Decision Making and Visualization Tool for Automotive Vehicle Setup

Digital twins have gained popularity throughout the years as a step towards reduced prototype concepts and digitalization in many application domains. One of the domains that benefit heavily from this capability is the automotive industry, and particularly motorsports, where physical testing of the different designs, car setups, and track scenarios can be expensive. The difficulty in testing several combinations of vehicle designs and configurations is impacting the effectiveness of decision-making and preventing engineers from making well informed design and vehicle setup decisions. Additionally, the large amounts of experimental data previously recorded can be leveraged in developing and calibrating the digital models and simulations to ensure their accuracy. In this work, a parametric decision-making environment is developed and is enabled by surrogate models of vehicle kinematic tests. This decision-making capability is supporting vehicle setup and tuning of the suspension system through calculating and visualizing the kinematic test results of the suspension system for different suspension parameter inputs. As an example, the performance of the suspension system is visualized through animation of the suspension model for the different test cases being explored. This study is demonstrating a use case for virtual testing of vehicle setup options and the tuning of the suspension system for the desired performance.