# Filtering Process to Optimize the Technical Data of Prototype Race Cars
## 3 Brief Description of the Applied Optimization Method  
The parameter optimization process implemented in our software comprises two distinct approaches: a graphical analysis method and an algorithmic technique based on Adaptive Simulated Annealing (ASA) [30]. While a comprehensive exposition of these methods is available in [11], this section provides a concise overview of the ASA-based approach, which plays a pivotal role in the framework.  
The ASA method was realized using MATLAB's Global Optimization Toolbox, specifically the \"simulannealbnd\" function, which is designed to perform bounded simulated annealing. To illustrate its application, we present an example optimization scenario: minimizing the time required for a race car to accelerate from a standstill to a velocity of 40 [km/h] by adjusting the gear ratio of the chain drive (\(i_{12}\)). The first step was to create a MATLAB function that calculates the time required to reach a speed of 40 [km/h], applying different gear ratios. The program code developed for this is shown in Figure 11. Using the above MATLAB code, the program code for optimization with the ASA method is shown in Figure 12 (Figures 11 and 12 can be found in the Appendix A).  
The codes in Figures 11 and 12 determine the optimal gear ratio and the corresponding acceleration time to reach 40 [km/h]. During execution, the program outputs dynamic visual feedback, including (a) the current value of the optimization variable, (b) the calculated time for each tested ratio, (c) the evolution of the minimum time over iterations, and (d) the best solution found to that point--all visualized in Figure 4. Upon completion, the optimal gear ratio (\(i_{12}=4.03\)) and corresponding acceleration time (\(t_{min}=4.64\) [s]) are displayed in the command window.  
Figure 4: MATLAB windows for monitoring the parameter values in real time ((**a**) the current value of the optimization variable, (**b**) the calculated time for each tested ratio, (**c**) the evolution of the minimum time over iterations, and (**d**) the best solution found to that point) [11].  
To emphasize the practical implications of parameter optimization, another use case is presented. In this scenario, the objective is to minimize the time required to complete a 100 m drag race. Again, the chain drive gear ratio is selected as the optimization variable. Figure 5 illustrates the vehicle's distance-time functions resulting from three gear ratio configurations: the optimal value (3.871) and two suboptimal alternatives (3.0 and 5.0). This simulation clearly demonstrates that the optimal ratio yields a time advantage of approximately 0.3 s, a margin that can be decisive in competitive racing.  
As previously discussed, it is neither necessary nor efficient to optimize all vehicle parameters indiscriminately. Many variables exhibit minimal influence on performance outcomes in specific competition contexts. Therefore, identifying the subset of influential parameters is essential. To this end, we introduce a systematic filtering process, described in detail in the following section, which determines which variables merit inclusion in the optimization process.