represented only by conventional input-output data. The approach is based on a functional representation of the pressure curve. The function parameters are adjusted by training a static neural network (SNN) for each working cycle. These parameters resp. “weights” are used in the following as reference pressure feature sequences. The sequences are simulated using time delay neural network (TDNN) as functions of engine speed, manifold pressure, ignition time and A/F ratio. The developed models can be used as stand alone models or as submodels within a global structure. It can be integrated as a real-time model in a HIL simulator to stimulate an ECU or implemented within an ECU for torque estimation. Performance of the proposed modeling strategy is verified by comparing experimental data from a test bench to real-time simulation results.