Research and Analysis of the Real-Time Interaction Between Performance and Smoke Emission of a Diesel Vehicle ^†

Abstract

In recent decades, environmental requirements for reducing the toxic components emitted from vehicle exhausts have decreased drastically. Technologies for after-treatment of diesel vehicle emissions are being improved continuously in order to meet increasingly stringent regulations. Passenger cars are a significant source of air pollution, especially in urban areas. The EU has decided to phase out internal combustion engines. Stricter Real Driving Emissions (RDE) testing procedures have also been introduced, aiming to assess the emissions of nitrogen oxides (NO_x) and particle number (PN). The present work investigates the interaction between performance and smoke emissions of a diesel vehicle on a pre-established route in an urban environment with an everyday (normal) driving style. The results showed that when the vehicle is technically sound and meets its technical specifications, smoke emissions are within normal limits.

1. Introduction

Increasing levels of motorization worldwide are inevitable, which in turn leads to heavier traffic, congestion and problems in traffic management, organization and safety. The increase in traffic has a number of negative effects: a decrease in the efficiency of the transport process and loss of time for all road users [1] (drivers and passengers) and for freight users [2], and as a result we have greater financial and economic damage [3], as well as problems related to road traffic injuries, the environment and climate change on Earth [4,5,6,7]. Congestion leads to increased fuel costs and increases emissions of toxic products due to increased idling, acceleration and braking [8,9,10]. As a result of idling, frequent acceleration and braking, we have increased wear and tear and a reduction in the service life of vehicles, as well as more frequent repairs and replacement of elements and parts of vehicles. The development of technical operations as a subsystem of road transport is determined by its rapid development, the need to save fuel, labor and materials, and the need to ensure reliable operation of vehicles [11]. The requirements for vehicle reliability are increasing in connection with the increase in the intensity and speed of movement and the increase in their power and environmental performance [12,13]. The increase in the number of vehicles and their aging significantly complicates the problems of maintaining the vehicle fleet. In order to solve the main tasks of technical operation, it is necessary to study the patterns of changes in the technical condition of the vehicle under the influence of various factors in the process of operation [13]. Knowledge of these patterns makes it possible to develop scientific methods for maintaining vehicles in good technical condition [14]. These methods are based on the use of mathematical statistics, probability theory, reliability theory and diagnostics [15].

The aim of this study was to investigate and analyze the parameters of a car with a diesel internal combustion engine in real operating conditions, paying particular attention to the smoke emissions. The study was conducted on a pre-established route in an urban environment with a daily (normal) driving style. In the study, we used an 18-year-old passenger car with a mileage of 151,000 km, environmental standard Euro 4. The results of the study showed that in terms of smoke emission in the exhaust gases, the car meets the standard for which it was approved. Of fundamental importance for this was the correct technical maintenance, service, storage and repair of the car.

2. Materials and Methods

The ever-increasing intensity of automobile traffic worsens the overall environmental performance of cars and their impact on the environment, traffic safety and quality of life in urban areas with intensive traffic [16,17,18,19]. In these areas, there is a lot of starting and stopping of vehicles, which increases the share of harmful substances released into the environment [20,21,22]. Studies show that air pollution causes many diseases, which leads to a deterioration in the quality of life of people and visitors to developed megacities, which is also expressed in increased healthcare costs and reduced productivity [23,24,25,26]. Emissions of pollutants from motor vehicles arise from the vehicle itself—fine particulate matter [27,28,29]—and from the combustion of fuel in the engine, which is subsequently released into the atmosphere through the exhaust pipe’s CO, CO₂, HC, NO_x and PM [29,30]. Under optimal combustion conditions, oxygen reacts with the hydrocarbon fuel to produce CO₂ and H₂O [31,32]. However, incomplete combustion occurs when there is not enough oxygen, leading to the generation of products such as carbon monoxide (CO) and hydrocarbon (HC), also precursors of PM [33]. In addition, the air in the combustion process is not pure oxygen, but contains some nitrogen (N₂). Nitrogen is oxidized at high temperatures, forming thermal NO_x [34], mainly from nitric oxide (NO) [35], with a small amount of nitrogen dioxide (NO₂) [36,37]. Thus, this study focuses on the real-time interaction between the performance and smoke emissions of a diesel vehicle. Their establishment was achieved by integrating modern methods and tools for determining, controlling and regulating exhaust emissions and monitoring some of the main parameters of the internal combustion engine. The application of this approach allowed us to assess the interaction between the performance and smoke emissions of a diesel vehicle. The analysis of this information will serve as a basis for making important management decisions related to improving the environmental performance of vehicles.

3. Results and Discussion

The study was conducted along a predetermined route (Figure 1) in an urban environment with a daily relaxed (normal) driving style, with normal traffic intensity. The distance traveled during the study was 8 km, observing all regulatory restrictions regarding the movement of cars in an urban environment, and the time for which the study was conducted was 15 min.

To test the interaction between the performance and smoke emissions of a diesel vehicle, a Skoda Fabia passenger car with a mileage of 151,000 km that is 18 years old was used. The technical characteristics of the car are presented in

Table 1. Vehicle technical data [38].

