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Tytuł artykułu

Modeling of fuel consumption using artificial neural networks

Treść / Zawartość
Identyfikatory
Warianty tytułu
PL
Modelowanie zużycia paliwa przy pomocy sztucznych sieci neuronowych
Języki publikacji
EN
Abstrakty
EN
The article presents a model of operational fuel consumption by a passenger car from the B segment, powered by a spark ignition engine. The model was developed using artificial neural networks simulated in the Stuttgart Neural Network Simulator (SNNS) package. The data for the model was obtained from longterm operational tests, during which data from the engine control unit were recorded via the OBDII diagnostic interface. The model is based on neural networks with two hidden layers, the size of which was selected using an original iterative algorithm. During the structure selection process, a total of 576 different networks were tested. The analysis of the obtained test errors made it possible to select the optimal structure of the 6-19-17-1 model. The network input values were: vehicle speed and acceleration, road slope, throttle opening degree, selected gear number and engine speed. The networks were trained using the efficient RPROP method. A correctly trained network, based on the set parameters, was able to forecast the instantaneous fuel consumption. These forecasts showed a high correlation with the measured values. Average fuel consumption calculated on their basis was close to the real value, which was calculated on the basis of two consecutive fuelings of the vehicle.
PL
W artykule przedstawiono model eksploatacyjnego zużycia paliwa przez samochód osobowy z segmentu B, zasilany silnikiem o zapłonie iskrowym. Model opracowano przy wykorzystaniu sztucznych sieci neuronowych, których działanie symulowano w pakiecie Stuttgart Neural Network Simulator (SNNS). Dane do modelu pozyskano z długotrwałych badań eksploatacyjnych, podczas których rejestrowano przez interfejs diagnostyczny OBDII dane pochodzące z jednostki sterującej silnikiem. Model oparto na sieciach neuronowych o dwu warstwach ukrytych, których wielkość dobrano przy pomocy autorskiego, iteracyjnego algorytmu. Podczas procesu doboru struktury przebadano łącznie 576 różnych sieci. Analiza uzyskanych błędów testowania pozwoliła na wybór optymalnej struktury modelu 6-19-17-1. Wielkościami wejściowymi sieci były: prędkość i przyspieszenie pojazdu, nachylenie drogi, stopień otwarcia przepustnicy, numer wybranego biegu oraz prędkość obrotowa silnika. Sieci uczono przy użyciu wydajnej metody RPROP. Poprawnie nauczona sieć na podstawie zadanych parametrów była w stanie prognozować chwilowe zużycie paliwa. Prognozy te wykazywały wysoką korelację ze zmierzonymi wartościami. Obliczone na ich podstawie średnie zużycie paliwa było zbliżone do rzeczywistej wartości, którą obliczono na podstawie dwu kolejnych tankowań pojazdu.
Czasopismo
Rocznik
Strony
103--113
Opis fizyczny
Bibliogr. 29 poz., rys., tab.
Twórcy
  • Silesian University of Technology, Faculty of Transport and Aviation Engineering, Krasińskiego 8 str., 40-019 Katowice, Poland
Bibliografia
  • 1. Aleksendrić D. Neural network prediction of brake friction materials wear. Wear. 2010; 268(1-2): 117-125. https://doi.org/10.1016/j.wear.2009.07.006.
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  • 3. Dennehy ER, Gallachóir BPÓ. Ex-post decomposition analysis of passenger car energy demand and associated CO2 emissions. Transportation Research Part D: Transport and Environment 2018;59:400-416. https://doi.org/10.1016/j.trd.2018.01.012.
  • 4. Freitas Salgueiredo C., Orfila O., Saint Pierre G., Doublet P., Glaser S., Doncieux S., Billat V. Experimental testing and simulations of speed variations impact on fuel consumption of conventional gasoline passenger cars Transportation Research Part D: Transport and Environment 2017; 57: 336-349. https://doi.org/10.1016/j.trd.2017.09.029.
  • 5. Gibała Ł, Konieczny J. Application of artificial neural networks to predict railway switch durability. Scientific Journal of Silesian University of Technology. Series Transport. 2018; 101:67-77. https://doi.org/10.20858/sjsutst.2018.101.7.
  • 6. González RM, Marrero GA, Rodríguez-López J, Marrero ÁS. Analyzing CO2 emissions from passenger cars in Europe: A dynamic panel data approach. Energy Policy 2019; 129: 1271-1281. https://doi.org/10.1016/j.enpol.2019.03.031.
  • 7. Grytsyuk O, Vrublevskyi O. Investigations of diesel engine in the road test. Diagnostyka. 2018; 19(2):89-94. https://doi.org/10.29354/diag/90279.
  • 8. Holmberg K, Erdemir A. The impact of tribology on energy use and CO2 emission globally and in combustion engine and electric cars. Tribology International 2019;135:389-396. https://doi.org/10.1016/j.triboint.2019.03.024.
  • 9. Komorska IM, Wołczyński Z, Borczuch AD. Diagnosis of sensor faults in a combustion engine control system with the artificial neural network. Diagnostyka. 2019;20(4):19-25. https://doi.org/10.29354/diag/110440.
  • 10. Lahimer AA, Alghoul MA, Sopian K, Khrit NG. Potential of solar reflective cover on regulating the car cabin conditions and fuel consumption. Applied Thermal Engineering. 2018; 143:59-71. https://doi.org/10.1016/j.applthermaleng.2018.07.020.
