Este artículo presenta las etapas del diseño y construcción del chasis de un vehículo eléctrico. Se inicia con el diseño conceptual, donde se definieron la geometría y el material utilizado; posteriormente, se procedió a realizar simulaciones mediante el software paramétrico de elementos finitos (FEM) Solidworks, con el objetivo de establecer las condiciones de frontera, carga y contactos, para finalmente realizar la planimetría de las piezas y su fabricación. Se analizó el comportamiento mecánico bajo una carga de 1500 N en el frente, posterior y lateral del chasis. En la zona frontal, se alcanzó un valor de 80.93 MPa, mientras que el límite del material está alrededor de 268 MPa para el material ASTM 500 grado B que fue utilizado. En cuanto a la deformación que sufre el chasis, se alcanzó un valor máximo de 0.29 mm, garantizando que la jaula de seguridad del chasis mantiene la integridad del piloto, considerando que las velocidades alcanzadas por este tipo de vehículo no superan los 60 Km/h. Finalmente, se generó la información técnica (planimetría), con el fin de continuar con la etapa de producción y construcción. El artículo es el resultado del trabajo interdisciplinario de estudiantes y profesores de diferentes ramas de la ingeniería: mecánica, eléctrica, electrónica y automotriz de la Institución Universitaria Pascual Bravo, y forma parte de las iniciativas para incentivar la movilidad eléctrica a través del diseño y fabricación de un vehículo para la 2da Competencia Nacional de Vehículos de Tracción Eléctrica (IICNVTE).

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.

The multi-objective optimization of an electric prototype racing vehicle is addressed in this study. The goal was to identify the optimal combination of battery type, pilot weight, and power mode to maximize operational time and distance while minimizing energy consumption. A structured $2\times 3\times 3$ factorial design was implemented, and the resulting data were analyzed through Response Surface Methodology (RSM) in combination with the Desirability Function Approach (DFA). The experimental design included two battery configurations, three weight levels, and three power settings, while data acquisition was performed through a custom Arduino-based system validated against commercial instruments. The results revealed that the configuration with the smallest battery, the lowest weight (66 kg), and the lowest power mode (N5) achieved the most efficient performance, yielding an operating time of 1.12 h, a travel distance of 24.63 km, and an energy performance index of 2.90 km/Ah. The integration of RSM with DFA provided a robust framework for identifying optimal multiparameter conditions under competition constraints. Unlike previous studies that examined these variables in isolation, this work advances the state of the art by demonstrating the feasibility of multiparameter optimization in real-world racing contexts, offering methodological and practical insights for sustainable electric mobility.

Surface texturing is a well-known method used in components like journal bearings to enhance load capacity and reduce friction. This study investigates how the placement of the texturized areas affects the performance of journal bearings. By employing the Reynolds equation for lubrication theory, numerical simulations are conducted on journal bearings with spherical texture elements. A comprehensive experimental design investigates the influence of the textures’ positioning and surface area. According to the numerical results, the positioning of the textures plays a crucial role in determining the eccentricity ratio, with certain positions producing more pronounced changes. Interestingly, there is no straightforward connection between the eccentricity ratio and attitude angle, which highlights the importance of precise texture positioning for most effective lubrication. Additionally, certain locations boost the minimum film thickness substantially, leading to improved hydrodynamic lubrication. These findings emphasize the significance of careful texture placement for enhancing journal bearing performance, particularly in managing friction and load under various operational conditions.