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27
METHOD OF AUTOMOBILES FUEL CONSUMPTION RATIONING
BASED ON TELEMATIC DATA
DOI 10.24411/2072-8735-2018-10124
Petr I. Smirnov,
Vologda State University, Vologda, Russia, petrsm@bk.ru
Keywords: fuel consumptions, satellite telematic control systems, motor transport enterprises, transport, urban traffic condition, transportation of a cargo vehicle.
The article describes the method to determine real exploitable rates of fuel consumption in cargo vehicles. The significance of incoming dataflow influence on actual fuel consumption in cargo vehicles considering their technical state, maintenance service, cargo weight, speed and traffic condition was identified; the reasons for increased fuel consumption of cargo vehicles were determined; algorithm for evaluation of fuel consumption basing on a massive dataflow from telematics control systems was developed. Setting of the real exploitable rates of fuel consumption makes it necessary to prepare summarized information based on big data arrays. The research was made on the basis of motor transport enterprises of Vologda and Arkhangelsk regions. By request of the enterprises the vehicles KamAZ-551 1 1 in Vologda (Vologda region), KamAz-55 1 1 1 in Velsk (Arkhangelsk region) and Fiat Ducato in Vologda were chosen as a test subject. The research has shown that the identification of real rates of fuel consumption is a time-consuming and difficult process. The authors of the article suggested the creation of a special software on the basis of the systems of transport telematic monitoring as one of the possible solutions for this problem. The program will perform operations of collection, filtration, processing and analysis of the information in automated regime. This program will allow to calculate real exploitable rates of fuel consumption for a particular enterprise.
Information about author:
Petr I. Smirnov, lecturer of the Department of Automobiles and Automobile Facilities, Vologda State University, Russia, Vologda
Для цитирования:
Смирнов П.И. Метод определения расхода топлива автомобилей на основе анализа телематических данных // T-Comm: Телекоммуникации и транспорт. 2018. Том 12. №76. С. 69-75.
For citation:
Smirnov P.I. (2018). Method of automobiles fuel consumption rationing based on telematic data. T-Comm, vol. 12, no.7, pр. 69-75.
г Г\
Introduction
Searching for additional fuel-saving means in the current economic situation is one of the most frequently occurring problems for motor transport enterprises of all forms of ownership.
Presently, the problem to solve isn't studied enough within the frameworks of cargo vehicle transportation in urban driving cycle.
The current legislation requires to install the system of telematics control in passenger transport and cargo vehicles as well as at a specialized rolling stock.
During the exploitation of cargo vehicles equipped with telematic control system, a big variable data array is permanently transmitted.
To determine fuel consumption of even one separate vehicle a long period of data collection is needed; these data include automobile traffic mode, current fuel consumption, climate conditions, terrain features, as well as the weight of cargo for cargo vehicles.
Considering the error probability in the system, it is a necessary to filler the data collected. Specificity of the collected information is in a big flow of interrelated data that require processing and analysis [1,2]. Setting of the real exploitable rates of fuel consumption makes it necessary to prepare summarized information based on big data arrays. The difficulty lies in necessity to structure the data on multidimensional principle that includes spread in fuel consumption rates depending on cargo weight, speed, the distance covered and travelling time.
The practical part
The research was made on the basis of motor transport enterprises of Vologda and Arkhangelsk regions. By request of the enterprises the vehicles KamAZ-55111 in Vologda (Vologda region), KamAz-55111 in Velsk (Arkhangelsk region) and Fiat Ducato in Vologda were chosen as a test subject. The equipment of telematics control of the company Otrmicomm was installed in the objects (automobiles) chosen for the research. The motor transport enterprises provided the information received with the use of the servers Omnicomm Online and processed for the period of time from September 2016 to May 2017 for analysis.
During transportation work by the chosen vehicles, the data, that allows to see the dependence of fuel consumption on the average speed of the vehicles under study, were selected from the array of information flow received from the systems of telematics control. The data are presented Table I.
Table 1
Dependence of fuel consumption oil the average speed
KamAZ-55111 KamAZ-55111 (Velsk) FIAT DUCATO
(Voloeda)
01.12.2016 - 09.01.2017^ 01.09.2016-
24.04.2017 24.04.2017 24.04.2017
fuel con- average fOCl con- average fuel con- average
sumption, speed. sumption. speed, sumption, speed.
