Abstract
The present research concerns itself with measuring and evaluating fuel consumption of compression ignition engine in passenger cars by a variety of methods and in different operating modes. The main objective is to compare different procedures for the cars’ fuel consumption experimental investigation in the point of view of their accuracy. The measurements were carried out on Citroen Berlingo motor vehicle. The very research is focused on implementing and evaluating the discussed consumption by three different methods, namely calculation from consumed fuel volume measured on fuel station flowmeter (pump stand), recording the data from an on-board computer and obtaining data from a specific application. Given that a certain driving style has a significant impact on fuel consumption, particular examinations were conducted in three operating modes, where the very journeys were made in order to measure the consumption in relation to an economic, a regular and a dynamic driving style. The vehicle being tested was driven on four specified routes that varied in their length, maximum permitted velocity on individual sections and number of intersections, or a citymotorway ratio. To this end, the experimental research resulted in putting toward recommendations and procedures, including appropriate methods, to ensure reliability of fuel consumption measurements with respect to the diverse operating modes of passenger cars.
1 Introduction
Fuel consumption is one of the main criteria when comparing fuel economy of road motor vehicles. Corresponding values of vehicles’ fuel consumption may be found in their technical certificate, though the values are inaccurate at times. Thus, it is appropriate for vehicle users to establish a procedure according to which they would be able to calculate the consumption without the help of laboratory equipment.
The main aim of the paper is therefore to suggest a methodological procedure for measuring fuel consumption that would quantify the amount of consumed fuel as accurately as possible. It may be argued that determining the most precise method is important not only from the viewpoint of calculating exact fuel costs.
Furthermore, fuel consumption is also an important indicator of the technical condition of vehicles. Due to increased consumption, it is possible to assume a vehicle failure. High fuel consumption may also be caused, for instance, by a leak in the fuel system, a fault in the injection unit, or even a more serious fault occurring directly in the engine. The previously mentioned applies to the most common types of engines in automobiles – spark ignition engine and compression ignition engine.
Additionally, it is advisable to monitor fuel consumption with regard to the production of harmful engine exhaust gases entering the air. Exhaust pollution is currently a serious issue, causing car manufacturing companies to compete in producing cars with the lowest reported fuel consumption. Such companies are also forced to do so by various regulations to limit exhaust emissions [1].
2 Literature review
Numerous publications are focused on the subject of examining the internal combustion engine fuel consumption under different conditions. For instance, Cho and Choi [2] look into possibility of estimation of fuel consumption efficiency by processing and analyzing digital tachograph big data for commercial vehicles using parallel processing with the MapReduce mechanism. Compared to the conventional fuel consumption examination using the On-Board Diagnostics II equipment, the authors implement actual tachograph data and On-Board Diagnostics II fuel consumption data to determine reasonable relationships to quantify fuel efficiency rates. Similarly to the previous case, even the studies [3, 4] discuss the scenarios of measuring and analyzing engine condition data regarding the fuel consumption investigation on the basis of actual driving behaviors in certain transport areas. And by analogy, the authors Baquero-Larriva and Alvarez-Coello develop a data acquisition system, which integrates different devices and technologies in terms of reliable fuel consumption measurement systems [5].
On the other hand, the literatures [6, 7] deal with analyzing specific combustion characteristics occurred in an engine under load. In line with these aspects, the authors in [6] evaluate capabilities of low-temperature combustion concept to operation of a medium-duty compression ignition engine (hereinafter referred to as CI) proposing multiple approaches to solve its crucial challenges. Wu and Jang also carried out engine combustion analysis, in which they designed a modified homogeneous charge compression ignition system for a 150cc motorcycle engine [7].
Multiple literature sources summarize outcomes related to investigation of the fuel consumption of various means of transport, deployed in public passenger transportation, compared to energetic consumption of alternative types of engine propulsion. For example, the manuscript [8] presents the authors’ findings associated with the long-term examination of operation characteristics of trolleybus Škoda 26Tr Solaris equipped with energy storage lithium-titanate traction battery, hybrid trolleybus with installed diesel generator, and bus complying with the EURO 6 emission standard in terms of electricity and diesel fuel consumption per 1 km. And also, the paper [9] highlights the uppermost results in the context of energetic attributes of different city bus systems. To this end, research activities focused on looking into the fuel and energy consumption of two buses fueled by compressed natural gas and engine diesel, trolleybus, and battery-electric bus were rendered while various driving conditions in the certain transport territory.
