Many control approaches for selective catalytic reduction (SCR) systems require knowledge of ammonia storage (NH3 storage) to dose urea accurately. Currently there are no technologies to directly measure internal NH3 storage in a vehicle, so it can only be inferred from hardware sensors located upstream, downstream, or in the catalyst. This paper describes an application of extended Kalman filter (EKF) state estimator used as a virtual sensor for urea injection control of a multi-brick aftertreatment system. The proposed estimator combines mean-value physics-based models of combined SCR and diesel particulate filter (SCR/DPF), SCR and clean-up catalyst (CUC). It uses hardware sensors at the inlet and outlet of the aftertreatment system, and includes no sensors between the catalysts. Performance of the proposed estimator was validated in simulations against a high-fidelity model of the aftertreatment system. The algorithm provides accurate estimates of the dominant gaseous species NOx and NH3 as well as NH3 storage for a feedback model predictive control (MPC) control of urea injection. Moreover, the algorithm is able to estimate upstream NO2/NOx ratio from provided constant reference. The proposed estimator is a link in the model-based control design toolchain aimed for post-EURO 6 RDE-compliant light-duty vehicle design. Together with the MPC controller they are capable of running in real-time on current production hardware.