1/100 km km/h 1/100 km km/h 1/100 km km/h
90,4 1.7 ¡42 10,7 33,1 8
163.1 3,7 108 10.7 57,8 9,1
157,1 4,5 86,9 11,3 30,3 9,4
185,2 14,9 86,5 11.7 4.5 10
138,8 ¡6 2,3 11,9 31,7 10,8
56,6 16 81.1 12,5 15,4 12
177,1 16.3 185,1 14 14,7 12,8
146,4 18,4 40 14,8 17,6 13,8
145 20,6 93,7 15 12,3 14,8
101,6 20,9 153,3 15.1 19,4 15,7
86,2 25,1 309 15.4 17,1 16,5
49,! 26.2 158,1 16.4 27,9 17,7
61,7 30,6 65,7 16.5 25,2 18.8
82,1 30,9 ! 14,7 16,7 18 19,1
68,8 31,5 82,4 17.8 26 19,1
70 32,1 59,9 18.1 22,7 19,2
82,1 32,6 79,7 18.9 18,8 20.4
98 33,2 121,8 20.7 12,1 21.1
53,5 33.6 70,4 24 20,8 21.1
56,9 34,8 87,6 25.1 12,8 38,2
56,4 35,8 105,1 25,4 15 38,6
84 35,8 42,9 25,5 12,7 50,2
66 36,3 46,2 27,1 8,6 50,4
56,3 36.8 50,3 28.1 8,7 58.4
54,9 36,9 98,2 28,3 10 59,5
56 37 31,7 35.8 9,7 59,8
66,3 37,2 90,8 36.1 11,8 60,7
61,5 37.4 64,5 37 10,3 61.6
74,4 39,1 41.2 37.1 11,3 63.4
65.5 39.8 47,2 37.7 11,1 63.6
53,5 41,8 68,6 39.5 10 64,5
55,3 42,4 35,9 39.9 10 64.8
60,7 42,5 40 40,3 9,5 65,6
48,1 43 51,1 40,4 9,1 66.2
56,6 43,2 53,5 40,4 9,4 66,3
51,6 44 45,5 40,6 10 68
58,2 44,6 47,3 41,5 10,5 68,6
75 45 41 41,6 9,7 68,8
60,1 45 33,8 41.7 9,8 70,4
60,3 47,2 38,1 41.9 9,3 72,3
52,3 47,8 34,5 42,1 10,9 72.7
52,2 48,1 40 42,2 10,2 73.7
58,5 48,2 37,8 42,3 9,3 75.5
52,5 50,1 37,6 42,6 10,9 75.5
55,8 50,2 33 43.1 10,6 76.5
49,7 51,8 43,5 43.5 9,3 77.1
52,6 52,4 64,6 43.6 9,9 77,5
45,6 52,5 28,8 43,8 11,7 78,9
55,6 53 38,3 43.9 9,8 79,8
51,7 54 42,8 44.5 10 80
49.2 57,9 38,5 45 9,6 80.9
56,1 59,4 37,5 45.1 9,8 81.1
39,3 45.8 9,7 85.9
38,4 46 9,9 86
44,6 46,3 10,3 86.2
43,4 46,7 8,8 89,4
40 47
35,1 47,4
41,3 47,5
40,2 47,8
33,7 48,3
41,5 48.7
34,8 49,1
43,7 49,3
43,5 49,4
39,3 49,5
24,1 49,8
40 50,3
43,5 50,4
40,1 50,8
45,5 50,9
37,4 51
39,5 51,1
22,7 51.4
41 51,5
34,5 51,5
29,8 51,9
37,8 52
39,8 52,2
46,8 53,8
28,8 61,1
Dot charts with polynomial trend line showing the genera! dependence of fuel consumption oil the average speed in urban traffic condition and put into the relevant coordinates were made for further processing of the filtered data. The charts made for each vehicle under study are presented Figures 1-3.
