Optimizing Vehicle Dynamics: A Methodology for Reducing Correlation Gaps

In the highly competitive automotive industry, final design decisions are often made early in the product development cycle. However, physical prototypes may exhibit deviations from nominal specifications due to design tolerances, manufacturing tolerances, stiffness element fitment, and other noise factors. These deviations can lead to significant performance discrepancies (correlation gaps) between real-world performance and the digital twin representation created during the design phase. Repeatedly measuring all system parameters to account for these differences can be both costly and impractical. This research aims to bridge the gap between digital and physical models by identifying key system parameters from system characteristic data generated through controlled dynamic testing. Specifically, dynamic load cases are conducted using a 4-poster test rig, where vehicle responses are recorded at various suspension locations under controlled conditions. By analyzing these target responses and correlating them with the digital twin’s output, an optimal set of digital model parameters is identified. This process enhances the accuracy of digital simulations, ensuring they better represent real-world vehicle behavior. Such an approach improves the reliability of digital twins while minimizing extensive physical validation efforts, thereby refining designs more efficiently. Ultimately, this method contributes to optimizing vehicle dynamics, reducing development costs, and improving overall performance predictions in the early design stages. The findings of this research could be valuable across different vehicle architectures, enabling more precise simulations and informed engineering decisions.