Model:	Skoda Fabia II 1.4 TDI
Body style:	passenger car
Production period:	2007 January 2010 Mart
Engine:	1422 cm3 Diesel
Power:	69 HP on 4000 min−1
Torque:	155 Nm on 1600–2800 min−1
Gearbox:	Manual gearbox (5 gears)
Drive type:	Front wheel drive (FWD)
Maximum speed:	163
Acceleration 0–100 km/h:	14.8 s
Fuel consumption (l/100 km):	4.8 (combined) 6.0 (urban) 4.2 (highway)
Fuel tank capacity:	45 L
Car dimensions:	4.92 m (length) 1.64 m (width) 1.50 m (height)
Gross weight:	1125 kg

[38]. The vehicle is a laboratory vehicle of the “Organization and Management of Road Transport” at the Technical University of Sofia, serviced, maintained, repaired and stored only according to the manufacturer’s instructions, with the relevant spare parts and fuels and lubricants. The vehicle is equipped with specialized equipment, including GPS position tracking and a system for monitoring acceleration, speed and distance traveled.

To measure the smoke content in the exhaust gases, a smoke meter from the company BrainBee [39] was used. For the reliability of the measurement results, the smoke meter passed a control test by an accredited service for conducting metrological measurements. The equipment used is designed to conduct measurements in stationary and mobile conditions. For monitoring and control of the main parameters of the diesel engine operating mode, diagnostic equipment from the company Texa Navigator [40] was used.

Table 2. Research equipment [39,40].

Model:	BrainBee OPA-100
Light transmission	0 ÷ 99.9%
Light transmission	0 ÷ 9.99 M−1
Rev counter	300 ÷ 9990 RPM heat
Oil Temperature	20 ÷ 150 °C
Smoke temp.	20 ÷ 400 °C
Model:	NAVIGATOR TXTS
Processor	Cortex m3 stm32f103zg 72 mhz, flash 1024 kbytes, sram 96 kbytes
Memory	SRAM memory: 8 MBits organized 512 KBytes x 16 bits,
Vehicle Battery	12 VDC and 24 VDC
Communication	virtual RS232 via USB 2.0 Device
Wireless Connection	Bluetooth class1
Supported protocols	Blink codes, ISO9141-2, ISO14230; CAN ISO11898-2, ISO11898-3 SAE J1850 PWM SAE J1850 VPW SAE J2534-1
Regulations	Directive: 1999/5/EC Safety: EN 60950
Environmental	Operating temperature: 0 ÷ 50 °C Stocking temperature: −20 ÷ 60 °C Operating moisture: 10 ÷ 80%

presents the technical characteristics of the equipment used during the study.

The indicators that were monitored with the smoke meter were engine speed, oil temperature, and percentage of smoke in the exhaust gases. The diagnostic equipment for monitoring engine operating modes measured speed, coolant temperature, engine fuel consumption and driving torque. The battery voltage was monitored but was not taken into account, since the health of the alternator and battery was previously determined. Figure 2 presents information on the monitored engine parameters and smoke.

A short sample of the parameters of the vehicle, the diagnostic equipment and the opacimeter tested is presented in

Table 3. Research indicators of diesel internal combustion engines.

	OBD DIAGNOSTICS					SMOKEMETER
1	840.00	92.70	0.00	6.04	0.60	850.00	0.12	97.00
2	840.00	92.70	3.00	9.06	1.00	850.00	0.13	97.00
47	3528.00	93.60	28.00	38.16	12.80	3060.00	0.82	98.00
48	2415.00	93.60	28.00	36.51	8.90	2680.00	0.61	99.00
423	1680.00	88.20	25.00	28.27	4.60	1620.00	0.89	96.00
424	2331.00	88.20	35.00	36.51	8.80	1800.00	0.82	96.00
894	840.00	81.00	0.00	6.04	0.60	830.00	0.00	94.00
895	840.00	81.00	0.00	6.04	0.60	840.00	0.00	94.00

. The tested indicators have a small shift due to the specificity of the information and the frequency of measurement of the equipment used.

One of the most commonly used methods for determining the relationship between variables influencing each other is the statistical method, which uses information obtained from observing certain parameters in real conditions. The main disadvantage of the statistical method is the collection of reliable information. With the correct methodology of data collection and information processing, reliable models for the interacting variables are obtained. To check the covariance of the studied variables, the statistical software Statgraphics Centurion 18, Version 18.1.12 was used.

The aim of the study was to establish patterns of interrelationships and to determine the influence between the performance and smoke emissions of a passenger car with a diesel engine. Initially, a multivariate statistical analysis (MVA) was performed covering seven observed variables simultaneously in order to study and measure relationships between the variables. In total, 895 complete cases were used in the analysis for use in the calculations, and this procedure aimed to calculate various statistics, including correlations, covariance and partial correlations, in order to reveal the most appropriate procedure for building a statistical model of our data. Summary statistics from the input data in the statistical product are presented in

Table 4. Summary statistics of the studied data variables.

