The study of lubricating oil is paramount for the optimal functioning of modern engines, and it has generated intensive research in the automotive industry. The aim is to improve the tribological properties of lubricants by including nanomaterials as additives in base oils. This article presents an exhaustive bibliographic review of the experiments carried out to optimize the tribological properties of nano-lubricants in order to identify the nanoparticles and experimental processes used and analyze the results obtained. The methodology adopted combines inductive and deductive elements. It begins with the formulation of a general theory on the application of nanoparticles in lubricants, followed by the collection of specific data on the conceptualization and preparation of nano-lubricants. A total of 176 articles focused on the application of nanoparticles in lubricants, especially to reduce the coefficient of friction, are reviewed. These works, with impact levels Q1 and Q2, delve into the application and are analyzed to review the obtained results. Most researchers worked with a nanoparticle concentration range of 0% to 1% by volume.

The article studies the friction coefficient in elastohydrodynamic lubrication (EHL) by means of analytically obtained equations for different contact geometries. The introduction of some simplifications allows for the simultaneous consideration of piezoviscous, pseudoplastic and thermal phenomena, resulting in complete and realistic models, which provide results in a quick and easy manner. The predictive potential of this analytical approach is analyzed by comparing the estimates of friction with full-EHL simulations and experimental data under different operating conditions. The results obtained allow us to discuss the influence of some assumptions taken into account and the scope of applicability of the models, in order to determine their usefulness and limitations.

The automotive industry faces increasing challenges due to fuel scarcity and pollutant emissions, necessitating the implementation of strategies that optimize engine performance while minimizing the environmental impact. This study aimed to analyze the influence of oil viscosity and fuel quality on the engine performance and pollutant emissions in an internal combustion engine. A Response Surface Methodology (RSM)-based experimental design was employed. Three oil viscosity levels (SAE 5W-30, 10W-30, and 20W-50) and three fuel quality levels (87, 92, and 95 octane) were evaluated using a Chevrolet Grand Vitara 2.0L (General Motors, Quito, Ecuador) tested on a dynamometer. The oil grades were selected to represent a practical range of viscosities commonly used in commercial vehicles operating under local conditions. The results indicate that using lower-viscosity oil (SAE 5W-30) increased the engine power by up to 6.25% compared to when using SAE 20W-50. Additionally, using higher-octane fuel led to an average power increase of 1.49%, attributed to improved combustion stability and the ability to operate at a more advanced ignition timing without knocking. The emissions analysis revealed that high-viscosity oil at high RPMs increased CO₂ emissions to 14.4% vol, whereas low-viscosity oil at low RPMs reduced CO₂ emissions to 13.4% vol. Statistical analysis confirmed that the engine speed (RPM) was the most influential factor in emissions (F = 163.11 and p < 0.0001 for CO₂; F = 247.02 and p < 0.0001 for NOx), while fuel quality also played a significant role. These findings suggest that optimizing the oil viscosity and selecting the appropriate fuel can enhance engine efficiency and reduce emissions, thereby contributing to the development of more sustainable automotive technologies. Future research should explore the use of ultra-low-viscosity lubricants (SAE 0W-20) and assess their long-term effects on engine wear.

En la presente investigación se explica la metodología para la creación de un sistema de diagnóstico aplicado a la detección de fallas mecánicas en vehículos con motores a gasolina mediante redes neuronales artificiales, el sistema se basa en el estudio de la fase de admisión del ciclo Otto, el cual es registrado a través de la implementación física de un sensor MAP (Manifold Absolute Pressure). Se emplea un estricto protocolo de muestreo y su correspondiente análisis estadístico. Los valores estadísticos de la señal del sensor MAP: área, energía, entropía, máximo, media, mínimo, potencia y RMS se seleccionaron en función al mayor aporte de información y diferencia significativa. Los datos se obtuvieron con la aplicación de 3 métodos estadísticos (ANOVA, matriz de correlación y Random Forest) para tener una base de datos que permita el entrenamiento de una red neuronal feed-forward backpropagation, con la cual se obtiene un error de clasificación de 1.89e-11. La validación del sistema de diagnóstico se llevó a cabo mediante la provocación de fallas supervisadas en diferentes motores de encendido provocado.

En el presente trabajo se explica la aplicación de RNA (redes neuronales artificiales) para la predicción de emisiones contaminantes generadas por fallas mecánicas en motores de encendido provocado, de la cual se puede cuantificar el porcentaje de CO (% monóxido de carbono) y el particulado por millón HC (ppm hidrocarburos sin quemar), a través del estudio de la fase de admisión del ciclo Otto, la cual es registrada por medio de la implementación física de un sensor MAP (Manifold Absolute Pressure). Se aplica un riguroso protocolo de muestreo y consecuente análisis estadístico. La selección y reducción de atributos de la señal del sensor MAP se realiza en función del mayor aporte de información y diferencia significativa con la aplicación de tres métodos estadísticos (ANOVA, matriz de correlación y Random Forest), de la cual se obtiene una base de datos que permite el entrenamiento de dos redes neuronales feed-forward backpropagation, con las cuales se obtiene un error de clasificación de 5.4061e-9 y de 9.7587e-5 para la red neuronal de CO y HC respectivamente.

In the present research the explanation of the applied methodology for the determination of the maximum pressure of compression of an engine of internal combustion alternative of ignited provoked (MEP), that is based on a study that starts from the characterization of the curves of the amperage rating of the starter motor. A protocol for data acquisition and subsequent statistical analysis is applied. The statistical values of the signal as energy, average, standard deviation, variance, kurtosis, asymmetry, maximum, minimum and crest factor are selected in function of the greater contribution of information for the characterization of the experiment; these values generate databases that are applied for the creation and training of a recurrent artificial neural network (RNAR) in which an absolute error of less than 2\% is obtained. In the first instance, the test methodology is applied in an engine assembled in a didactic bank and then the application of the method is applied in vehicles.