The automotive industry is moving towards software-defined vehicles, where software shapes the car’s features and user experience. Unlike traditional vehicles that rely on multiple separate computing units with tightly coupled software, the new centralized high-performance computers offer a more flexible, software-defined platform. Mapping software to hardware is the process of resource allocation. As the number of software components in software-defined vehicles increases, managing this process with the existing approach becomes increasingly challenging. In this paper, we propose utilizing an automated and model-based approach for analyzing/planning resource allocation during the design phase and implementing a virtualization-based environment using virtual machines and containers for deployment in software-defined vehicles. Our approach enables automated resource allocation, thereby ensuring flexible software deployment and updates in the vehicles. The proposed workflow begins with the formal definition of system information through models and constraint languages. Based on this, an optimization problem is generated and solved automatically. Afterwards, deployment-related code is generated based on the optimization results from the optimization solver and is then transmitted and deployed to the vehicle. We demonstrate our method by setting up a simulated software-defined vehicle platform using an ARM-based automotive reference central computer with Autoware applications.