Enhancing Multi-Objective Performance: Optimizing the Efficiency of an Electric Racing Vehicle

This study addresses the multi-objective optimization of an electric racing vehicle’s performance by identifying the optimal combination of battery type, pilot weight, and power mode to maximize travel time and distance while minimizing energy consumption. A sequential methodology integrating Response Surface Methodology (RSM) and the Desirability Function Approach (DFA) was applied using a 2×3×3 factorial experimental design. Performance data were collected through a custom Arduino-based acquisition system and analyzed using first- and second-order models. Results showed that the configuration comprising the smallest battery (1.0), the lowest weight (66 kg), and the lowest power mode (N5) produced the best overall performance, with a maximum operational time of 1.12 h, a travel distance of 24.63 km, and an energy efficiency of 2.90 km/Ah. The statistical models demonstrated strong explanatory power, with adjusted R² values above 0.83 and global p-values below 0.01. Both individual response optimization and global desirability analysis converged on the same optimal settings, validating the robustness of the methodology. The findings offer a replicable framework for optimizing high-efficiency electric vehicles and contribute to the broader development of sustainable transportation technologies.