This analysis compares the energy efficiency and operational costs of combustion vehicles (Hyundai Accent 1.6 L and Chevrolet Sail 1.5 L) with the Nissan Leaf, an electric vehicle, under current fuel and electricity pricing in Ecuador. Combustion vehicles, converting gasoline into mechanical energy, demonstrate substantial energy losses, leading to higher operational costs, especially with recent gasoline price hikes to USD 2.722 per gallon. In stark contrast, the Nissan Leaf exhibits significantly greater energy efficiency, consuming only 15–20 kWh per 100 km, which translates to lower running costs (USD 11.20 to fully charge a 40 kWh battery). Despite the clear economic and environmental benefits of electric vehicles, their adoption in Ecuador is hampered by geographical challenges such as diverse terrain that can affect vehicle range and battery longevity. Moreover, the limited and uneven distribution of EV charging stations, mostly concentrated in urban areas, poses significant barriers. For broader implementation, a strategic expansion of the EV infrastructure and careful consideration of the national energy grid’s capacity to support increased electric vehicle uptake are essential. Addressing these challenges is crucial for realizing the full potential of electric vehicles in enhancing Ecuador’s sustainability and energy independence.

The efficiency of electric motors is highly dependent on their operating temperature, with lower temperatures contributing to enhanced performance, reliability, and extended service life. This study presents a comprehensive review of state-of-the-art cooling technologies and evaluates their impact on the thermal behavior of a commercial motor–generator system in high-demand applications. A baseline model of a permanent magnet synchronous motor (PMSM) was developed using MotorCAD 2023^® software, which was supported by reverse engineering techniques to accurately replicate the motor’s physical and thermal characteristics. Subsequently, multiple cooling strategies were simulated under consistent operating conditions to assess their effectiveness. These strategies include conventional axial water jackets as well as advanced oil-based methods such as shaft cooling and direct oil spray to the windings. The integration of these systems in hybrid configurations was also explored to maximize thermal efficiency. Simulation results reveal that hybrid cooling significantly reduces the temperature of critical components such as stator windings and permanent magnets. This reduction in thermal stress improves current efficiency, power output, and torque capacity, enabling reliable motor operation across a broader range of speeds and under sustained high-load conditions. The findings highlight the effectiveness of hybrid cooling systems in optimizing both thermal management and operational performance of electric machines.

This research analyzes the emissions of oxides of nitrogen, NOx, emitted by an internal combustion engine, indirect injection multipoint Renault 1.8 L and its behavior in function of the values of lambda to suggest strategies of control of the oxygen content, with a better Functional sampling at the time, to the exhaust of the engine in order to mitigate the production of these toxic emissions. The analysis of the production of these emissions was done experimentally in an engine dynamometer where the engine was subjected to different conditions of load and revolutions to find the habitual behavior of a motor of a light vehicle before the information obtained by the Electronic Control Module From the monitoring performed by a conventional high and low oxygen probe. The study aims to focus the incidence of this type of monitoring on the production of NOx and to propose alternatives that offer a greater range of measurements offering advantages from the environmental point of view to a compound that has a high degree of toxicity and affectation on the respiratory system causing a significant increase in cases of lung cancer. Index Terms— NOx, lambda, dynamometer, oxygen sensor, ignition advance

This study presents a novel methodology for optimizing energy management strategies in heavy-duty Fuel Cell Hybrid Electric Vehicles (FCHEVs) with pantograph charging systems. The approach integrates machine learning (ML) techniques to predict energy demand, optimize the power distribution between the battery and fuel cell, and enhance overall efficiency. The methodology involves clustering vehicle and road data, supervised ML classification, and zonification of routes for adaptive energy management. The proposed system was validated using real-world driving data from five different routes in Germany. The results indicate a significant improvement in hydrogen consumption and fuel cell degradation compared to conventional control strategies. This research establishes a framework for advanced energy management in heavy-duty hydrogen-powered electric vehicles.

The study proposed to analyze the combined driving cycles and their effect on fuel consumption within the Distrito Metropolitano de Quito. For this, a test protocol was established where the orography of the land, traffic density and road infrastructure of the District were studied. These factors allowed the evaluation of consumption through a global positioning system and a canister in order to systematically travel a set of roads in distance, time, and average speed in a vehicle with an Otto cycle engine. The data was processed through a statistical analysis of minimization of weighted averages. The findings revealed that the combined driving cycle obtained a high reliability (99.7%) in each procedure. The outcome of the study concluded that the combined cycle was differential to that proposed by the manufacturer, since there is a variation of 40% as proposed by the vehicle manufacturer.

La contaminación vehicular constituye un factor determinante en la degradación ambiental y la afectación a la salud pública, por lo que la medición precisa de emisiones gaseosas es esencial en la Revisión Técnica Vehicular (RTV). Este estudio comparó el desempeño de dos analizadores de gases, uno homologado y otro no homologado, utilizados en un Centro de Revisión Técnica Vehicular (CRTV) en Ecuador. Se evaluaron las concentraciones de CO, CO₂, O₂ y HC en un total de 242 vehículos con motor a gasolina, bajo condiciones de ralentí y 2500 revoluciones por minuto. Se aplicaron pruebas estadísticas para determinar diferencias significativas entre los equipos. Los resultados evidenciaron discrepancias marcadas en las mediciones de CO, CO₂ y O₂, mientras que para HC las lecturas fueron comparables. El analizador no homologado subestimó el CO en ralentí en un 27,8 %, y sobrestimó el CO₂ en un 18,4 % bajo ambas condiciones. Las mediciones de O₂ presentaron una desviación estándar tres veces mayor respecto al equipo homologado (±0,71 % vs. ±0,23 %), indicando una alta variabilidad. Estos hallazgos destacan la necesidad de utilizar exclusivamente equipos homologados para garantizar la validez técnica de las inspecciones vehiculares. La investigación aporta evidencia empírica que respalda el fortalecimiento de la normativa técnica en Ecuador, promoviendo procedimientos de control ambiental más precisos, transparentes y confiables.