Unlike the previous articles, the research study [10] describes a life-cycle evaluation of several passenger cars propelled by distinct fuel types to comprehensively assess the fuel consumption as well as environmental impact aspects, i.e. internal combustion engine vehicles (fueled by gasoline, diesel, liquefied petroleum gas, methanol, compressed natural gas, hydrogen and ammonia), hybrid electric vehicles (fueled by 50% gasoline and 50% electricity) and electric vehicles. Whereas Polcar et al. in the publication [11] emphasize the most significant findings concerning a calibration and its utilization in fuel consumption measurement by using the CAN-Bus network, Skrucany et al. assess distinctions in vehicle fuel consumption when installing certain aerodynamical devices through specific telematics SW [12].
3 Data and methods – methodology for quantifying fuel consumption
In order to achieve the mentioned research objective, i.e. to find out and determine the most accurate manner in which fuel consumption is to be measured by passenger car users under normal conditions, a specific methodology has been chosen including the following basic steps:
Selection of a motor vehicle;
Selection of measured routes (circular routes);
Use of a measuring technique;
Determination of a measurement procedure.
Petrol (gasoline) and diesel are the most widely used sources of energy worldwide for internal combustion engines [13]. A vehicle powered by compression ignition engine using diesel was selected for the intended examination. The following statistics (Table 1) provide more detailed specifications of the vehicle being tested.
Specifications of vehicle used for measurements
Specifications | Vehicle | Vehicle with compression ignition engine |
---|---|---|
Type | Passenger car | |
Category | M1 | |
Make | CITROEN | |
Trade name | BERLINGO | |
Cylinder displacement | 1,560 | |
Maximum engine power | 88 kW | |
Fuel type | Diesel + adblue | |
Transmission | M5 | |
Production year | 2016 | |
Drive | Front-wheel | |
Curb weight | 1,630 kg | |
Fuel consumption | Urban | 4.9 l×100 km−1 |
Extra-urban | 4.2 l×100 km−1 | |
Combined | 4.4 l×100 km−1 |
Four routes were designated to quantify measurement results to be as accurate as possible, with all of the routes starting and finishing at a certain fuel station (thereinafter referred to as FS), where all measurements were also performed [14]. The following table (Table 2) shows parameters of individual routes.
Specifications of designated routes
Parameters | Routes | Short urban route | Long urban route | Short combined[*] route | Long combined[*] route |
---|---|---|---|---|---|
Length | 3.9 km | 8.8 km | 32.5 km | 61.7 km | |
Maximum permitted speed | 50–70 km×h−1 | 50–80 km×h−1 | 50–130 km×h−1 | 50–130 km×h−1 | |
Number of intersections with | 2 | 1 | 1 | - | |
significant waiting time | |||||
City : Motorway ratio | - | - | 1:1 | 1:3 |
In regard to the research, it is mandatory to use such measurement techniques that would enable one to quantify the exact amounts of fuel tanked up from a dispenser at the FS and then calculate deviations from particular values indicated on the dispenser [14]. The particular investigating techniques are specified in the following table (Table 3).
Specifications of measurement techniques
Measurement technique | Purpose | Range of measurement | Accuracy of measurement |
---|---|---|---|
Kern FKB Digital scale | fuel weight measurement | 36,100g | 0.1g |
Densitometers | dispensed fuel density measurement | 650 – 770 kg×m−3 | |
760 – 880 kg×m−3 | |||
Jerrycans | dispensed fuel | 5 l, 10 l and 20 l | |
Fuel tank filler neck – modified | stimulation of pump gun location | - | - |
Wireless adapters ELM 327 and V-Car II | fuel consumption of vehicle being examined | - | - |
Mobile applications Torque Pro and OBD FUSION |
Prior to the actual fuel consumption examination, deviation measurements were conducted, namely as follows [15]:
Determination of deviations of the amount of dispensed fuel at the FS in relation to the actually measured amount.
