Fuel economy optimization from the interaction between engine oil and driving conditions

Tormos, Bernardo; Plá, Benjamín; Bastidas, Sophia; Ramı́rez, Leonardo; Pérez, Tomás A.

doi:10.1016/j.triboint.2019.05.042

Cited by 14 publications

(5 citation statements)

References 41 publications

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“…Such reductions hardly could be explained only by friction reduction since friction losses on an ICE correspond to about 25% of fuel consumption at urban conditions, less at full load. Typical fuel savings with improved lubricant are around 2% [31][32][33][34][35][36]133], Tormos [133]. Other than pure tribological mechanisms should exist for graphene in the oil producing substantial fuel savings.…”

Section: Graphene As Combustion Improvermentioning

confidence: 99%

Review of Graphene-Based Materials for Tribological Engineering Applications

Tomanik,

Christinelli,

Souza

et al. 2023

Eng

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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.

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Section: Graphene As Combustion Improvermentioning

confidence: 99%

Review of Graphene-Based Materials for Tribological Engineering Applications

Tomanik,

Christinelli,

Souza

et al. 2023

Eng

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“…1–5 However, the energy saving potential of lubricating oil is closely related to the actual combustion of the engine and the driving condition of the vehicle. 6–10 How to quickly and accurately measure the fuel economy of engine oils under specified cycles is a difficult problem in the process of lubrication technology development.…”

Section: Introductionmentioning

confidence: 99%

Development of turbocharged engine bench test adapted to multiple driving cycles for lubricants fuel economy

Ye,

Yimin,

Xiangkui

2023

International Journal of Engine Research

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Aimed to more truly and stably measure the fuel economy of a vehicle under different driving cycles, simultaneously reduce prime cost and short experimental period. An engine fuel economy test method and bench which could adapt to multiple cycle conditions was developed. The fuel economy test of a certain SUV under NEDC/WLTC was carried out, and the transient operating parameters of the engine were collected. The engine fuel economy test bench was established and accurately controlled according to transient parameters. The FEI of five different formulations of engine oils was tested. The results indicate that the actual bench parameters are in good agreement with the target transient parameters. Multiple FC measurements of baseline oil and multiple FEI measurements of high reference oil show that the engine bench has good repeatability. The data fluctuation degree of bench test is obviously smaller than that of vehicle test. The correlation coefficients of comprehensive FEI between bench test and vehicle test are 95.25% (NEDC) and 96.39% (WLTC). It means that the bench test can completely replace the vehicle test for fuel economy analysis. The FEI of No.3 oil which has the best fuel improvement are 1.888% (NEDC) and 1.248% (WLTC) respectively. Compared with the vehicle test, the engine bench testing can save 75% of the time. The fuel economy bench and test methods are also suitable for the verification of fuel economy improvement of other engine optimization measures under different driving cycles. The development period of engine technologies can be shortened, and the test cost can be reduced.

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“…To reduce vehicle emissions and fuel consumption, manufacturers invest enormous resources for developing different strategies and methodologies to increase the efficiency of vehicles. Some strategies do not require substantial hardware modification, such as the introduction of low viscosity/friction engine oils [1][2][3][4][5][6][7][8][9][10][11][12][13][14][15][16][17]. Fuel savings with improved oil formulations in the order of 1 to 3% [8,9,11] are evaluated by specific test protocols such as the API sequence VI-A, Mercedes Benz M111 and ECE-15 cycle for truck chassis [11].…”

Section: Introductionmentioning

confidence: 99%

“…Computer and/or empirical models are usually used to predict individual contributions prior to more expensive engine and vehicle tests; see for example [11,[13][14][15][16]. During engine development, numerical models are used for optimizing fuel consumption (as well as other parameters).…”

Section: Introductionmentioning

confidence: 99%

Machine-Learning-Based Digital Twins for Transient Vehicle Cycles and Their Potential for Predicting Fuel Consumption

Tomanik

Jiménez-Reyes

Tomanik³

et al. 2023

Vehicles

Self Cite

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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.

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Fuel economy optimization from the interaction between engine oil and driving conditions

Cited by 14 publications

References 41 publications

Review of Graphene-Based Materials for Tribological Engineering Applications

Review of Graphene-Based Materials for Tribological Engineering Applications

Development of turbocharged engine bench test adapted to multiple driving cycles for lubricants fuel economy

Machine-Learning-Based Digital Twins for Transient Vehicle Cycles and Their Potential for Predicting Fuel Consumption

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