Fuel Consumption Measurements Research Articles

Pavement Life Cycle Cost Analysis Based on Hysteresis Data and Analysis of Fuel Consumption Measurements

Higher investment costs for Portland cement concrete over asphalt concrete pave-ments is compensated by lower maintenance costs and a longer technical life. For sus-tainability reasons the less rolling resistance on the former pavement type is claimed to be another benefit. However, methods of measuring fuel consumption are difficult to carry out in a consistent manner. The present paper examines some fuel measure-ments and then uses the hysteresis method as a validation for some of the tests. The data are then used to determine the economical implications and life cycle cost analy-sis. The results support choosing Portland Cement Concrete for roads with high vol-ume truck traffic.

Gravimetric approach of fuel consumption in 30% biodiesel blends fuel: cost-effective solution of real-world fuel consumption measurement

Gravimetric approach of fuel consumption in 30% biodiesel blends fuel: cost-effective solution of real-world fuel consumption measurement

Metrology for reliable fuel consumption measurements in the maritime sector

Reliable fuel consumption measurements play an essential role in the maritime sector whether for emission determinations or the use of novel fuels. A verification of the performance of flow meters used for fuel consumption determination under realistic conditions is thus of interest. Apart from the influence of the pressure- and temperature-dependent transport properties of the fuels, a characterization of the measurement performance under dynamic fuel consumption is of relevance. Traceable metrological infrastructure and procedures, which will enable an evaluation of the measurement performance of flow meters in this regard, are being developed in the scope of the EMPIR project “Safest” (20IND13). A consumption profile of a ferry navigating in a harbour serves as basis. In addition to the measurement accuracy under dynamic conditions, first investigations of the performance of flow meters are carried out in terms of fluid temperature and fuel transport properties for the example of spindle screw meters.

A test bench for the assessment of flow meters accuracy for fuel consumption measurement in highly dynamic drive cycle tests

The transport sector is one of the most significant contributors in terms of NOx, particulate matter and net CO2 emission. In order to mitigate this environmental impact, severe homologation limits have been introduced by the different regional Authorities. Both pollutant emissions and CO2 production of vehicles are measured during standardized laboratory test cycles and road tests, both characterized by relatively abrupt powertrain load and fuel rate transients. This dynamic approach results in severe issues in terms of fuel flow measurement capabilities as flow meters are typically calibrated in steady fuel flow conditions. In the present activity, an experimental test bench was developed with the aim of offering a flexible and affordable test system to evaluate the performances of different flow meters in dynamic conditions. The test bench consists of a complete fuel injection system managed to reproduce an assigned cycle; by this approach the highly-dynamic engine-like fuel flow rate is replicated, offering the opportunity to compare the accuracy of the flow meter under test with both a master Coriolis-type flow meter and the nominal fuel flow time-profile. This bench allows the execution of flow meter accuracy tests in controlled operating conditions with reduced efforts with respect to conventional vehicle test benches and with a high reproducibility level.

Non-invasive fuel consumption measurement for internal combustion engines based on Otto cycle

Non-invasive fuel consumption measurement for internal combustion engines based on Otto cycle

On-Board Fuel Consumption Meter Field Testing Results

This paper aims to investigate and compare the fuel consumption data obtained from on-board fuel consumption meters (OBFCMs) from approximately 1000 vehicles through field testing. Furthermore, this research aims not only to compare the OBFCM readings but also to juxtapose them against the fuel consumption specifications provided by the respective vehicle manufacturers. To collect data, a cost-effective on-board diagnostics (OBD) reader and a user-friendly mobile app were employed, providing an accessible and efficient method for fuel consumption analysis. Field testing involved a diverse range of vehicles, covering various makes, models, and years of production. The OBCFM readings were recorded over a 9-month period, probably capturing a wide range of driving conditions and patterns. In order to ensure the reliability of the OBCFM readings, the fuel consumption measurements obtained from the manufacturers specifications were utilized as a reference benchmark. Preliminary data analysis indicates that there are noticeable variations in the fuel consumption data obtained from the OBCFM and the manufacturer specifications. These differences can be attributed to various factors. The novelty of the presented data lies in using a new feature implemented in EU cars since 2019. The study capitalizes on this feature, allowing for the collection of data from a broad spectrum of vehicles throughout the country under genuine driving conditions.

