The automobile industry strives to develop high-quality vehicles quickly that fulfill the buyer’s needs and stand out within the competition. Full utilization of simulation and Computer-Aided Engineering (CAE) tools can empower quick assessment of different vehicle concepts and setups without building physical models.
This research focuses on optimizing vehicle ride and handling performance by utilizing a tuning specifications range. Traditional approaches to refining these aspects involve extensive physical testing, which consumes both time and resources. In contrast, our study introduces a novel methodology leveraging virtual Subjective Rating through driving simulators. This approach aims to significantly reduce tuning time and costs, consequently streamlining overall development expenditures.
The core objective is to enhance vehicle ride and handling dynamics, ensuring a superior driving experience for end-users. By meticulously defining and implementing tuning specifications, we aim to fine-tune various parameters affecting performance, including suspension settings and overall vehicle dynamics.
In this study, we optimized tuning specifications using advanced software such as Adams/Car and ModeFrontier. Design of experiments and optimizations were conducted in modeFRONTIER. Utilizing a Multiple-Criteria Decision Making (MCDM) approach, we selected the best performance iteration for ride and handling from the optimized results.
A primary advantage of our proposed methodology is the utilization of driving simulators for optimized results, extracted from the Multiple-Criteria Decision Making (MCDM) approach. After finalizing the agreement between the driving simulator and CAE results, we implemented them in the physical vehicle. By employing advanced driving simulators to simulate real-world driving conditions and collect subjective feedback on vehicle performance, we can expedite the tuning process and reduce the need for physical prototypes, leading to substantial cost savings.