Electric vehicles are environmentally friendly and more efficient than conventional combustion vehicles. However, from the point of view of energy vectors, they may use energy produced by less efficient and more polluting means. In this paper, an applicative methodology is used to develop a charging equalizer for an electric vehicle that makes it possible to efficiently use the energy produced by a 350 W photovoltaic panel to intelligently charge the five batteries of the vehicle. In addition, using a quantitative methodology, an analysis of the different physical and electrical parameters obtained by a series of sensors installed in the vehicle is presented, and the efficiency of the system is determined. Different routes were travelled within the city of Cuenca with and without the load equalization system, which made it possible to determine an increase in vehicle efficiency of up to 27.9%, equivalent to an additional travel distance of approximately 14.35 km. This is a promising result, since with small investments in solar panels and electronic materials, the performance of low-cost electric vehicles can be significantly improved.

This research aims at proposing an alternative to improve the efficiency of electric vehicles (EVs) and reduce greenhouse gas (GHG) emissions in the context of electric mobility. A photovoltaic and wind hybrid energy system was installed in a Chok S2 electric vehicle. In addition, a charge equalization system was included to balance and maximize the performance of each of the EV’s five batteries connected in series. The results show a 20% improvement in vehicle efficiency after conducting tests on a 17 km Andean route. The photovoltaic system generated 535 W, while the wind system generated 135 W/s at a speed of 45 km/h. These findings highlight the potential of hybrid renewable energy systems to improve the efficiency and range of electric vehicles.

The development of intelligent technologies for analyzing and classifying agricultural products plays a key role in Agriculture 4.0. This is particularly relevant for tomato crops, where substantial morphological variability and a high susceptibility to mechanical damage call for precise and consistent evaluation methodologies. This study proposes a robotic classification system that integrates computer vision, an XArm6 collaborative robot, a SoftGripper, and an RS D435i camera to enable reliable detection, classification, and damage-free manipulation of tomatoes. The perception module uses an RGB-D camera and a YOLO model specifically trained for tomato detection, enabling precise estimation of the fruit’s three-dimensional position. Ripeness is determined through HSV-space segmentation into four categories, while size is estimated from the apparent diameter combined with depth information. The end-effector, manufactured from Smooth-SIL® 940 food-grade silicone and validated through structural simulations in ANSYS, ensures soft and adaptive manipulation. The system achieved over 95 % accuracy in 8 of the 12 evaluated classes, demonstrating stable detection, classification, and grasping performance. Overall, these results highlight the potential of intelligent systems as an effective and scalable solution for tomato classification in modern agricultural settings.

This research describes the development of an autonomous robotic triage system, carried out by a student through project-based and challenge-based learning methodologies, aimed at solving real-world problems using applied technologies. The system operated in three phases: environment exploration, victim detection through computer vision supported by autonomous navigation, and remote measurement of vital signs. The system incorporated SLAM algorithms for mapping and localization, YOLOv8 pose for human detection and posture estimation, and remote photoplethysmography (rPPG) for contactless vital-sign measurement. This configuration was integrated into a mobile platform (myAGV) equipped with a robotic manipulator (myCobot 280) and tested in scenarios simulating real emergency conditions. All three triage phases defined in this case study were executed continuously and autonomously, enabling navigation in unknown environments, human detection, and accurate positioning in front of victims to measure vital signs without human intervention. Although limitations were identified in low-light environments or in cases of facial obstruction, the modular ROS-based architecture was designed to be adaptable to other mobile platforms, thereby extending its applicability to more demanding scenarios and reinforcing its value as both an educational and technological solution in emergency response contexts.