Depends me of the average operating fuel consumption on the average tpced |Kamai in Vologda}
« Dependence of fuel
y * 0.07«** - 3,631i* * 1 t>TS*
Average sDecd, km/h
Fig, 1. Dependence of fuel consumption on the average speed of KamAZ (Vologda)
• hprniinitolM
Fig. 2. Dependence of fuel consumption oil the average speed of KamAZ (Velsk)
Dependence oïthe average ope,al Ing fuel consumption on 1he ipeed
[Fia, Quea[a in Un IfkgrfiJ
* dependence of Tue] ccrtiuinplion On average ipnd
— potïnam
[Dependence of fuet
cbnwfffiliOn M average speed]
Ti - if.lXJ, V D.tJli + i] .in ■ O
4 i .'■ Iv'-.--* .
. r 'i * c .r< km/h
Fig. 3. Dependence of fuel consumption on the average speed of Fiat Ducato (Vologda)
Monitoring of the Incoming How of the filtered data and analytical processing of the charts allowed to make the following conclusions.
1. Quadratic function of polynomial trend line shows that the dependence of fuel consumption on the speed isn't linear; it has one extremum. Moreover, the function decreases before reaching the extremum. However, the extremum of the given function is reached when the coordinates value corresponds to the speed which is over the speed limit set in a city. For example,
Fiat Ducato has the lowest fuel consumption when it goes with the speed of about 70 km/h. It means that under any circumstances fuel consumption will be higher than the minimal possible one with the speed of the vehicle within the urban driving
cycle.
The quality and style of driving of a particular driver, i.e. frequency of the use of vehicle controls which leads to the intensive change of revolutions of the crankshaft in internal combustion engine and transmission ratio and, hence, to the more intensive change of the speed of a vehicle, also influence the increase of fuel consumption at low speed in urban traffic condition. After the extremum is reached, the function starts to increase again. To understand this peculiarity, the fuel (low equation was examined [1].
Vehicle fuel consumption is directly dependent on the resistance force:
Qs=
36000-pT-r|T
,1/100 km, (1)
where gt. - specific fuel consumption, g/(kW.h); P^ - road resistance force, N; Pu - air resistance force, N; Ph — acceleration resistance force, N; p, - fuel density, kg/1; T|T - transmission coefficient of efficiency.
All resistance forces depend on the speed of a vehicle. Moreover, road resistance force and acceleration resistance force linearly decrease, and air resistance force quadratically increase. It explains nonlincarity of the function.
The examination of the equation given above also shows that transmission coefficient of efficiency and fuel density also influence fuel consumption. Hence, fuel consumption is directly dependent on technical condition of a vehicle and the quality of the fuel being used. Increased fuel consumption can indicate poor technical condition of a vehicle. Consequently, it is possible for Engineering and Technical Serv ice of an enterprise to correct the frequency of technical maintenance of vehicles which will allow to optimize the work of a motor transport enterprise on the whole by reducing the time of vehicles stoppage in technical maintenance and repairing area.
2. The presented charts show the dependence of spread in fuel consumption rates on speed. The lower the speed, the bigger the spread. This dependence is determined by the fact that the dataflow from the telematic complexes installed in a vehicle doesn't contain the data on cargo weight. The collection of these data is especially important for cargo vehicles because the weight of cargo can exceed the weight of a vehicle itself. And the loading of a vehicle influence fuel consumption at low speed more than at high speed. Correspondingly, the problem of remote data collection to specify the incoming information appears.
To understand the dependence of fuel consumption on traffic conditions of a vehicle, we need to compare the charts presented above. They need to be put into the single coordinates. The result is shown in Figure 4.
It can be concluded from the comparison that the spread in fuel consumption rales array at both KamAZes is bigger than at Fiat. Thus the conclusion can be made that the spread also depends on the relation between carrying capacity of a vehicle and its unladen weight.
Besides, the data presented above show that real exploitable fuel consumption exceeds a standard one established by the producer. The standard fuel consumption for KamAZ vehicles is 27
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T-Comm Tom 12. #7-2018
T-Comm Vol.12. #7-2018
References
When the necessary equipment will be installed in a vehicle, the program will require to enter the vehicle registration number which start the search of the necessary information in the system of satellite telematics. Possible invalid data, w hich can influence the final result, w ill be eliminated from the information received. Then the program will divide the data which correspond to the urban and highway traffic conditions. The information will be processed and analyzed separately for the urban and highway traffic conditions; after that an operator will choose what information and for which vehicle shall be displayed. As a result, a user will receive the data on fuel consumption rates for a particular vehicle.
