The current study shows an empirical analysis to establish the effects of the variations in compressor discharge pressure on the drying performance of aromatic herbs, in terms of the coefficient of performance (CoP), moisture content (MC), specific moisture extraction rate (SMER), drying temperature, drying time and energy consumption. In conducting the research, a heat pump drying system was utilized as a mechanism for dehydrating herbs, seeds, and fruits. It was used thanks to its benefits like higher efficiency and its low power consumption. Three levels of discharge pressure were considered, 1380 kPa, 1100 kPa, and 827 kPa, using 1,1,1,2-tetrafluoroethane (R134a) as a refrigerant and oregano leaves as the main product. The findings show that, concerning the same oregano moisture sample, the lower the compressor discharge pressure, the lower drying temperature, also, the higher drying time was obtained. Despite the fact that the CoP decreased with the compressor discharge pressure, in comparison with the baseline case, it remained essentially the same for the other two cases.

The rapid development of Intelligent Connected Vehicles (ICVs) and the Internet of Vehicles (IoV) has paved the way for new real-time monitoring and control systems. However, most existing telemetry solutions remain limited by high costs, reliance on cellular networks, lack of modularity, and insufficient field validation in competitive scenarios. To address this gap, this study presents the design, implementation, and real-world validation of a low-cost telemetry platform for electric race vehicles. The system integrates an ESP32-based data acquisition unit, LoRaWAN long-range communication, and real-time visualization via Node-RED on a Raspberry Pi gateway. The platform supports multiple sensors (voltage, current, temperature, Global Positioning System (GPS), speed) and uses a FreeRTOS multi-core architecture for efficient task distribution and consistent data sampling. Field testing was conducted during Colombia’s 2024 National Electric Drive Vehicle Competition (CNVTE), under actual race conditions. The telemetry system achieved sensor accuracy exceeding 95%, stable LoRa transmission with low latency, and consistent performance throughout the competition. Notably, teams using the system reported up to 12% improvements in energy efficiency compared to baseline trials, confirming the system’s technical feasibility and operational impact under real race conditions. This work contributes to the advancement of IoV research by providing a modular, replicable, and cost-effective telemetry architecture, field-validated for use in high-performance electric vehicles. The architecture generalizes to urban e-mobility fleets for energy-aware routing, predictive maintenance, and safety monitoring.

The link between the world of communications and the world of racing is provided by the telemetry systems in electric racing cars. These systems send real-time data about the vehicle’s behavior and systems to enable informed decisions during the race. The objective of this research was to integrate telemetry into the battery bank of an electric racing car in order to find the optimal values of current and voltage that optimize the charging process and thus improve the performance of the vehicle in competition using Response Surface Analysis. Specifically, the telemetry system consisted of an Arduino Mega, a digital wattmeter, and temperature sensors, all installed in the vehicle. Once the telemetry data were obtained, a response surface design was fitted with current, voltage, and temperature as factors varying from low to high values, with the objective function being to minimize the battery charging time. Using the response surface methodology and the steepest descent algorithm, it was found that all factors significantly affect the charging time, with the minimum charging time being 6961 s, obtained with a current of 2.4 amps and voltages of 50.5 volts and 43.6 volts.

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 rapid development of Intelligent Connected Vehicles (ICVs) and the Internet of Vehicles (IoV) has paved the way for new real-time monitoring and control systems. This study presents the design, implementation, and validation of a telemetry solution applied to electric race vehicles, integrating ESP32-based data acquisition with LoRaWAN communication and real-time visualization via Node-RED. The system was deployed and tested in Colombia’s National Electric Drive Vehicle Competition, demonstrating high sensor accuracy (>95\%), low transmission latency, and robustness under real-world racing conditions. Results revealed a 12\% improvement in energy efficiency compared to previous competitions, highlighting the system’s potential for enhancing driving strategies. This implementation contributes to the advancement of IoV technologies in high-performance electric vehicles, offering a scalable architecture for intelligent transportation systems.