Real-driving CO2, NOx and fuel consumption estimation using machine learning approaches

Real driving emissions (RDE) testing are gaining attention for monitoring and regulatory purposes because of providing more realistic emission and fuel consumption measurements compared to laboratory tests. This study aims to develop machine learning (ML) based emission and fuel consumption estimation models using real-driving measurement data. A light-duty diesel vehicle equipped with a portable emissions measurement system (PEMS) was driven in an urban test route by 30 participant drivers of disparate backgrounds to obtain a wide variety of data in terms of driving behaviour and traffic conditions. The Pearson correlation coefficient was used to select the input variables among 36 driving behaviours and 6 engine parameters. The CO2, NOx and fuel consumption prediction models were developed using linear regression (LR), support vector machine (SVM) and Gaussian process regression (GPR). The results showed that all three models could predict CO2 with an absolute relative error (ARE) of less than 9%. The GPR model showed the best performance in CO2 prediction with an R2 of 0.74 and ARE of 3.30%. LR model showed the best prediction accuracy for NOx with an R2 of 0.80 and ARE of 8.91%. All three models worked well for fuel consumption prediction, however, GPR showed the best accuracy with an R2 of 0.81 and ARE of 3.52%. This method lays a foundation for developing route/region specific emission and fuel consumption models that will help to monitor and reduce the environmental impact and the amount of burned fuel. Moreover, developing models from different driver classes will provide valuable insights into emission-optimal driving behaviour which could be used to train new drivers.

Impacting Factors on the Accuracy of a Fuel Consumption Measurement

The paper discusses the challenges in performing an accurate fuel consumption measurement and the various factors that affect it. Based on an empirical pressure measurement carried out on a cargo ferry by KRAL engineers, ways to improve measurement accuracy are identified.

Characterisation of a Coriolis flow meter for fuel consumption measurements in realistic drive cycle tests

When testing light-duty and heavy-duty vehicles on chassis dynamometers, as in the WLTP, or engines on engine test benches, as in the WHDC, it is required to measure the fuel consumption. In the preferable case, the measurement of the fuel consumption is carried out with suitable flow meters. These require high measurement accuracy in a wide flow range, independent of the fuel type, as the flow rate range is often very large and depends on the power range of the vehicle engines. Moreover, the fuel flow rate in the test cycles is very dynamically related to the loads. In the scope of the ongoing EMPIR Joint Research Project 20IND13 SAFEST the dynamic flow behaviour as well as the measurement accuracy of flow meters for different types of fuels are investigated. This paper presents first results from the realisation of dynamic flow profiles, and flow measurements with a Coriolis Flow Meter with different representative fuels in a wide density and viscosity range and a wide flow rate range at different fuel temperatures.

Revolutionizing IC Genset Operations with IIoT and AI: A Study on Fuel Savings and Predictive Maintenance

In a world increasingly aware of its carbon footprint, the quest for sustainable energy production and consumption has never been more urgent. A key player in this monumental endeavor is fuel conservation, which helps curb greenhouse gas emissions and preserve our planet’s finite resources. In the realm of the Industrial Internet of Things (IIoT) and artificial intelligence (AI) technologies, Caterpillar (CAT) generator set (genset) operations have been revolutionized, unlocking unprecedented fuel savings and reducing environmental harm. Envision a system that not only enhances fuel efficiency but also anticipates maintenance needs with state-of-the-art technology. This standalone IIoT platform crafted with Visual Basic.Net (VB.Net) and the KEPware Object linking and embedding for Process Control (OPC) server gathers, stores, and analyzes data from CAT gensets, painting a comprehensive picture of their inner workings. By leveraging the Modbus Remote Terminal Unit (RTU) protocol, the platform acquires vital parameters such as engine load, temperature, pressure, revolutions per minute (RPM), and fuel consumption measurements, from a radar transmitter. However, the magic does not stop there. Machine Learning.Net (ML.Net) empowers the platform with machine learning capabilities, scrutinizing the generator’s performance over time, identifying patterns and forecasting future behavior. Equipped with these insights, the platform fine tunes its operations, elevates fuel efficiency, and conducts predictive maintenance, minimizing downtime and amplifying overall efficiency. The evidence is compelling: IIoT and AI technologies have the power to yield substantial fuel savings and enhance performance through predictive maintenance. This research offers a tangible solution for industries eager to optimize operations and elevate efficiency by embracing IIoT and AI technologies in CAT genset operations. The future is greener and smarter, and it starts now.

