In the automotive industry, increasing noise regulations are influencing product sales and passenger comfort, creating a need for more effective noise testing methods. Hardware-in-Loop (HiL) based virtual acoustic testing serves as a critical step before Driver-in-Loop testing, allowing for the assessment of noise levels inside and outside the vehicle under various conditions before real-world testing is performed. This study addresses the conversion of a physical vehicle model in the AMESim environment into FMU-RT (Linux) format, and the translation of a powertrain control system from Simulink to C++ code. Key challenges in the FMU-RT conversion include the integration of appropriate submodels for each component and enabling the interfaces necessary for SCANeR and virtual acoustic testing. In the C++ code conversion, challenges involved configuring the correct parameters, ensuring the model runs at a fixed time step and sampling time of 1 ms, and resolving issues related to the scheduler, data types, S-function source codes, and joystick interfaces. More than 100 errors were addressed to successfully generate the C++ code. In HiL testing, encrypted versions of the physical and control models are deployed for co-simulation, although HiL testing can be time-consuming and costly. To mitigate these challenges, we propose an integrated model approach for predicting HiL performance, particularly for acoustic noise testing across a range of scenarios, from simple to complex, thus reducing both time and cost. This integrated approach uses a physical vehicle model as FMU (Windows) and a control system as- Simulink-based model, providing access to ICE and EM auto-gear positions, as well as vehicle modes (EV, ICE, and Hybrid). Additionally, the model outputs 42 key variables—6 for SCANeR visualization and 36 for virtual acoustic testing. Validation of the model shows strong correlations with HiL Test Lab RT data, with R² values greater than 90% for 31 variables and 80-90% for 5 variables, demonstrating the efficacy of the integrated model in predicting acoustic noise performance.