Determination of deviations of the amount of dispensed fuel during automatic pump gun shutting off.
All measurements were taken on the same pump stand and with the same pump gun to eliminate deviations caused by the use of various pump guns.
Measurement preparation and the consumption investigation as such followed next.
Detection of the initial level of fuel in a related fuel tank – i.e. the level after the first automatic pump gun shut-off.
Reset of values displayed on available on-board computer and the mobile phone application.
Recording of values of average consumption and traveled distance from the on-board computer.
Recording of values of average consumption and traveled distance from the application.
Subtraction of refueled amount for calculating fuel consumption displayed on the pump stand.
Each measurement was carried out 3 times on all routes that were completed in different driving styles (economical, regular and dynamic). As for the first style, the vehicle picked up speed slowly and was driven at lower, constant, speeds. Before its stopping, for example, at an intersection, rundown of the vehicle was used. During the dynamic style, however, the vehicle was driven inconstantly, higher gears were put in at higher revs than usual and the measurements were taken at higher speeds. Aswell as that, the vehicle was not run down, but braking in front of an obstacle was sharp.
The second measurement was conducted in a manner where the initial level of fuel was not seen as the first pump gun shut-off, but the fuel was refilled almost to the brim of the fuel tank filler neck (up to a protrusion in the neck serving as a control mark). The measurement was performed once on each of the routes in the regular style.
4 Obtained results
Prior to commencing the measurements, a standard value of fuel consumption was calculated and assigned as a starting value, on the basis of which other types of consumption measurements were then compared. This implies the necessity for the most accurate determination of the amount of consumed fuel based on calculations from the measured values at the FS and the size of possible deviations related to the calculations [16].
4.1 Investigation of accuracy of the amount of dispensed fuel
The first examination was used to determine accuracy of dispensed fuel at the FS, where the maximum permissible error (i.e. the permissible difference between the displayed and the actual fuel volume) is of +/− 0.5 % of the dispensed quantity. In practice, that accounts for +/− 0.1 liters when filling 20 liters of fuel [17].
The aforementioned measurement was carried out by recording weights of fuel from laboratory scales and amounts of fuel dispensed at the FS, which were subsequently compared with calculated values based on the measured weight and fuel density [18]. During the measurement, the fuel temperature was of 8∘C and the diesel density was measured at 845 kg×m−3.Measured deviations are given in the following table (Table 4).
Accuracy of amounts of dispensed fuel – diesel
Diesel | Amount [l] | Weight [g] | V [m3] | Calculated amount [l] | Deviation [l] | Deviation [%] |
---|---|---|---|---|---|---|
4.83 | 4,060.5 | 0.004805325 | 4.805325444 | 0.0247 | 0.51 | |
10.23 | 8,573.6 | 0.010146272 | 10.14627219 | 0.0837 | 0.83 | |
4.92 | 4,125.3 | 0.004882012 | 4.882011834 | 0.0380 | 0.78 | |
10.26 | 8,608.8 | 0.010187929 | 10.18792899 | 0.0721 | 0.71 | |
4.94 | 4,136 | 0.004894675 | 4.894674556 | 0.0453 | 0.93 | |
7.32 | 6,140.3 | 0.007266627 | 7.266627219 | 0.0534 | 0.73 | |
Mean deviation | 0.0529 | 0.75 |
4.2 Investigation of deviations of the amount of dispensed fuel during automatic pump gun shutting off
The second examination was aimed at determining deviations of pumped fuel caused by the pump gun shutting off. In order to do so, Škoda Felícia fuel tank filler neck was modified and used to capture a particular position in the tested vehicle as best as possible and also to keep the same position during the measurement. Results are shown in the following table (Table 5).
Results of deviation measurement during automatic pump gun shutting off
Order of measurement | 1 | 2 | 3 | 4 | 5 | 6 | 7 | 8 | 9 |
---|---|---|---|---|---|---|---|---|---|
Refueled amount [l] | 0.91 | 0.87 | 0.93 | 0.92 | 0.89 | 0.9 | 0.91 | 0.88 | 0.94 |
Following the measured values, an average value of 0.9055 l was then determined. Next, a standard deviation of 0.02297 l was quantified. The values also show that (in certain cases) a deviation caused by the pump gun shutting off may take a value of 0.07 l of fuel.
