Make informed design decisions early on by quantifying millions of architectures virtually
Architecture analysis, whether it is a powertrain architecture or a cooling system architecture, ensures that the system architectures are aligned with desired requirements and that all the possibilities are thoroughly explored. It is an essential aspect of Model-Based Systems Engineering, (MBSE), an approach where all requirements are captured and converted into a model showing the relationship between function and requirements. In this article, we will explore an architecture analysis technique with generative engineering within the realm of MBSE. We will also showcase a case study of cooling architecture analysis for electric vehicles (EVs) to demonstrate the practical application of these techniques.
The current state of the art in automotive architecture selection often involves a time-consuming and iterative process of evaluating and refining concepts based on past experiences and expert judgment. This process can be subjective, prone to biases, and limited by the knowledge and experiences of the individuals involved. It may also overlook certain trade-offs and system interactions that can significantly impact the overall performance and efficiency of the automotive architecture. As automotive systems become more complex, interconnected, and technologically advanced, there is a growing need for a more systematic and comprehensive approach to concept selection that goes beyond the limitations of the existing state of the art.