Filtering Process to Optimize the Technical Data of Prototype Race Cars

At the Faculty of Engineering, University of Debrecen, we have long been engaged in the design and development of self-constructed, predominantly electric, single-seat prototype race cars. To enhance the efficiency of both vehicle design and competitive performance, the authors previously developed a modular technical data optimization software. This tool comprises two key modules: a vehicle dynamics simulation program that derives driving dynamics from technical specifications (parameters) and an optimization module that fine-tunes these parameters for various racing scenarios. However, the large number of input variables often renders the optimization process computationally intensive and time-consuming. To address this challenge, we introduce a novel filtering process designed to streamline the optimization process. This method systematically identifies and excludes parameters whose uncertainties exert minimal influence on the simulation outcomes. This approach significantly reduces computational overhead, thereby accelerating the optimization process.