Analysis of Uncertainty Impacts on Emissions and Fuel Economy Evaluation for Chassis Dynamometer Testing

This study illustrates a methodology for quantifying the uncertainties encountered in the measurement of tailpipe emissions and in the fuel consumption measurements for light-duty conventional vehicles tested on a four-wheel drive chassis dynamometer. The study leverages high-fidelity experimental data collected over three standard drive cycles, UDDS, HWY and US06, intended to simulate a wide range of operating conditions. A method is developed to estimate the measurement uncertainties in fuel consumption for a test cycle, which occur due to the accumulation of measurement uncertainties propagated through the system. The uncertainty determination model uses statistical analysis and standard propagation techniques to evaluate and combine the uncertainties introduced from various sources (including the vehicle, chassis dynamometer, driver, and instrumentation). The analysis also examines three different experimental methods for determining the fuel consumption: 1) carbon mass balance, 2) volumetric fuel scale and 3) gravimetric fuel scale, and takes into consideration the properties of the instrumentation used. The results show that the most significant influence on the determination of the emissions comes from the concentration measurement, and similarly the biggest impact on the total fuel consumption uncertainty comes from the uncertainty in the determination of the carbon dioxide mass, due to the large presence of this pollutant in the overall emissions. It was found that the fuel consumption uncertainties are in the range of ±1-2% for all three methods analyzed, with the lowest values being obtained for measurements performed using the gravimetric method for all three drive cycles considered.

Test and Measurement Data Analysis of Fuel Consumption Measurement and Control System of Floating Ball Engine

Abstract Fuel consumption is an important index to evaluate the fuel economy of automobile engine, and it is one of the important measurement parameters in engine test research. This paper uses the engine eddy current test bench to carry out the repeatability verification test, measurement accuracy and accuracy verification test, and engine bench test of the designed and developed floating ball fuel consumption meter. The data obtained from the test are analyzed to verify the floating ball fuel consumption. performance indicators of the instrument.

Regulated and unregulated emissions and exhaust flow measurement of four in-use high performance motorcycles

Exhaust emissions from four larger motorcycles, 599–1078 cm 3 engine displacement, 2008 to 2015 model years, were measured on a chassis dynamometer using cold and hot engine start World Motorcycle Transient Cycle. Raw exhaust gas was sampled and analyzed online by a portable FTIR analyzer, providing mid-infrared spectra which was resolved for greenhouse gases CO 2 , methane and N 2 O, and for NO, NO 2 , NH 3 , CO and formaldehyde. Total number of non-volatile particles was measured by a portable counter. Particle size distributions were measured by fast electric mobility sizers, each with a different diluter. As a novel approach, useful for on-road measurements, exhaust flow was measured using a high time resolution Pitot tube, resolving rapid pulsations and reverse flows in the exhaust pipe which were often of higher magnitude than the average flow. The fuel consumption inferred from carbon emissions calculated from measured exhaust flow and the exhaust gas composition measured by the portable FTIR was, for all bikes, within 6% of the gravimetric fuel consumption measurement. The CO 2 , CO and NO emissions based on diluted exhaust gas sampled from a full-flow dilution tunnel were comparable to those based on raw exhaust gas composition and exhaust flow measured by the fast-response Pitot tube. Emissions of NH 3 were 27–273 mg/km and were associated primarily with fuel-rich operation during transients, while emissions of NO were 30–509 mg/km were associated with slightly lean operation and/or high exhaust flows, suggesting the three-way catalytic converters were perhaps undersized, but generally working, and the emissions of NO and NH 3 are attributed primarily to non-stoichiometric operation, primarily during transients. Methane and N 2 O constituted about 1% of the global warming potential of CO 2 . The average number of non-volatile particles per km varied from 26 to 270 billion (2.6–27 × 10 11 ). From all particles within the 5–560 nm range, 29–75% were smaller than 23 nm. Particle sizers were reporting higher concentrations of nanoparticles when connected to a heated two-stage ejector diluter compared to a rotating disc diluter. • CO 2 , CH 4 , N 2 O, NO, NO 2 , NH 3 , formaldehyde measured with portable FTIR, WMTC cycle. • Exhaust flow measured with high resolution Pitot tube capable of on-road use. • Total carbon emissions within 6% (avg 3%) of direct measurement of fuel flow. • NO 30–509 mg/km, NH 3 27–273 mg/km, high share of non-stoichiometric transients. • 26–270 billion non-volatile particles/km; 29–75% of all 5–560 nm particles <23 nm.