This program will allow to calculate real exploitable rates of fuel consumption for a particular enterprise.
Conclusions
The research resulted in the following conclusions;
1. The dependence of fuel consumption on speed is nonlinear and is determined by the technical state of a vehicle, cargo weight and exploitation conditions.
2. The reasons for cxcccdancc of fuel consumption arc connected with discrepancy of the standard testing cycle and the real traffic regime ofa vehicle as well as with the quality and style of driving.
3. Algorithm for evaluation of fuel consumption basing on the data from telematics control systems and considering special features of transport work ofa particular enterprise was developed.
4. Algorithm for evaluation of fuel consumption was developed; it includes the follow ing steps:
1. Install the necessary equipment in a vehicle with the installed system of satellite monitoring,
2. Collect the necessary data during the definite period of vehicle exploitation.
3. Make a selection of the collected data.
4. Processed the data received.
5. Analyze the data received.
1. Fro berg. A.; Nielsen, L. (2008). Efficient drive cycle simulation. IEEE Trans. Veh. Techno!., 57, pp. 1442-1453.
2. Guzzella, L.; Sciarretta, A. (2007). Vehicle Propulsion Systems. Springer Verlag; Berlin, Germany.
3. Du JD, Han WJ, Peng YH, Gu CC. (2010). Potential for reducing GHG emissions and energy consumption from implementing tlie aluminum intensive vehicle fleet in China. Energy 2010;35{12):467le8.
4. Mao H, Liu Z, Zhao F, Li W, Hang W. (2015). Scenario analysis of energy consumption and greenhouse gas emissions from China's passenger vehicles. Energy 2015;9I:15 le9,
5. Khayyam Ft, Bab-Hadiashar A. (2014). Adaptive intelligent energy management system of plug-in hybrid electric vehicle. Energy 20!4;69:319e35.
6. Kecojevie V, Komljenovic D, (2010). Flaul truck fuel consumption and C02 emission under various engine load conditions. Min Frig 2010;62(12):44e8.
7. Lilvinov A.S., Farobin Y.E. (1989). Automobile: the theory of performance properties: textbook for higher education. Moscow: Mashinostroyeniye, 240 p.
8. Muratori M, Moran MJ, Serra E, Rizzoni G. (2013), Highly-resolved modeling of personal transportation energy consumption in the United States. Energy 2013;58:168e77.
9. Pikalev O.N., Smirnov P.I. (2016). Review of methodological approaches lo organization of the monitoring systems of vehicles using GPS-track ing technologies. SCIENCE OF THE YOUNG - FUTURE OF RUSSIA collection of scientific articles of perspective projects of young scientists* 3 volumes. Kursk: ZAO "Universitetskaya kniga", pp. 293-295.
10. Wliyie K, Daly HE, O Gallach oir BP, (2013). Modelling HGV freight transport energy demand in Ireland and the impacts of the property construction bubble. Energy 2013;50(l):245e51.
11. Zhao H, Burke A, Miller M. (2013). Analysis of Class 8 truck technologies for their fuel savings and economics. Transp Res Pari D Transp Environ 2013;23:55e63.
¡2. Zamboni G, Andre M, Roveda A, Capobianco M. (2015). Experimental evaluation of heavy duty vehicle speed patterns in urban and port areas and estimation of their fuel consumption and exhaust emissions. Transp Res Part D Transp Environ 2015;35:lel0.
