Theoretical Framework for Developing a Full Vehicle Hybrid Model to Predict Tire Characteristics Using Receptance Coupling Technique

Purpose This paper introduces a theoretical framework for developing an effective hybrid model of a full vehicle to predict tire characteristics. The primary goal is to establish a foundation for employing reduced-order modeling techniques for complex dynamic systems. Methods The framework emphasizes two structured coupling schemes: multi-step and single-step approaches, expanding upon the concept of coupling two subsystems. These methods aim to decompose intricate dynamic systems into integrated subsystems identified by selected points and a limited number of degrees of freedom at interfaces and internal regions. By combining receptance functions of individual disassembled subsystems using the frequency-based substructuring technique, the response of the coupled dynamic system can be predicted. This structured method utilizes a set of explicit equations governing interactions between subsystems, enabling the integration of both experimental and analytical data sources for frequency response functions (FRFs). The FRF encompasses a comprehensive range of substructure properties, including stiffness, mass, and damping, facilitating the development of reduced-order models and ensuring a simpler, more reliable, and collaborative modeling approach. Results The substructuring approach enables the determination of the entire dynamic system receptance matrix and vibrational response based on a minimal number of measurements conducted under decoupled subsystem conditions. This is achieved through a set of explicit equations developed based on compatibility and equilibrium principles at the connection points. Moreover, it provides flexibility to predict the receptance matrix and response at non-accessible points, facilitating the analysis of transferred vibrations through the load path among subsystems compared to traditional methods. Conclusion This theoretical framework facilitates the development of modular, hybrid, and reduced-order models for predicting system responses. By integrating independent subsystems and leveraging the combined receptance functions, it enables the creation of effective vehicle dynamic models for performance prediction, target setting, and target cascading during the early stages of development.