4.3 First investigation of fuel consumption related to compression ignition engine in passenger car
At the beginning of the first measurement, fuel was refilled until the first pump gun is shut off. The actual measurement started by resetting the previously measured values of fuel consumption and traveled distance in the on-board computer as well as the specific application. The routes were driven gradually in an economical style, a regular style and a dynamic style [19].
The very measurement was performed on Citroen Berlingo with turbocharged diesel engine. Standard On-Board computer (hereinafter referred to as PC), as well as the OBD Fusion application along with the V-Car II Wifi adapter were used for the very comparison purpose. To reach higher measurement accuracy, only recorded data in the form of an Excel file were drawn from the application, including the length of intervals between individual records, the amount of fuel consumed over that period and the traveled distance [20]. Based on such values, the total fuel consumed on a given route as well as the average consumption may be easily calculated. Results of the first investigation are summarized in the following table (Table 6).
Results of the first investigation of fuel consumption related to compression ignition engine
No. | Route* | Driving style* | Distance – PC [km] | Distance – OBD FUSION [km] | Consumpt. – PC [l×100km−1] | Consumpt. – OBD FUSION [l×100km−1] | Refueled amount [l] | Calculated consumpt. [l×100km−1] |
---|---|---|---|---|---|---|---|---|
1 | 1 | Economical | 3 | 4 | 5.0 | 5.81 | 1.14 | 38.000 |
2 | 1 | Regular | 3 | 4.01 | 5.4 | 6.71 | 0.88 | 29.333 |
3 | 1 | Dynamic | 3 | 4 | 7.1 | 9.49 | 0.88 | 29.333 |
4 | 2 | Economical | 9 | 9.06 | 4.1 | 4.38 | 0.5 | 5.556 |
5 | 2 | Regular | 9 | 9.05 | 5.1 | 6.17 | 0.65 | 7.222 |
6 | 2 | Dynamic | 9 | 9.05 | 6.8 | 8.27 | 0.6 | 6.667 |
7 | 3 | Economical | 32 | 32.32 | 4.6 | 5.05 | 2.1 | 6.563 |
8 | 3 | Regular | 32 | 32.72 | 6.2 | 7.4 | 2.61 | 8.156 |
9 | 3 | Dynamic | 32 | 32.71 | 6.9 | 8.69 | 2.55 | 7.969 |
10 | 4 | Economical | 59 | 59.82 | 5.2 | 5,73 | 2.91 | 4.932 |
11 | 4 | Regular | 59 | 59.75 | 6.2 | 7.3 | 3.49 | 5.915 |
12 | 4 | Dynamic | 59 | 60.51 | 7.6 | 9.61 | 4.6 | 7.797 |
*1 – Short urban route; 2 – Long urban route; 3 – Short combined route; 4 – Long combined route
4.4 Second investigation of fuel consumption related to compression ignition engine in passenger car
Considering preliminary results from the first measurement, it was clear that measuring a relatively accurate amount of consumed fuel would not be an easy task. As large inaccuracies arose, particularly in connection with the shortest route measurements, a second method of determining fuel consumption was applied. Prior to each measurement, the vehicle was refueled up to a point visible with the naked eye, i.e. just below the fuel tank lid [21]. Results of the second investigation are listed in the following table (Table 7).
