Optimizing Vehicle Dynamics: A Methodology for Reducing Correlation Gaps

Final design choices are frequently made early in the product development cycle in the fiercely competitive automotive sector. However, because of manufacturing tolerances design tolerances stiffness element fitment and other noise factors physical prototypes might show variations from nominal specifications. Significant performance differences (correlation gaps) between the digital twin representation produced during the design phase and real-world performance may result from these deviations. Measuring every system parameter repeatedly to take these variations into account can be expensive and impractical.

The goal of this study is to identify important system parameters from system characteristic data produced by controlled dynamic testing to close the gap between digital and physical models. Dynamic load cases are carried out with a 4-poster test rig where vehicle responses are captured under controlled circumstances at different suspension locations. An ideal set of digital model parameters is found by examining these target responses and comparing them with the output of the digital twin. This procedure makes digital simulations more accurate and guarantees that they more accurately depict the behavior of actual vehicles.

By reducing the need for extensive physical validation this method increases the dependability of digital twins and facilitates more effective design refinement. In the end this approach helps to improve overall performance predictions in the early stages of design optimize vehicle dynamics and lower development costs. Across various vehicle architectures the research conclusions may prove useful facilitating more accurate simulations and well-informed engineering choices.