	PM	Col.temp	Consum	Driv.troq	Eng.sped	Oil.temp	Veh.sped
Count	895	895	895	895	895	895	895
Average	0.259575	88.1606	1.8838	11.9899	1281.7	97.3743	27.038
Standard deviation	0.422079	3.34285	2.30589	10.7943	480.288	1.84391	14.5105
Coeff. of variation	162.604%	3.79178%	122.406%	90.0283%	37.4727%	1.89%	53.67%
Minimum	0	80.1	0	0	756.0	93	0
Maximum	2.33	97.3	15.5	38.71	3885.0	102	64
Range	2.33	17.2	15.5	38.71	3129.0	9	64
Stnd. skewness	26.7981	1.1802	26.014	8.72595	19.4317	3.23857	−1.18691
Stnd. kurtosis	27.3739	0.357881	39.2148	−4.01327	24.0763	2.80767	−4.69859

. This table shows summary statistics for each of the selected data variables. This includes measures of central tendency, measures of variability and measures of shape. Of particular interest here are the standardized skewness and standardized kurtosis, which can be used to determine whether the sample comes from a normal distribution.

The next step in data analysis was to perform a correlation analysis of the data in order to determine the statistical relationship between the variables.

Table 5. Correlations.

	PM	Col.temp	Consum	Driv.troq	Eng.sped	Oil.temp	Veh.sped
PM		0.2072	0.5500	0.5557	0.3763	0.2631	0.1868
		(895)	(895)	(895)	(895)	(895)	(895)
		0.0000	0.0000	0.0000	0.0000	0.0000	0.0000
Col.temp	0.2072		0.1261	0.1344	0.0206	0.8192	−0.0588
	(895)		(895)	(895)	(895)	(895)	(895)
	0.0000		0.0001	0.0001	0.5379	0.0000	0.0781
Consum	0.5500	0.1261		0.8854	0.6576	0.0586	0.1517
	(895)	(895)		(895)	(895)	(895)	(895)
	0.0000	0.0001		0.0000	0.0000	0.0793	0.0000
Driv.troq	0.5557	0.1344	0.8854		0.4095	0.0650	0.0467
	(895)	(895)	(895)		(895)	(895)	(895)
	0.0000	0.0001	0.0000		0.0000	0.0514	0.1623
Eng.sped	0.3763	0.0206	0.6576	0.4095		−0.0028	0.4650
	(895)	(895)	(895)	(895)		(895)	(895)
	0.0000	0.5379	0.0000	0.0000		0.9335	0.0000
Oil.temp	0.2631	0.8192	0.0586	0.0650	−0.0028		0.0996
	(895)	(895)	(895)	(895)	(895)		(895)
	0.0000	0.0000	0.0793	0.0514	0.9335		0.0028
Veh.sped	0.1868	−0.0588	0.1517	0.0467	0.4650	0.0996
	(895)	(895)	(895)	(895)	(895)	(895)
	0.0000	0.0781	0.0000	0.1623	0.0000	0.0028

shows the Pearson product moment correlations between each pair of variables. These correlation coefficients range from −1 to +1 and measure the strength of the linear relationship between the variables. Also shown in parentheses is the number of pairs of data values used to calculate each coefficient. The third number in each place in the table is a p-value that tests the statistical significance of the calculated correlations. p-values below 0.05 indicate statistically significant non-zero correlations at the 95.0% confidence level. The table presents the correlation coefficient, number of variables, and p-value, respectively. The main variables for us here were the variables regarding PM and diesel engine performance—we will only consider them. The following pairs of variables have p-values below 0.05: PM and Consum; PM and Driv.troq; PM and Eng.sped; PM and Veh.sped. Figure 3 graphically shows the most widely used index of linear dependence or Pearson correlation coefficient. This correlation coefficient depends on the purpose of the study. It should be interpreted meaningfully. For the purposes of our study, we will assume the ordinal correlation coefficient where 0.9 < R < 1—very high correlation; 0.7 < R < 0.9—high correlation; 0.5 < R < 0.7—significant correlation; 0.3 < R < 0.5—moderate correlation; 0 < R < 0.3—weak correlation. When determining the relationship between the variables, a perfect or very strong correlation naturally occurs between fuel consumption and drive torque. The variable of interest for the study is PM, which significantly correlates mainly with the amount of fuel consumption and drive torque and has a moderate correlation with engine speed. We could continue with the statistical analysis, but we will stop here and make general conclusions at the end of the publication.

4. Conclusions

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In conclusion, we can say the following:

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The highest correlation between PM and drive torque is 0.56. This means that it is necessary to pay attention to environmental and economical training for vehicle drivers.

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The correlation coefficient between PM in terms of fuel consumption is 0.55, which does not describe a large part of the variables from which PM is generated in diesel engines. This requires research into the sources of PM produced by vehicles, as well as the quality of fuels used in diesel vehicles.

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Of particular importance is the technical condition of transport vehicles, including their service, repair and storage, by appropriately trained specialists using high-quality spare parts and fuel and lubricants.