Suspension Parameter Identification Based on Synthetic Data

In this research, we extend a previously validated methodology for identifying vehicle suspension parameters from simpler quarter-vehicle and half-vehicle models to a comprehensive full-vehicle model. This study focuses on the application of synthetic data to perform non-invasive parameter identification, addressing the increased complexity inherent in the full-vehicle model due to its more intricate vertical dynamic equations and a larger set of parameters. Our approach employs a refined local optimization algorithm to ensure precise simulation of vehicle dynamics and accurate parameter identification. The full-vehicle model's complexity presents new challenges, including heightened non-linearity and the impact of different objective functions on the identification process. Our findings highlight the robustness and adaptability of the synthetic data-based methodology while also delineating the limitations encountered in this more demanding scenario. These insights contribute to the ongoing development of efficient and cost-effective solutions for dynamic vehicle parameter identification, essential for predictive maintenance and advanced vehicle design.