Graphene-based materials have great potential for tribological applications. Graphene’s unique properties such as low shear resistance, high stiffness, and thermal conductivity make it an attractive material for improving the properties of lubricants in a wide range of industrial applications, from vehicles to house refrigerators and industrial machinery such as gearboxes, large compressors, etc. The current review aims to give an engineering perspective, attributing more importance to commercially available graphene and fully formulated lubricants instead of laboratory-scaled produced graphene and base oils without additives. The use of lubricants with graphene-based additives has produced e.g., an increase in mechanical efficiency, consequently reducing energy consumption and CO₂ emissions by up to 20% for domestic refrigerators and up to 6% for ICE vehicles. Potential effects, other than purely friction reduction, contributing to such benefits are also briefly covered and discussed.

Transient car emission tests generate huge amount of test data, but their results are usually evaluated only using their “accumulated” cycle values according to the homologation limits. In this work, two machine learning models were developed and applied to a truck RDE test and two light-duty vehicle chassis emission tests. Different from the conventional approach, the engine parameters and fuel consumption were acquired from the Engine Control Unit, not from the test measurement equipment. Instantaneous engine values were used as input in machine-learning-based digital twins. This novel approach allows for much less costly vehicle tests and optimizations. The paper’s novel approach and developed digital twins model were able to predict both instantaneous and accumulated fuel consumption with good accuracy, and also for tests cycles different to the one used to train the model.

Lubricant friction modifier additives are used on lower viscosity engine oils to mitigate boundary friction. This work presents the development of a graphene-based material as an oil friction modifier additive, from formulation to actual vehicle tests. The graphene material was initially characterized using scanning electron microscopy (SEM) and Raman spectroscopy, which revealed the predominance of graphene nanoplatelets (GNPs) with an average of nine layers. After functionalization, two GNP additive variants were initially mixed with a fully formulated SAE 0W-20 engine oil and tribologically evaluated using reciprocating sliding tests at 40 and 120 °C and Hertzian pressure up to 1.2 GPa when both variants presented friction reduction. Then, the GNP additive variant with better performance was evaluated in a vehicle emission test using a fully formulated 5W-20 SAE oil as a reference. The addition of 0.1% of GNPs reduced fuel consumption by 2.6% in urban conditions and 0.8% in highway ones. The urban test cycle was FTP75 and higher benefits of the GNP additive occurred especially on the test start, when the engine and oil were still cold and on test portions where the vehicle speed was lower.

The growing awareness of reduced friction losses and new demands for electrical powertrains demand improved lubricants. Due to their unique properties, such as high thermal and electrical conductivity, graphene and its derivatives have been investigated for tribological applications, especially as lubricant additives. In this work, we investigated three commercially available graphene variants, one comprising a few layers and the other two comprising nanoplates, after functionalization as additives to lithium soap grease. The grease temperature dropping point increased by approximately 6 °C. Additionally, during the reciprocating friction test, friction increased with the test duration for the baseline grease, whereas it decreased for the ones containing graphene-based additives. On the test end, friction was reduced by 8% compared to the baseline grease. On a four-ball tribometer, the wear scar was reduced from 10 to 18% compared to the baseline grease. In general, no significant difference was seen between the three graphene-based variants. The promising results found with graphene nanoplates, a less expensive material than a few graphene layers, creates opportunities for a cost-competitive additive to commercial greases.

Climate constraints impose greenhouse gas emissions mitigation, and passenger cars have considerable contributions to contribute to this. To improve the engine efficiency of vehicles equipped with conventional powertrains, many technologies are available but with limited individual contribution. The experimental assessment of some technology regarding fuel economy measurement results is sometimes lower than test uncertainties. This study proposes a methodology to compensate the fuel economy for two test uncertainties: vehicle speed variations and battery recharging. The proposed method can be applied when investigating the effects of different vehicle design changes, including engine power cell design. In this work, the proposed method is demonstrated on the test of two oils: one 5W40, the other 5W20, both without FM. Applying the proposed methodology to experimental results, the expected higher influence of oil viscosity on urban conditions could be observed, and the experimental results presented a much better correlation with the vehicle numerical simulation. Applying the proposed compensation, fuel savings of using the 5W20 in comparison to the 5W40 oil was 3.5% under urban conditions and 2.0% on highways.

Two engine oil additives with graphene were tested in diesel engines. The first was a graphene oxide (GO)-based, commercially available additive that the supplier recommends using at a 3% v/v concentration in engine oil. The second was a graphene nanoplatelet (GNP)-based additive that is under development, which is more concentrated and allows for the addition of a much smaller amount of additive. Using the GO additive results in a reduction of brake-specific fuel consumption from 0.2% to 0.7%, depending on the engine load, and a 2% reduction in fuel consumption when the engine is run without load. The use of 0.1% wt of GNPs led to 0.4% of fuel savings on an ESC emission cycle. Increasing the GNP concentration to 0.2% did not further reduce fuel consumption.