  • 11. Lodi C, Seitsonen A, Paffumi E, De Gennaro M, Huld T, Malfettani S. Reducing CO2 emissions of conventional fuel cars by vehicle photovoltaic roofs. Transportation Research Part D: Transport and Environment. 2018; 59: 313-324. https://doi.org/10.1016/j.trd.2018.01.020.
  • 12. Olesiuk D, Bachmann M, Habermeyer M, Heldens W, Zagajewski B. SNNS application for crop classification using hymap data. Proceedings of the Whispers - 2nd Workshop on hyperspectral image and signal processing. 2010. 1-4. https://doi.org/10.1109/WHISPERS.2010.5594848.
  • 13. Orfila O, Freitas Salgueiredo C, Saint Pierre G, Sun H,. Gruyer YLiD, Glaser S. Fast computing and approximate fuel consumption modeling for Internal Combustion Engine passenger cars. Transportation Research Part D: Transport and Environment. 2017; 50: 14-25. https://doi.org/10.1016/j.trd.2016.10.016.
  • 14. Pamuła T. Classification of road traffic conditions based on texture features of traffic images using neural networks. Scientific Journal of Silesian University of Technology. Series Transport. 2016; 92:101-109. https://doi.org/10.20858/sjsutst.2016.92.10.
  • 15. Peters A, Gutscher H, Scholz RW. Psychological determinants of fuel consumption of purchased new cars. Transportation Research Part F: Traffic Psychology and Behaviour. 2011; 14(3): 229-239. https://doi.org/10.1016/j.trf.2011.01.003.
  • 16. Riedmiller M, Braun H. A direct adaptive method for faster backpropagation learning the RPROP algorithm. IEEE International Conference on Neural Networks. 1993; 1:586-591. https://doi.org/10.1109/ICNN.1993.298623.
  • 17. Shebani A. Iwnicki S. Prediction of wheel and rail wear under different contact conditions using artificial neural networks. Wear. 2018; 406-407: 173-184. https://doi.org/10.1016/j.wear.2018.01.007.
  • 18. Szczucka-Lasota B, Kamińska J, Krzyżewska I.Influence of tire pressure on fuel consumption in trucks with installed tire pressure monitoring system (TPMS). Scientific Journal of Silesian University of Technology. Series Transport. 2019; 103:167-181. https://doi.org/10.20858/sjsutst.2019.103.13.
  • 19. Tifour B, Moussa B, Ahmed H, Camel T. Monitoring and energy management approach for a fuel cell hybrid electric vehicle. Diagnostyka. 2020; 21(3):15-29. https://doi.org/10.29354/diag/123996.
  • 20. Tsiakmakis S, Fontaras G, Ciuffo B, Samaras Z. A simulation-based methodology for quantifying European passenger car fleet CO2 emissions. Applied Energy. 2017; 199:447-465. https://doi.org/10.1016/j.apenergy.2017.04.045.
  • 21. Wang H, Fu L, Zhou Y, Li H. Modelling of the fuel consumption for passenger cars regarding driving characteristics. Transportation Research Part D: Transport and Environment. 2008; 13 (7): 479-482. https://doi.org/10.1016/j.trd.2008.09.002.
  • 22. Wang J, Rakha HA, Fadhloun K. Validation of the Rakha-Pasumarthy-Adjerid car-following model for vehicle fuel consumption and emission estimation applications. Transportation Research Part D: Transport and Environment. 2017; 55: 246-261. https://doi.org/10.1016/j.trd.2017.06.030.
  • 23. Weiss M, Irrgang L, Kiefer AT, Roth JR, Helmers E. Mass- and power-related efficiency trade-offs and CO2 emissions of compact passenger cars. Journal of Cleaner Production 2020; 243: 118326. https://doi.org/10.1016/j.jclepro.2019.118326.
  • 24. Weiss M, Zerfass A, Helmers E, Fully electric and plug-in hybrid cars - An analysis of learning rates, user costs, and costs for mitigating CO2 and air pollutant emissions. Journal of Cleaner Production. 2019;212:1478-1489. https://doi.org/10.1016/j.jclepro.2018.12.019.
  • 25. Wierzbicki S. Evaluation of the effectiveness of onboard diagnostic systems in controlling exhaust gas emissions from motor vehicles. Diagnostyka. 2019, 20:(4):75-79. https://doi.org/10.29354/diag/114834.
  • 26. Wu JD, Liu JC. Development of a predictive system for car fuel consumption using an artificial neural network. Expert Systems with Applications. 2011; 38 (5):4967-4971. https://doi.org/10.1016/j.eswa.2010.09.155.
  • 27. Yilmazkaya E. Dagdelenler G, Ozcelik Y, Sonmez, H. Prediction of mono-wire cutting machine performance parameters using artificial neural network and regression models. Engineering Geology. 2018.239:96-108. https://doi.org/10.1016/j.enggeo.2018.03.009.
  • 28. Zervas E. Impact of altitude on fuel consumption of a gasoline passenger car. Fuel 2011; 90(6): 2340-2342. https://doi.org/10.1016/j.fuel.2011.02.004.
  • 29. Zhang X, Jia B, Jiang R. Impact of safety assistance driving systems on oscillation magnitude, fuel consumption and emission in a car platoon. Physica A: Statistical Mechanics and its Applications. 2018; 505:995-1007. https://doi.org/10.1016/j.physa.2018.04.033.
Uwagi
Opracowanie rekordu ze środków MNiSW, umowa Nr 461252 w ramach programu "Społeczna odpowiedzialność nauki" - moduł: Popularyzacja nauki i promocja sportu (2021).
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-cedabb0d-cc0e-400f-87e2-87664dd859ec
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