# Filtering Process to Optimize the Technical Data of Prototype Race Cars
## 1 Introduction  
The Faculty of Engineering at the University of Debrecen has a longstanding tradition in designing and constructing custom-built, predominantly electric, single-seat prototype race cars [1, 2]. These vehicles have successfully competed in various national and international student engineering competitions, such as the Shell-ECO Marathon [3], the MVM Energy Race organized by Hungarian Electricity Works Ltd., and the Pneumobile competition [4], achieving numerous podium finishes. Building on this foundation, a new initiative has been launched to further expand our racing activities by developing purpose-built electric race cars to compete in both the Hungarian National Slamom Championship and the internationally renowned Formula Student competition. Competitiveness in motorsport is highly dependent on tailoring a race car's technical parameters to the specific demands of each competition. For example, slamom events require intense acceleration and deceleration between 0-100 km/h, as well as superior cornering performance. Achieving such capabilities necessitates the use of vehicle dynamics simulations and parameter optimization. Numerous studies have addressed vehicle dynamics simulation in various contexts [5, 6, 7, 8], often employing advanced simulation environments such as MATLAB/Simulink (R2022b) [9]. These works explore topics, including land vehicle modeling with active safety and four-wheel drive systems [5], longitudinal dynamics of automatic transmission systems [6], powertrain modeling and development for electric vehicles [7], and energy consumption analysis under varying vehicle parameters [8]. Optimization techniques are alsowidely used in various engineering fields [10; 11; 12; 13] to refine performance parameters. These include classical and intelligent algorithms such as Fuzzy Logic [14; 15], Adaptive Neuro-Fuzzy Inference Systems [16; 17], Taguchi Methods [18; 19], Grey System Theory [20; 21], Teaching-Learning-Based Optimization [22; 23], Genetic Algorithms [24], Particle Swarm Optimization [25], Tabu Search [26], and Simulated Annealing [27; 28; 29; 30]. There are many publications available on optimization with the above-mentioned algorithms related to the field of the vehicle industry. For example, the publications [31; 32] use a genetic algorithm for optimization, with which the vehicle's driving dynamics characteristics (e.g., acceleration) and other characteristics (fuel consumption, emissions) are optimized. Reference [33] applies an evolutionary algorithm to solve a multi-objective optimization problem regarding vehicle powertrain cost and energy consumption. In [34], the particle swarm algorithm is used for a different type of application, namely, the optimization of vehicle fleets. In publications [35; 36; 37], the simulated annealing algorithm is used for optimization. In [35], the powertrain of electric vehicles is optimized in terms of grade ability and range. The publication [37] presents the optimization of the vibration damping of a vehicle's drivetrain. In [36], the attachment of a vehicle's engine and powertrain to the chassis is optimized. To support our diverse vehicle platforms and competition goals, a specialized modular software system was developed in-house [10; 11]. This system integrates a vehicle dynamics simulation module [10] that generates the car's performance curves from input technical parameters and an optimization module [11] that allows for task-specific tuning of these parameters. Its modular design enables flexible adaptation to various vehicle architectures, including electric, pneumatic, and internal combustion drivetrains. This modular architecture is particularly advantageous given the substantial variation in vehicle designs--for instance, powertrains may be electric, pneumatic, or internal combustion-based. Without such modularity, adapting the simulation framework to accommodate different drivetrain configurations would necessitate extensive reprogramming, making the development process significantly more labor-intensive.