## 1 Introduction  
Reconfigurable dynamic systems are increasingly prevalent in industries such as robotics, aerospace, and automotive manufacturing, thanks to their ability to adapt to varying operational conditions and structural configurations. However, accurately predicting the dynamic behavior of these systems, especially during the early stages of development, presents significant challenges due to the complex interactions between subsystems. This complexity is particularly challenging in noise, vibration, and harshness (NVH) analysis, where the dynamic interactions between subsystems often lead to non-linear vibrational responses. In applications such as electric vehicles (EVs), the prediction of these responses early is crucial for appropriate target setting and optimizing vehicle performance and mitigating NVH issues. Model reduction techniques based on Frequency-Based Substructuring (FBS) and Proper Orthogonal Decomposition (POD) are increasingly used to improve the efficiency of simulation and control for such complex dynamic systems. While system decomposition through receptance coupling methods focuses on dividing a system into smaller subsystems using FBS, which incorporates the full frequency range of dynamic properties through Frequency Response Functions (FRFs), POD relies on mode truncation, specifically targeting the most significant modes to reduce system complexity. However, a primary challenge with POD is the requirement for complete spatial information from the original model to construct the reduced-order model, which can result in significant computational bottlenecks when applied to large-scale systems [1]. To address these challenges, acceptance coupling methods using FBS have been developed using coupling Frequency Response Functions (FRFs) of individual subsystems. Initially developed for the coupling of rigid subsystems with fewer degrees of freedom, the original algorithms by [PERSON] and [PERSON] have been widely adopted across various engineering domains. By transitioning from the time domain to the frequency domain and employing linear properties, this reduced-order modeling approach offers a practical tool for reducing the size of non-linear dynamic systems. This framework integrates FBS with GRC, introducing a systematic technique that facilitates the reliable prediction of dynamic responses in reconfigurable systems. Through the reduction in computational complexity, this methodology effectively simplifies non-linear behaviors, particularly in cases where the study of high-frequency dynamics is important. This approach has a hybrid feature due to flexibility in leveraging both numerical and experimental data. The proposed approach enhances the modeling capabilities of existing methods, providing a more comprehensive understanding of subsystem interactions and their contributions to overall system behavior. This reduced-order framework is especially valuable in early design stages where limited data are available to develop a detailed model like FEM or MBD. Two case studies validate the effectiveness of the proposed methodology. The first one presents the analytical derivation of acceptance matrix components for a complete vehicle system using a simplified lumped parameter model. The second case study develops a vibrational model for a flexible dynamic structure that can simply be representative of a vehicle's flexible body structure consisting of three main substructures: the front, middle, and rear end structures. This simple configuration is selected for validation with conventional numerical methods, but this method can be expanded to accommodate more complex dynamic systems with modular and reconfigurable configurations. These studies validate the extended methodology and highlight its practical applications in automotive NVH analysis, where accurate modeling of vibration transfer pathways is essential for optimizing vehicle performance.