Recent research:
The consolidated works analyze how engine performance and emissions respond to material, design, and control strategies across conventional and advanced powertrain contexts. The first study investigates how oil viscosity and fuel quality affect power output and pollutant emissions in a gasoline engine, using a Response Surface Methodology experimental design on a mid-size SUV. Findings show that lower-viscosity lubricants (e.g., SAE 5W-30) can boost power, while higher-octane fuels improve combustion stability and enable advanced timing; however, high-viscosity oils at high speeds raise CO2 emissions. Emissions are most strongly influenced by engine speed (RPM), with fuel quality also significant. The second work provides a comprehensive bibliographic review of nano-lubricants, synthesizing 176 studies on nanoparticles in lubricants, concentration ranges (0–1%), and their effects on friction reduction, highlighting prevailing experimental approaches and materials. The third piece details a design methodology for a Formula Student monocoque guided by 2020 regulations, using CAD and FEM data to optimize mass distribution, geometry, and safety factors; results compare composite laminates’ weights and demonstrate structural advantages over tubular chassis in a referenced electric model. The fourth article describes a methodology combining artificial intelligence with sensor data (Knock Sensor and Camshaft Position Sensor) to detect mechanical failures in an internal combustion engine. Using Random Forest for feature selection plus an ANN and an SVM for classification, the approach achieved very low error rates, illustrating AI-driven fault diagnosis feasibility in engines. Collectively, the research advances understanding of how lubricants, materials, and AI-enabled diagnostics can boost efficiency, reduce emissions, and improve reliability in modern transportation technologies, while outlining practical design and validation pathways for future sustainable powertrains.