Model Based Calibration Generation for Gasoline Particulate Filter Regeneration

Gasoline Particulate Filters (GPF) are widely employed in exhaust aftertreatment systems of gasoline engines to meet the stringent particulate emissions requirements of Euro 6 and China 6 standard. Optimization of GPF performance requires a delicate trade-off between fuel economy, engine performance and drivability. This results in a complex lengthy and iterative calibration development process which uses a lot of hardware resources. To improve the calibration process and reduce hardware testing, physics-based modeling of the GPF system is used. A 1-D chemical model supplemented with 3D CFD solver is utilized to evaluate pressure drop and soot burning performance characteristics of the GPF under engine dynamometer test conditions. The chemical kinetics of soot burning for the 1D model is developed using test data obtained from well controlled laboratory environment. Later, the model was applied to engine dynamometer conditions where it demonstrated robust pressure drop and soot burning predictions against test data under various exhaust flow rate, temperature and soot load conditions. The validation of this 1-D kinetic model enabled us to come up with a new method for the generation of optimized calibration maps for GPF control system in the vehicle. Compared to the conventional calibration method, the new physics-based calibration generation method uses less test data. This results in faster calibration development process at a lower cost as dependency on complicated testing in engine dynamometer and development vehicles is reduced.