Hydro Bushing Model Identification using Physics-Informed Neural Networks

Advances in computer aided engineering and numerical methods have made modeling and analyzing vehicle dynamics a key part of vehicle design. Over time, many tools have been developed to model different vehicle components and subsystems, enabling faster and more efficient simulations. Some of these tools use simplified mathematical models to achieve the desired performance. These models depend on model identification methods to determine the parameters and structure that best represent a system based on observed data. This work focuses on the development of a model identification for hydro bushings, a crucial component in nearly all ground vehicles. It introduces an innovative approach to identifying the dynamic properties of hydro bushings using the rapidly evolving physics-informed neural networks. The developed physics-informed network incorporates physical laws into its training process, allowing for an improved mapping of a hydro bushing’s excitation to its dynamic response. The trained model not only accurately predicts the dynamic properties of the bushings within the experimentally measured range but also successfully extrapolates its predictions on frequency values beyond the measured range.