The paper provides an overview of a developed methodology and a toolchain for modeling and control of a complex aftertreatment system for passenger cars. The primary objective of this work is to show how the use of this methodology allows to streamline the development process and to reduce the development time thanks to a model based semi-automatic control design methodology combined with piece-wise optimal control. Major improvements in passenger car tailpipe NOx removal need to be achieved to fulfil the upcoming post EURO 6 norms and Real Driving Emissions (RDE) limits. Multi-brick systems employing combinations of multiple Selective Catalytic Reduction (SCR) catalysts with an Ammonia Oxidation Catalysts, known also as Ammonia Clean-Up Catalyst (CUC), are proposed to cover operation over a wide temperature range. However, control of multi-brick systems is complex due to lack of available sensors in the production configurations. Advanced control and inferential sensing techniques can address this complexity, making the control design task more straight forward and less error prone when compared to traditional control design approach. This paper shows an application of Model Predictive Control (MPC) to SCR multi-brick system. The key components of the control strategy are the following: system model including real-time observer, ammonia storage controller and efficiency controller. The system observer is implemented as extended Kalman filter (EKF) and both controllers in the cascade are implemented as MPC. Well established methodology supported by a toolchain is an enabler for minimizing the development time and to simplify the control design process.