Results of the second investigation of fuel consumption related to compression ignition engine
No. | Route* | Driving style | Distance – PC [km] | Distance – OBD FUSION [km] | Consumpt. – PC [l×100km−1] | Consumpt. – OBD FUSION [l×100km−1] | Refueled amount [l] | Calculated consumpt. [l×100km−1] |
---|---|---|---|---|---|---|---|---|
1 | 1 | Regular | 3 | 4.02 | 6.1 | 7.05 | 0.79 | 26.33 |
2 | 2 | Regular | 9 | 9.07 | 4.8 | 5.38 | 0.75 | 8.33 |
3 | 3 | Regular | 32 | 32.67 | 6.2 | 7.53 | 2.20 | 6.88 |
4 | 4 | Regular | 59 | 59.43 | 7.2 | 8.81 | 4.54 | 7.69 |
1 – Short urban route; 2 – Long urban route; 3 – Short combined route; 4 – Long combined route
5 Discussion
The following section (divided into three parts) provides tabular and graphical representation involving comparisons of measured values by individual measurement methods, fuel consumption deviations as well as mean values and standard deviations of the methods. Evaluations of obtained results of the first measurement and the second measurement are presented in the first two parts, whereas the third part includes recommendations and procedures for the most accurate measurement of fuel consumption of compression ignition engine of passenger cars by their users in specific conditions [22].
5.1 Evaluation of the first investigation results being achieved
The graph below (Figure 1) illustrates a particular comparison of resulting values of the first measurement. Here, it is possible to observe significant inaccuracies of calculated fuel consumption in relation to the shortest route. The main causes consist in fuel foaming, which (diesel) has an even more significant effect, and the pump gun shutting off deviations.

Comparison of the first measurement values
As may be seen, the average difference between the consumption calculated on the basis of the OBD Fusion application and the calculated consumption is only 0.51%. Thus, the lowest deviation from the calculated value is again the figure calculated from the OBD Fusion application data. Even in this case, the deviation is to be increased by the accuracy of dispensed fuel (diesel, it represents a value of 0.75%), and it is also necessary to account for the deviation of pump gun shutting off, which is in the range of +/− 0.023 l. As a result, the calculated consumption deviation from the application will be approximately 1.26% +/− 0.023 l. Deviation values of the first investigation are given in the following table (Table 8).
Comparison of the first investigation deviation values in relation to fuel consumption of compression ignition engine
No. | Deviation (PC and calculated) [l] | Deviation (OBD FUSION and calculated) [l] | Deviation (PC and OBD FUSION) [l] | Deviation (PC and OBD FUSION) [%] | Deviation (OBD FUSION and calculated) [%] | Deviation (PC and calculated) [%] |
---|---|---|---|---|---|---|
1 | −33.00 | −32.19 | −0.81 | −16.20 | −554.04 | −660.00 |
2 | −23.93 | −22.62 | −1.31 | −24.26 | −337.16 | −443.21 |
3 | −22.23 | −19.84 | −2.39 | −33.66 | −209.10 | −313.15 |
4 | −1.46 | −1.18 | −0.28 | −6.83 | −26.84 | −35.50 |
5 | −2.12 | −1.05 | −1.07 | −20.98 | −17.05 | −41.61 |
6 | 0.13 | 1.60 | −1.47 | −21.62 | 19.39 | 1.96 |
7 | −1.96 | −1.51 | −0.45 | −9.78 | −29.95 | −42.66 |
8 | −1.96 | −0.76 | −1.20 | −19.35 | −10.22 | −31.55 |
9 | −1.07 | 0.72 | −1.79 | −25.94 | 8.30 | −15.49 |
10 | 0.27 | 0.80 | −0.53 | −10.19 | 13.92 | 5.15 |
11 | 0.28 | 1.38 | −1.10 | −17.74 | 18.97 | 4.59 |
12 | −0.20 | 1.81 | −2.01 | −26.45 | 18.87 | −2.59 |
Ø | −7.27 | −6.07 | −1.20 | −19.42 | −92.08 | −131.17 |
Ø | −0.90 | 0.20 | −1.10 | −17.65 | −0.51 | −17.52 |
without | ||||||
1 route |
The following evaluation of results was done without consideration of the 1st route measurement. The 1st route measurement is represented by the numbers of rows 1, 2 and 3. There are considerable deviations (calculated results and other two methods). This fact was caused by pushing a certain amount of fuel through the tank lid when refueling to the full tank volume. This phenomenon is most seen when short-distance measuring routes.
The following graph (Figure 2) captures mean values of the individually measured routes and their standard deviations processed for the methods used in the first measurement.