Estimation of the accuracy of fuel consumption measurement based on data from the on-board CAN-bus

The legal requirements for vehicle exhaust emissions are becoming increasingly stringent. Emissions are directly related to fuel consumption. Therefore, reducing fuel consumption is a very urgent task. To solve this problem, it is necessary to assess the influence of various factors on the actual fuel consumption. To determine the fuel consumption, there are a large number of different methods using external flow meters installed in the fuel line, installing additional level sensors in the fuel tank. A very simple and convenient method is based on the use of information from the electronic engine control unit. This method requires minimal financial investment and does not require significant structural changes. The question arises about the measurement accuracy according to this method, since the ECU calculates the fuel consumption according to a mathematical model, calibrated and programmed at the factory. To assess the accuracy of this method, a set of tests was carried out. The tests were performed at constant vehicle speed and according to the standard driving cycle. During the tests, the fuel consumption was recorded using a high-precision calibrated flow meter installed in the fuel line and on the basis of data from the engine ECU. Based on the test results, a conclusion was made about the size of the discrepancy in the methods for determining the fuel consumption.

Compensation of the error of automotive capacitive fuel level sensors when using them on special self-propelled rolling stock

Consumption of diesel fuel by the special rolling stock of Russian Railways per year amounts to tens of thousands of tons, and the issue of reliable accounting and control of its consumption is quite urgent. Currently, part of the special self-propelled rolling stock is equipped with on-board systems for measuring fuel consumption, however, in many units of this equipment, fuel control and accounting is carried out in manual mode. Massive introduction of on-board fuel consumption measurement systems on special self-propelled rolling stock is constrained, on the one hand, by the rather high cost of fuel sensors used on locomotives, on the other hand, by the increased error of relatively inexpensive automotive capacitive fuel level sensors. As part of the laboratory tests of such sensors, it was determined that when they operate on fuel of the same grade, the error corresponds to the passport and is at the level of 1 %, and when operating on fuel of different grades without additional recalibration, the error can reach 4 % or more. This is largely due to the simplified technology for measuring the amount of fuel in units of volume and insufficient compensation for changes in the density of diesel fuel. To solve this problem, an alternative to standard technology for determining the amount of fuel using automotive capacitive fuel level sensors is proposed, in which the dependence of the readings of these sensors on the fuel density at a standard temperature, once obtained in laboratory conditions, is used. Proposed technology of using automotive capacitive fuel level sensors on a special self-propelled rolling stock will allow keeping its relative reduced error at the level of 1 % and will provide measurement of the amount of fuel in units of mass.

Impact of Pavement Roughness on Fuel Consumption for a Range of Vehicle Types

Fuel consumption increases with pavement roughness, thereby impacting vehicle operating costs. This paper explores the use of physics-based models to simulate a fleet consisting of 29 different vehicles representing a range of vehicle categories, technologies, and fuel types. Each vehicle model was simulated over a gamut of pavement roughness profiles while traveling at steady-state speeds from 16 to 145 km/h to estimate the impact of roughness on the vehicle fuel consumption. Published fuel consumption measurements on a large sport utility vehicle (SUV) and a dump truck gathered before and after pavement resurfacing (road condition going from fair to good) were used for validation of the prediction of change in fuel consumption due to pavement roughness. The simulation results compared closely (within 2%) with the measured data sets for both vehicles.

Performance tests of flowmeters for fuel consumption measurements in fishing vessels

In the study, we developed a flowmeter to be mounted on fishing vessels to measure their fuel consumption in the sea, and evaluated the performance of the flowmeter under the dynamic conditions of a fishing vessel. In order to analyze the dynamic behavior of the fishing vessel in the sea, dynamic motions (pitch and heave) based on the speed of the fishing vessel were obtained through simulation and field test. In the presence of vibration, the uncertainty of the ultrasonic flowmeter increased from 0.12% to 0.5%. The measurement error of the ultrasonic flowmeter also increased from 0.5% up to 2% when dynamic behavior motions (pitch and heave) increased with the speed of the fishing vessel. The results confirmed that the measurement errors of ultrasonic flowmeter were consistent at the same flow rate and speed of the fishing vessel in the dynamic test bed and sea field test. Additionally, to confirm the long-term stability of the developed flowmeter, a long-term test of the flowmeter was performed for three days in actual fishing conditions. The results indicated that the ultrasonic flowmeter developed in the study can measure the fuel consumption of an actual fishing vessel. To the best of the authors’ knowledge, this is the first study to measure the fuel consumption in the sea via mounting a flowmeter on an actual fishing vessel.

