Automotive control applications are very challenging due to the presence of constraints, nonlinearities and the restricted amount of computation time and embedded facilities. Nevertheless, the need for optimal trade-off and efficient coupling between the available constrained actuators makes Nonlinear Model Predictive Control (NMPC) conceptually appealing. From a practical point of view however, this control strategy, at least in its basic form, involves heavy computations that are often incompatible with fast and embedded applications. Addressing this issue is becoming an active research topics in the worldwide NMPC community. The recent years witnessed an increasing amount of dedicated theories, implementation hints and software. The Control Parametrization Approach (CPA) is one option to address the problem. The present chapter positions this approach in the layout of existing alternatives, underlines its advantages and weaknesses. Moreover, its efficiency is shown through two real-world examples from the automotive industry, namely: the control of a diesel engine air path; and the Automated Manual Transmission (AMT)-control problem. In the first example, the CPA is applied to the BMW M47TUE Diesel engine available at Johannes Kepler University, Linz while in the second, a real world Smart hybrid demo car available at IFP is used. It is shown that for both examples, a suitably designed CPA can be used to solve the corresponding constrained problem while requiring few milliseconds of computation time per sampling period.