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 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.

This study investigates the effect of oil viscosity on pollutant emissions and fuel consumption of an internal combustion engine (ICE) at high altitudes using a response surface methodology (RSM). A Chevrolet Corsa Evolution 1.5 SOHC gasoline engine was used in Cuenca, Ecuador (2560 m above sea level), testing three lubricating oils with kinematic viscosities of 9.66, 14.08, and 18.5 mm²/s, measured at a temperature of 100 °C under various engine speeds and loads. Key findings include the following: hydrocarbon (HC) emissions were minimized from 150.22 ppm at the maximum load to 7.25 ppm with low viscosity and load; carbon dioxide (CO₂) emissions peaked at 15.2% vol with high viscosity and load; carbon monoxide (CO) ranged from 0.04% to 3.74% depending on viscosity and load; nitrogen oxides (NOx) were significantly influenced by viscosity, RPM, and load, indicating a need for model refinement; and fuel consumption was significantly affected by load and viscosity. RSM-based optimization identified optimal operational conditions with a viscosity of 13 mm²/s, 1473 rpm, and a load of 78%, resulting in 52.35 ppm of HC, 13.97% vol of CO₂, 1.2% vol of CO, 0 ppm of NOx, and a fuel consumption of 6.66 L/h. These conditions demonstrate the ability to adjust operational variables to maximize fuel efficiency and minimize emissions. This study underscores the critical role of optimizing lubricant viscosity and operational conditions to mitigate environmental impact and enhance engine performance in high-altitude environments.

This study investigates the effect of oil viscosity on pollutant emissions and fuel consumption of an internal combustion engine (ICE) in high-altitude using Response Surface Methodology (RSM). A Chevrolet Corsa Evolution 1.5 SOHC gasoline engine was used in Cuenca, Ecuador (2560 meters above sea level), testing three lubricating oils with kinematic viscosities of 9.66, 14.08, and 18.5 cSt under various engine speeds and loads. Key findings include: hydrocarbon (HC) emissions were minimized to 7.25 ppm with low viscosity and load; carbon dioxide (CO2) emissions peaked at 15.2% vol with high viscosity and load; carbon monoxide (CO) ranged from 0.04% to 3.74% depending on viscosity and load; nitrogen oxides (NOx) were significantly influenced by viscosity, RPM, and load, indicating a need for model refinement; and fuel consumption was significantly affected by load and viscosity. RSM-based optimization identified optimal operational conditions with a viscosity of 13 sCt, 1473 rpm, and a load of 78%, resulting in 52.35 ppm of HC, 13.97% vol of CO2, 1.2% vol of CO, 0 ppm of NOx, and a fuel consumption of 6.66 l/h. These conditions demonstrate the ability to adjust operational variables to maximize fuel efficiency and minimize emissions. This study underscores the critical role of optimizing lubricant viscosity and operational conditions to mitigate environmental impact and enhance engine performance in high-altitude environments.

This study develops an integrated methodological approach for interpreting used oil analysis results in diesel engines, focusing on optimizing maintenance strategies. The methodology combines a literature review with a quantitative assessment of 156 lubricant analysis reports from a fleet of diesel waste collection trucks operating in Cuenca, Ecuador, a high-altitude city. The framework includes critical limits for key lubricant parameters, correlation analysis, and Principal Component Analysis (PCA) to identify dominant degradation mechanisms. The Binary Segmentation (BS) algorithm is also used for Change-Point Detection. The findings indicate four primary degradation pathways: thermal–chemical degradation influenced by sulfur, oxidation, and soot; metallic wear and base depletion, involving iron, chromium, and copper; external contamination linked to silica and copper; and viscosity alteration due to lubricant aging. Significant degradation shifts were identified at approximately 346 and 444 service hours, suggesting critical points for condition-based maintenance interventions. This study highlights the effectiveness of multivariate statistical tools in enhancing the interpretation of used oil analysis and optimizing predictive maintenance strategies. The integration of Change-Point Detection and multivariate analysis provides a robust framework for defining oil change intervals based on lubricant condition rather than fixed time- or mileage-based criteria. This approach offers practical benefits for fleet operations, enabling the reduction in operational costs, enhancing engine reliability, and minimizing the environmental impact of unnecessary lubricant changes.

Early detection of faults in internal combustion engines (ICEs) remains an important challenge for improving operational reliability and supporting condition assessment. Conventional approaches based on On-Board Diagnostics II OBD-II data, vibration, or exhaust gas analysis face practical limitations, motivating the exploration of non-invasive acoustic diagnostics. This study proposes and validates a pipeline that combines acoustic signals, Symmetrized Dot Pattern (SDP) transformations, and convolutional neural networks (CNNs) to identify injector and ignition failures in multicylinder ICEs. Acoustic measurements were acquired in a purpose-built semi-anechoic chamber using a smartphone equipped with Micro-Electro-Mechanical Systems (MEMS) microphones, ensuring high-signal-to-noise-ratio recordings under controlled fault conditions. A total of 540 independent acoustic recordings were transformed into 540 grayscale SDP images, which were used to train and test a custom CNN architecture. Results demonstrated an overall accuracy of 81.11% in independent verification, with outstanding performance in Spark Plug 3 and Spark Plug 4 (F1 = 1.000), as well as Injector 4 and normal operation (F1 = 0.947). More challenging cases included Injector 1, Injector 3, and Spark Plug 1 (F1 ≈ 0.70–0.78), where spectral overlap led to cross-misclassifications. These findings are consistent with recent studies reporting the robustness of SDP for representing transient acoustic phenomena and the superior classification capability of CNNs for pattern recognition tasks. The study extends SDP applications to internal combustion engines, demonstrating the feasibility of detecting injection and ignition faults using acoustic measurements. The proposed SDP–CNN pipeline represents a non-invasive, low-cost approach to acoustic fault analysis using consumer-grade sensing devices. Future research should focus on expanding datasets, incorporating domain adaptation techniques, and validating performance under real-world operating conditions to improve generalization and support practical acoustic monitoring applications.