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).

为了达到政府规定的排放限值并减少对环境的负面影响，后处理系统的使用对于基于内燃机（ICE）的动力总成系统已变得至关重要。特别地，选择性催化还原（SCR）系统是一种广泛的后处理技术，对于$\mathrm{ñ}{\mathrm{Ø}}_{\mathrm{X}}$减排，它显示出复杂的动力学特性，需要注入尿素作为还原剂。当前的尿素注射策略通常依赖于$\mathrm{ñ}{\mathrm{Ø}}_{\mathrm{X}}$排放反馈。这项工作提出了一个在线同步预测模型$\mathrm{ñ}{\mathrm{Ø}}_{\mathrm{X}}$ 和 $\mathrm{ñ}{\mathrm{H}}_3$SCR催化器之后的废气排放，即使在尿素喷油器故障的情况下，也无法依靠喷油器反馈信号进行排放估算。所提出的模型基于先进的车载后处理仪器，并提出了扩展的卡尔曼滤波器（EKF），以结合基于数据的模型和传感器信号的分析以提供可靠的估计。$\mathrm{ñ}{\mathrm{Ø}}_{\mathrm{X}}$ 和 $\mathrm{ñ}{\mathrm{H}}_3$滑。拟议的策略是在动态驾驶循环中进行实验评估的，例如全球统一的轻型车辆测试循环（WLTC）和标准化随机测试（RTS）。在标准条件下（无故障）且尿素喷射失败率为标称喷射量的25％和120％时，对提出的方法进行了评估。结果，对$\mathrm{ñ}{\mathrm{Ø}}_{\mathrm{X}}$ 和 $\mathrm{ñ}{\mathrm{H}}_3$ 与最新的面向控制的模型（基于物理的零维模型或基于数据的模型）相比，在所有注入故障条件下的滑移率分别提高了47.8％和61.8％，平均总滑坡率得到了改善。