Three-way catalyst model development using physical and machine learning methods for engine control design purposes

Gasoline engine control strategies ensure a combustion control around stoichiometry. That is because the three-way catalytic converter allows CO and HC oxidation under lean operating conditions while ensuring NOx reduction for rich mixtures. In case of engine malfunction, the controller must adapt to compensate for potential torque losse and other critical attributes, potentially leading to significant deviation of the fuel-air mixture richness from stoichiometry and larger emission levels. Therefore, during development of the engine fault diagnostics the impact on the pollutant emissions must be considered. In this paper, a model-based development process is proposed. It is based on system simulation modelling techniques, where a complete exhaust line is represented in order to predict tail-pipe emissions under stoichiometric, lean and rich conditions, for engine control design purposes. Two different modelling approaches are applied and evaluated in this paper. First, a physics-based modelling where thermal and chemical aspects of the pollutant conversion phenomena are considered. In this study, the focus is on the chemical reaction’s selection and kinetic parameters calibration. Second, a Machine Learning approach based on Neural Networks to represent the pollutant conversion process and monolith thermal dynamics. Our main contribution to this method is the selection of an optimal neural network architecture and application of a convenient training process. The study successfully resulted in a validated gasoline exhaust line model for both approaches. Simulation results were validated using experimental data with driving cycle tests under stoichiometric, 10% lean and 10% rich operating conditions; the average error over a large set of evaluation cycles is below 6% for the physics-based model and below 3% for the neural network model.