Comparison of values obtained during the first measurement method
5.2 Evaluation of the second investigation results being achieved
The graph below (Figure 3) illustrates a particular comparison of measured values of the second measurement.

Comparison of the second measurement values
Measured values of deviation of the second measurement are listed in the following table (Table 9).
Comparison of the second investigation deviation values in relation to fuel consumption of compression ignition engine
No. | Route* | Deviation (PC and calculated) [l] | Deviation (OBD FUSION and calculated) [l] | Deviation (PC and OBD FUSION) [l] | Deviation (PC and OBD FUSION) [%] | Deviation (OBD FUSION and calculated) [%] | Deviation (PC and calculated) [%] |
---|---|---|---|---|---|---|---|
1 | 1 | −20.23 | −19.28 | −0.95 | −15.57 | −273.52 | −331.69 |
2 | 2 | −3.53 | −2.95 | −0.58 | −12.08 | −54.89 | −73.61 |
3 | 3 | −0.68 | 0.65 | −1.33 | −21.45 | 8.70 | −10.89 |
4 | 4 | −0.49 | 1.12 | −1.61 | −22.36 | 12.66 | −6.87 |
Ø | −6.23 | −5.12 | −1.12 | −17.87 | −76.77 | −105.77 | |
Ø | −1.57 | −0.39 | −1.17 | −18.63 | −11.18 | −30.46 | |
without | |||||||
1 route |
The following evaluation of results was done without taking into consideration the 1st route measurement for the same reason as the results from the first measurement (see comments below summarized in the Table 8).
In light with the above comparison, a lower deviation was obtained when using the OBD Fusion application (11.18%). Along with the increase in the dispensed fuel accuracy (with a value of 0.75% for diesel), it represents a deviation of approximately 11.93%.
The following graph (Figure 4) captures mean values of the individually measured routes and their standard deviations processed for the methods used while the second measurement.

Comparison of values obtained during the second measurement method
Furthermore, the dependence of the OBD Fusion deviation value on the distance traveled was determined. The equation representing this dependence has the form as follows: y = 111.12 × −2.928 (see Figure 5).

Influence of distance traveled on measured deviations – OBD Fusion
Lastly, an equation referring the dependence of the on-board computer (PC) deviation value on the distance traveled was quantified (see Figure 6). The equation representing this dependence has the form as follows: y = 89.477x − 2.798.

Influence of distance traveled on measured deviations – PC
In Figure 7 below, it can be seen that at some point, the on-board computer achieves lower deviation values compared to the OBD Fusion application, but these values are more stable with less variation of values. If the values of the shortest route are not taken into account, the equation expressing the influence of the consumed fuel amount on the OBD Fusion deviation value has the form: y = −0.0369x3 + 0.8856x2 − 6.6136x + 18.503 and an equation specifying the influence of the amount of fuel consumed on the on-board computer deviation value has the form as follows: y = −0.1842x3+3.1854x2−17.32x + 32.436.

Influence of the amount of fuel consumed on measured deviations without taking into account the shortest route – PC and OBD Fusion
The following figure (see Figure 8) presents the influence of the distance traveled on the deviation value of the on-board computer and OBD Fusion application without respecting the shortest route.

Influence of the distance traveled on measured deviations without respecting the shortest route – PC and OBD Fusion
In this scenario, when omitting the values of the shortest route, it is again possible to see how the magnitude of the mean deviation decreases with increasing number of kilometers traveled.
5.3 Recommendations and determination of procedure for measuring fuel consumption
To determine the exact fuel consumption, the authors would recommend the first of the measurement methods from the results being obtained. This is because the accuracy of applying this method is higher than the second one, as well as because the deviation caused by switching off the pump gun can be taken into account when calculating the very accuracy.
Furthermore, results of the investigation and above compiled charts and tables confirm that the greater the amount of fuel consumed and the longer the route section being examined, the lower the deviations among individual measurements. The authors certainly do not recommend conducting relevant experiments on a route shorter than 30 km, since measurements themselves at shorter distances are very inaccurate (see Figure 5 and 6). Therefore, for the most accurate calculation, the authors recommend the longest possible route section being examined.
