To reach the emission limits imposed by governments and reduce the negative impact on the environment, the use of aftertreatment systems has become essential for internal combustion engine (ICE) based powertrains. In particular, the selective catalytic reduction (SCR) system is a widespread aftertreatment technology with high efficiency for $\mathrm{N}{\mathrm{O}}_{\mathrm{x}}$ abatement which shows complex dynamics and requires urea injection as reducing agent. Current urea injection strategies usually rely on the $\mathrm{N}{\mathrm{O}}_{\mathrm{x}}$ emissions feedback. This work presents a model for the on-line simultaneous prediction of $\mathrm{N}{\mathrm{O}}_{\mathrm{x}}$ and $\mathrm{N}{\mathrm{H}}_3$ emissions after the SCR catalyst, allowing the emissions estimation even in conditions of urea injector failure, when it is not possible to rely on the injector feedback signal. The proposed model is based on state of the art on-board after-treatment instrumentation and proposes an extended Kalman filter (EKF) to combine a data-based model and the analysis of sensor signals to provide a reliable estimation of $\mathrm{N}{\mathrm{O}}_{\mathrm{x}}$ and $\mathrm{N}{\mathrm{H}}_3$ slip. The proposed strategy is experimentally assessed in dynamic driving cycles, such as Worldwide harmonised Light vehicles Test Cycle (WLTC) and Standardised Random Test (RTS). The proposed method is evaluated in standard conditions (without failures) and with urea injection failures of 25% and 120% of the nominal injection amount. As a result, the prediction on $\mathrm{N}{\mathrm{O}}_{\mathrm{x}}$ and $\mathrm{N}{\mathrm{H}}_3$ slip has been improved in all injection failure conditions, by an overall average of 47.8% and 61.8%, respectively, when compared to state-of-the-art control oriented models (physically based zero dimensional model or data-based).

This work presents the development of a model to capture the NO _x sensors cross sensitivity behavior based on N O x sensor cell temperature, as well as a model do predict the slip of the NO _x and NH ₃ after the SCR catalyst, as a way to reduce the error in the exhaust emissions estimation needed for feedback SCR control strategies. The emissions prediction model is based on the different cross sensitivity behavior of two distinct NO _x sensors. The proposed models were tested and compared on a fully instrumented engine test bench when applied in a Worldwide harmonized Light vehicles Test Cycle (WLTC) and a full map cycle. As a result, the proposed model showed for NO _x sensors cross sensitivity estimation an overall improvement of 34.8% for sensor 1 and 31.0% for sensor 2, and in terms of emissions prediction an overall improvement of 36.3% for NO _x and 45.5% for NH ₃ slip.

This work presents an optimized ammonia injection strategy for the Worldwide Harmonized Light Vehicles Test Cycle and its potential benefits in terms of NO _x emissions and ammonia consumption in selective catalytic reduction. An optimization tool based on optimal control was used to improve the ammonia injection in the selective catalytic reduction with different NO _x emission limits. This optimal control can be used in two ways: one to minimize NO _x emission and another to reduce the ammonia consumption in the selective catalytic reduction. The optimized strategy and the standard ammonia injection strategy were tested and compared on a fully instrumented engine test bench when applied in a Worldwide Harmonized Light Vehicles Test Cycle. The results showed a considerable improvement in the use of the optimization tool. When compared to the standard calibration, the new injection strategy for the same amount of ammonia injection reduced NO _x emissions by 13.7%, and for the same NO _x concentration emissions 33.5% of ammonia consumption was saved.