Verification and validation (V&V) are essential stages in the design cycle of automotive controllers to remove the gap between the designed and implemented controller. In this paper, an early model-based methodology is proposed to reduce the V&V time and improve the robustness of the designed controllers. The application of the proposed methodology is demonstrated on a cold start emission control problem in a midsize passenger car. A nonlinear reduced order model-based controller based on singular perturbation approximation (SPA) is designed to reduce cold start hydrocarbon (HC) emissions from a spark ignition (SI) combustion engine. A model-based simulation platform is created to verify the controller robustness against sampling, quantization, and fixed-point arithmetic imprecision. In addition, the results from early model-based verification are used to identify and remove sources of errors causing propagation of numerical imprecision in the controller structure. Thus the structure of the controller is modified to avoid or to reduce the level of numerical noise in the controller design. The performance of the final modified controller is validated in real-time by testing the control algorithm on a real engine control unit. The validation results indicate the modified controller is 17–63% more robust to different implementation imprecision while it requires lower implementation cost. The proposed methodology from this paper is expected to reduce typical V&V efforts in the development of automotive controllers.