With regard to the results of investigation, the accuracy of approx. 9% can be demonstrated in the interval up to a distance of 8.8 km. At distances of 30 km and more, the accuracy is about 4%, while long-term is at the value of 5.18% as for the OBD Fusion application and of 5.27% as for on-board computer.
In addition, the results of measurements show that the deviation value when consumption of about 1 liter of fuel is at the level of about 12% as for the OBD Fusion application and up to 15% as for on-board computers. More accurate and constant values of deviations at the level of about 1.5-4% can be achieved by consuming at least 3 liters of fuel.
6 Conclusion
The main objective of this research work was to find out how to determine fuel consumption of compression ignition engine in passenger cars as accurately as possible from their users’ perspective. The paper looked into the implementation and evaluation of fuel consumption quantifications by calculation methods, recording data from the above mentioned on-board computer and the specific mobile phone application called OBD Fusion.
The very examinations were performed on Citroen Berlingo motor vehicle and consisted of the following parts: selection of vehicles, designation of routes to be driven, description of used measuring technique, determination of proposed measurement procedure, measurements of accuracy of the amount of dispensed fuel as well as deviations of the amount of dispensed fuel during automatic pump gun shutting off, application of the first measurement method and subsequently the second measurement method related to searching for initial fuel levels during the examinations. To meet the objective, a certain degree of deviation on investigating the discussed fuel consumption was determined.
Based on the conducted experiments and their findings, the most accurate method related to quantifying the fuel consumption from the user’s standpoint has been established, and the specific methodology of such investigationwhen defining four measurement stages has been proposed.
Acknowledgement
This manuscript was supported within solving the research project entitled “Autonomous mobility in the context of regional development LTC19009” of the INTER-EXCELLENCE program, the VES 19 INTERCOST subprogram.
and
Project VEGA No. 1/0128/20: Research on the Economic Efficiency of Variant Transport Modes in the Car Transport in the Slovak Republic with Emphasis on Sustainability and Environmental Impact, Faculty of Operation and Economics of Transport and Communications: University of Zilina, 2020-2022.
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© 2021 T. Skrúcaný et al., published by De Gruyter
This work is licensed under the Creative Commons Attribution 4.0 International License.
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- Electrochemical studies of the synergistic combination effect of thymus mastichina and illicium verum essential oil extracts on the corrosion inhibition of low carbon steel in dilute acid solution
- Adoption of Business Intelligence to Support Cost Accounting Based Financial Systems — Case Study of XYZ Company
- Techno-Economic Feasibility Analysis of a Hybrid Renewable Energy Supply Options for University Buildings in Saudi Arabia
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- Development of Smartphone-Controlled Hand and Arm Exoskeleton for Persons with Disability
- Investigation of saturation flow rate using video camera at signalized intersections in Jordan
- The features of Ni2MnIn polycrystalline Heusler alloy thin films formation by pulsed laser deposition
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- Development of Solar-Powered Water Pump with 3D Printed Impeller
- Identifying Innovative Reliable Criteria Governing the Selection of Infrastructures Construction Project Delivery Systems
- Kinetics of Carbothermal Reduction Process of Different Size Phosphate Rocks
- Plastic forming processes of transverse non-homogeneous composite metallic sheets
- Accelerated aging of WPCs Based on Polypropylene and Birch plywood Sanding Dust
- Effect of water flow and depth on fatigue crack growth rate of underwater wet welded low carbon steel SS400
- Non-invasive attempts to extinguish flames with the use of high-power acoustic extinguisher
- Filament wound composite fatigue mechanisms investigated with full field DIC strain monitoring
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- A SIMULINK implementation of a vector shift relay with distributed synchronous generator for engineering classes
- Evaluation of measurement uncertainty in a static tensile test
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- Comparison between two calculation methods for designing a stand-alone PV system according to Mosul city basemap
- Reduction of transport-related air pollution. A case study based on the impact of the COVID-19 pandemic on the level of NOx emissions in the city of Krakow
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- UAVs in rail damage image diagnostics supported by deep-learning networks
- Exhaust emissions of buses LNG and Diesel in RDE tests
- Measurements of urban traffic parameters before and after road reconstruction
