In the recent years, engine developer attention is focused on the application of advanced control strategies for the SCR system, considered as the leading technology for the abatement of NOX emissions in Diesel engines. The most challenging task in SCR control is the optimization of the urea dosing strategy while reaching the balance between high NOX reduction efficiency and low NH3 slip in the tailpipe, especially during transients. In order to accomplish this task, an Extended Kalman Filter (EKF) estimator is developed, to be integrated in the closed loop control of SCR urea dosing. The EKF estimator is applied to a one-state model, in which the ammonia coverage ratio θ is the only dynamic process taken into account. To improve the model accuracy, an additional dynamics, related to total ammonia adsorption capacity ω, is considered while conceiving the EKF. The recursive EKF algorithm allows achieving enhanced estimation accuracy with a slight increase of the computational burden that is still suitable with on-board application.
Development of an EKF Observer for an automotive SCR system
Arsie I.;
2019-01-01
Abstract
In the recent years, engine developer attention is focused on the application of advanced control strategies for the SCR system, considered as the leading technology for the abatement of NOX emissions in Diesel engines. The most challenging task in SCR control is the optimization of the urea dosing strategy while reaching the balance between high NOX reduction efficiency and low NH3 slip in the tailpipe, especially during transients. In order to accomplish this task, an Extended Kalman Filter (EKF) estimator is developed, to be integrated in the closed loop control of SCR urea dosing. The EKF estimator is applied to a one-state model, in which the ammonia coverage ratio θ is the only dynamic process taken into account. To improve the model accuracy, an additional dynamics, related to total ammonia adsorption capacity ω, is considered while conceiving the EKF. The recursive EKF algorithm allows achieving enhanced estimation accuracy with a slight increase of the computational burden that is still suitable with on-board application.I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.
