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 vehicle performance and noise levels inside and outside the vehicle under various conditions before physical prototype testing is performed.
The Hardware-in-the-Loop (HiL) simulator setup is equipped with joystick control that requires a physical representation of the vehicle dynamics model provided as a Functional Mock-up Unit (FMU) in real-time format. In contrast, the vehicle control logic is implemented in C++ code. The simulator incorporates both lateral and longitudinal dynamics. Additional interfaces are integrated to support joystick input and virtual road visualization enabling realistic vehicle maneuvering and dynamic performance evaluation.
However, performing all test protocols directly on the HiL setup can be time-consuming and costly. To address this limitation of full HiL testing, in this study, an offline Software-in-the-Loop (SiL) Co-simulation framework was developed as an alternative. This method replicates the HiL environment within MATLAB/Simulink, where joystick actions are simulated according to predefined driving protocols. The dynamic behavior of the vehicle during a reverse driving protocol, involving a 540° constant steering angle and 0–100% acceleration pedal input, was analyzed and compared between Offline SiL and HiL environments.
Results demonstrated that 85% of key parameters exhibited strong correlation (R2 > 0.9), confirming that the offline SiL-based approach effectively replicates HiL performance. The remaining parameters also showed acceptable consistency. These findings indicate that the proposed Offline Co-simulation method is a promising, cost-effective, and scalable alternative for accurately predicting vehicle dynamic behavior, aligning well with current automotive industry needs for early-stage validation and optimization.