- The use of deep recurrent neural networks to predict performance of photovoltaic system for charging electric vehicles
- Analysis of dangers in the operation of city buses at the intersections
- Psychological factors of the transfer of control in an automated vehicle
- Testing and evaluation of cold-start emissions from a gasoline engine in RDE test at two different ambient temperatures
- Age and experience in driving a vehicle and psychomotor skills in the context of automation
- Consumption of gasoline in vehicles equipped with an LPG retrofit system in real driving conditions
- Laboratory studies of the influence of the working position of the passenger vehicle air suspension on the vibration comfort of children transported in the child restraint system
- Route optimization for city cleaning vehicle
- Efficiency of electric vehicle interior heating systems at low ambient temperatures
- Model-based imputation of sound level data at thoroughfare using computational intelligence
- Research on the combustion process in the Fiat 1.3 Multijet engine fueled with rapeseed methyl esters
- Overview of the method and state of hydrogenization of road transport in the world and the resulting development prospects in Poland
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- Special Issue: Terotechnology 2019 - Part II
- DOE Application for Analysis of Tribological Properties of the Al2O3/IF-WS2 Surface Layers
- The effect of the impurities spaces on the quality of structural steel working at variable loads
- Prediction of the parameters and the hot open die elongation forging process on an 80 MN hydraulic press
- Special Issue: AEVEC 2020
- Vocational Student's Attitude and Response Towards Experiential Learning in Mechanical Engineering
- Virtual Laboratory to Support a Practical Learning of Micro Power Generation in Indonesian Vocational High Schools
- The impacts of mediating the work environment on the mode choice in work trips
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- Special Issue: Annual Engineering and Vocational Education Conference - Part II
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- Special Issue: ICIMECE 2020 - Part I
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- Prediction Model of Magnetorheological (MR) Fluid Damper Hysteresis Loop using Extreme Learning Machine Algorithm
- A review on the fused deposition modeling (FDM) 3D printing: Filament processing, materials, and printing parameters
- Facile rheological route method for LiFePO4/C cathode material production
- Mosque design strategy for energy and water saving
- Epoxy resins thermosetting for mechanical engineering
- Estimating the potential of wind energy resources using Weibull parameters: A case study of the coastline region of Dar es Salaam, Tanzania
- Special Issue: CIRMARE 2020
- New trends in visual inspection of buildings and structures: Study for the use of drones
- Special Issue: ISERT 2021
- Alleviate the contending issues in network operating system courses: Psychomotor and troubleshooting skill development with Raspberry Pi
- Special Issue: Actual Trends in Logistics and Industrial Engineering - Part II
- The Physical Internet: A means towards achieving global logistics sustainability
- Special Issue: Modern Scientific Problems in Civil Engineering - Part I
- Construction work cost and duration analysis with the use of agent-based modelling and simulation
- Corrosion rate measurement for steel sheets of a fuel tank shell being in service
- The influence of external environment on workers on scaffolding illustrated by UTCI
- Allocation of risk factors for geodetic tasks in construction schedules
- Pedestrian fatality risk as a function of tram impact speed
- Technological and organizational problems in the construction of the radiation shielding concrete and suggestions to solve: A case study
- Finite element analysis of train speed effect on dynamic response of steel bridge
- New approach to analysis of railway track dynamics – Rail head vibrations
- Special Issue: Trends in Logistics and Production for the 21st Century - Part I
- Design of production lines and logistic flows in production
- The planning process of transport tasks for autonomous vans
- Modeling of the two shuttle box system within the internal logistics system using simulation software
- Implementation of the logistics train in the intralogistics system: A case study
- Assessment of investment in electric buses: A case study of a public transport company
- Assessment of a robot base production using CAM programming for the FANUC control system
- Proposal for the flow of material and adjustments to the storage system of an external service provider
- The use of numerical analysis of the injection process to select the material for the injection molding
- Economic aspect of combined transport
- Solution of a production process with the application of simulation: A case study
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- Utilization of intelligent vehicle units for train set dispatching
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- LVRT enhancement of DFIG-driven wind system using feed-forward neuro-sliding mode control
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