Determination of the deviation of the on-board computer in the vehicle when determining the average fuel consumption

Fuel consumption measurement itself is a demanding process and it is difficult to determine the exact consumption of a vehicle. Fuel consumption can be determined in various ways. One way to determine consumption is through driving tests. We know several types of driving tests. Nowadays, most vehicles and all new vehicles can provide a wealth of data to the driver directly during vehicle operation. One of them is the data on the consumption of the vehicle also through the on-board computer located in the vehicle. The information provided to the driver may not reflect reality. In most cases, they are inaccurate and do not correspond to reality. Therefore, the subject of the research will be to verify the accuracy of the provided data on vehicle consumption by the on-board computer. The aim of the research will be to determine the extent to which consumption data are true. Vehicle consumption, as well as measurements are performed on one vehicle in every day traffic. This will ensure that it is possible to compare the measured data with each other.

The effect of sweet orange peel addition into premium gasoline on fuel consumption and flue gas emissions

This study offered an investigation as to how the use of sweet orange peel oil as an additive for premium gasoline can increase octane performance which influence combustion. Sweet orange peel oil-acting as a RON booster-contains monoterpene compounds and oxygenated hydrocarbons which can improve the quality of combustion. The study also aimed to analyze the combustion characteristics of premium gasoline mixture affected by sweet orange peel additives on SI engines. This study carried out tests on an engine fuel consumption and HC emissions. The cyclic rings and methyl groups of monoterpene compounds in sweet orange peel additives contribute to the increase of electronegativity of additives, therefore being able to interfere with the premium gasoline’s compounds which are dominated by aromatics hydrocarbons. It has an impact on the branches of single-bond molecules series of premium gasoline. This makes the premium gasoline cannot be ignited at low temperatures, which results in perfect combustion. In addition, the polarity of triacetin and eugenol, which includes oxygenated hydrocarbons, is able to reduce CO as the avoided gas in combustion. Sweet orange peel additives—processed by maceration extraction method—are added to premium gasoline. There are 3 ratios of additives in this study; orange peel essential oil extracted with a concentration ratio of dried orange peels simplicial to n-hexane (v / v) of 1% (EO A); 0.75% (EO B); and 0.6% (EO C). To determine the effect of the additive, exhaust gas emission testing and fuel consumption measurement were carried out on the 2013 Honda Absolute Revo motorcycle 4-step, SOHC, 1 cylinder, 109.17 cc engine. The exhaust gas, i.e. CO, HC, CO2, and O2 were analyzed using a Stargas-898-type gas analyzer. Furthermore, fuel consumption measurement was carried out at 3500 rpm engine speed. The results show that CO could be reduced by 37.7%, while the remaining O2 was reduced by 17%. The addition of sweet orange peel additives could increase CO2 levels in exhaust gases when compared to premium gasoline combustion without additives. However, the premium gasoline with additive A (EO A) blend tends to produce higher hydrocarbon emissions compared to other types of premium additive proportions. In conclusion, adding sweet orange peel additives to premium gasoline can reduce fuel consumption by 3.3%; 3.2%; and 3.4% for each blend, respectively.

Вплив деяких параметрів конструкції теплового витратоміра палива на передачу тепла в ньому

The article analyzes the influence, relationship and value of design parameters of the heat flow meter on its radial and axial heat fluxes in the tube (tube diameter, heater diameter and their ratio, thermal conductivity of the tube material, etc.). It is shown that at the stage of choosing the design parameters of the flowmeter it is necessary to take into account the influence of its radial heat flux on the axial one. The influence of radial heat flux in the flowmeter tube on the error of fuel loss measurement is substantiated. Analytical dependences which allow to define an axial heat stream are resulted, their analysis concerning influence of flowmeter tube constructive parameters on heat transfer is carried out. Measures are planned and recommendations are developed for the choice of design flowmeter parameters, development or use, provided that the influence of radial heat flow on the axial is reduced, which will reduce the total error of fuel consumption measurement. Regarding the choice of design parameters of heat meters while reducing the error of measuring fuel consumption, it is shown that the maximum possible decrease in the diameter of the heater and increase the diameter of the flow tube reduce the impact of radial heat flow on the axial and thus reduce the total fuel consumption error. Numerical ratios of tube diameter to flowmeter heater diameter for different thermal conductivities of tube materials are given under the condition of minimal influence on fuel consumption measurement error. For tube materials with a thermal conductivity 0.16… 0.25 W / (m ∙ K) (ebonite, fluoroplastic F-5, etc.) the tube diameters ratio and the heater should be within 1.51… 1.62, and for materials with more high thermal conductivity (thermal conductivity greater than 14.9 W / (m ∙ K)), this ratio should be equal to 1.99.