# Filtering Process to Optimize the Technical Data of Prototype Race Cars
## 2 Brief Description of the Applied Vehicle Dynamics Model and Simulation Program  
The filtering methodology introduced in this study is applied to the input parameters of a custom-developed vehicle dynamics simulation program tailored for prototype race cars [10]. Those input parameters whose minor variations do not significantly affect the simulation results relevant to the given competition task are filtered out. As a result, only the technical data that meaningfully influence vehicle performance are retained for optimization. We demonstrate the filtering process using our previously developed simulation program tailored for prototype race cars. This simulation program is based on our own vehicle dynamics model, the structure of which is shown in Figure 1. It is important to note that the proposed filtering approach is not limited to this particular model and can be extended to other vehicle dynamics simulation platforms.  
The foundational model, shown in Figure 1, was developed for a single-seater, electrically powered prototype race car featuring a rigid frame and a drivetrain comprising a series-wound DC motor transmitting torque to the rear wheels via a chain drive. It must be remarked that the car has a rigid suspension, so the whole vehicle can be modeled as having a rigid frame structure. This model conceptualizes the vehicle as comprising four primary subsystems: the vehicle body, including the motor housing (stator), the motor rotor, the front and rear wheels, and associated rotating components. The relationship between the motor torque (\(M_{motor}\)) and the torque delivered to the rear wheels (\(M_{wheel}\)) is the following:  
\[M_{wheel}=-\eta\cdot i_{12}\cdot M_{motor} \tag{1}\]  
where  
\[i_{12}=\frac{z_{2}}{z_{1}} \tag{2}\]  
The meaning of the notations in Equations (1) and (2) and in Figure 1 can be found in the Nomenclature.  
Figure 1: The vehicle dynamics model for the race car, together with the forces and torques acting on it [10].  
The modeled race car was powered by a series-wound DC motor. The developed model of this motor [10] is shown in Figure 2.  
Based on the models presented in Figures 1 and 2, a complete vehicle dynamics simulation program was developed in MATLAB Simulink [10]. The modular structure of the simulation program is shown in Figure 3.  
The complete simulation program, developed in MATLAB Simulink, is modular in structure (Figure 3), reflecting the logical organization of the dynamic model. It consists of five discrete modules: the vehicle body, front wheel assembly, rear wheel assembly, motor, and drivetrain. A comprehensive description of each module's functionality and internal architecture can be found in [10]. In Section 3, a drag race is simulated, which is short in duration (~10 s), so thermal effects (e.g., changes in tire pressure due to tire heating) do not cause significant errors in the simulation. The maximum speed reached by the vehicle is not very high (~55 [km/h]), so errors resulting from aerodynamic changes are not significant.  
The inputs of this program and its output functions are listed in the Nomenclature. The input data for the simulation include various vehicle and motor parameters. These may be sourced from the manufacturers' catalogs and the scientific literature or determined through  
Figure 3: The simulation program developed in MATLAB Simulink [10].  
Figure 2: The model of the series-wound DC motor applied for the simulation [10].  
experimental measurement [38; 39; 40; 41; 42; 43; 44]. While certain data may be readily available, others can require custom measurement setups [45]. To address this, a bespoke experimental measurement system was developed and implemented, as described in [46].