МЕТОД ОПРЕДЕЛЕНИЯ РАСХОДА ТОПЛИВА АВТОМОБИЛЕЙ НА ОСНОВЕ АНАЛИЗА ТЕЛЕМАТИЧЕСКИХ ДАННЫХ
Смирнов Петр Ильич, Вологодский государственный университет, Вологда, Россия, petrsm@bk.ru
Аннотация
В статье описана методика определения реальных эксплуатационных норм расхода топлива грузовых транспортных средств на основе разработанного программного обеспечения для анализа больших массивов данных получаемых от автомобильных телекоммуникационных систем. Была выявлена значимость влияния поступающего потока данных на фактический расход топлива грузовых автотранспортных средств с учетом их технического состояния, технического обслуживания, массы перевозимого груза, скорости и режима движения, причины повышенного расхода топлива грузовых автомобилей, разработан алгоритм оценки расхода топлива на основе массивного потока данных от систем телематического контроля. В процессе эксплуатации грузового транспорта, оборудованного системой телематического контроля, происходит постоянная передача объемного, переменного во время выполнения транспортной работы массива данных. Установка реальных эксплуатационных норм расхода топлива вызывает необходимость подготовки суммарной информации на основе больших массивов данных. Сложность заключается в необходимости структурирования данных по многомерному принципу, который включает в себя разброс показателей расхода топлива в зависимости от массы перевозимого груза, скорости движения, пройденного пути и времени движения. Исследования были проведены на базе автотранспортных предприятий Вологодской и Архангельской областей. В качестве объекта исследования по заказу автопредприятий были выбраны автомобили КАМАЗ-55111 в городе Вологда (Вологодская область), КАМАЗ-55111 в городе Вельск (Архангельская область) и Fiat Ducato в городе Вологда. На объектах (автомобилях), выбранных для исследования, было установлено оборудование телематического контроля фирмы Omnicomm. Заказчики в лице автотранспортных предприятий предоставляли для анализа информацию, полученную с использованием серверов Omnicomm Online и обработанную за период с сентября 2016 г. по май 2017. Авторами статьи предложено создать специальное программное обеспечение на основе систем телематического мониторинга транспорта в качестве одного из возможных путей решения данной проблемы. Созданная программа будет выполнять операции по сбору, фильтрации, обработке и анализу информации в автоматизированном режиме.
Ключевые слова: расход топлива, телематика, базы данных, спутниковые телематические системы управления, автотранспортные предприятия, транспорт, грузовые перевозки.
Литература
1. Froberg, A.; Nielsen, L. Efficient drive cycle simulation. IEEE Trans. Veh. Technol. 2008, 57, 1442-1453.
2. Guzzella, L.; Sciarretta, A. Vehicle Propulsion Systems; Springer Verlag: Berlin, Germany, 2007.
3. Du JD, Han WJ, Peng YH, Gu CC. Potential for reducing GHG emissions and energy consumption from implementing the aluminum intensive vehicle fleet in China. Energy 2010;35(12):4671e8.
4. Hao H, Liu Z, Zhao F, Li W, Hang W. Scenario analysis of energy consumption and greenhouse gas emissions from China's passenger vehicles. Energy 2015;91:151e9.
5. Khayyam H, Bab-Hadiashar A. Adaptive intelligent energy management system of plug-in hybrid electric vehicle. Energy 2014;69:319e35.
6. Kecojevic V, Komljenovic D. Haul truck fuel consumption and CO2 emission under various engine load conditions. Min Eng 2010;62(12):44e8.
7. Литвинов А.С., Фаробин Я.Е. Автомобиль: теория эксплуатационных свойств: учебник для вузов. М.: Машиностроение, 1989. 240 c.
8. Muratori M, Moran MJ, Serra E, Rizzoni G. Highly-resolved modeling of personal transportation energy consumption in the United States. Energy 2013;58:168e77.
9. Пикалев О.Н., Смирнов П.И. Обзор методологических подходов к организации систем мониторинга транспортных средств с применением технологий GPS-трекинга // НАУКА МОЛОДЫХ - БУДУЩЕЕ РОССИИ сборник научных статей международной научной конференции перспективных разработок молодых ученых: в 3 томах. Курск: ЗАО "Университетская книга", 2016. С. 293-295.
10. Whyte K, Daly HE, O Gallach oir BP. Modelling HGV freight transport energy demand in Ireland and the impacts of the property construction bubble. Energy 2013;50(1):245e51.
11. Zhao H, Burke A, Miller M. Analysis of Class 8 truck technologies for their fuel savings and economics. Transp Res Part D Transp Environ 2013;23:55e63.
12. Zamboni G, Andre M, Roveda A, Capobianco M. Experimental evaluation of heavy duty vehicle speed patterns in urban and port areas and estimation of their fuel consumption and exhaust emissions. Transp Res Part D Transp Environ 2015;35:1e10.
Информация об авторе:
Смирнов Петр Ильич, старший преподаватель кафедры Автомобилей и автомобильного хозяйства, заведующий лабораторией диагностики автомобилей и телекоммуникационных систем, Вологодский государственный университет, Вологда, Россия
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