Modeling of fuel consumption using artificial neural networks
1
Silesian University of Technology
Submission date: 2020-07-22
Final revision date: 2020-11-18
Acceptance date: 2020-11-18
Online publication date: 2020-11-19
Publication date: 2020-11-19
Diagnostyka 2020;21(4):103-113
KEYWORDS
TOPICS
ABSTRACT
The article presents a model of operational fuel consumption by a passenger car from the B segment, powered by a SI engine. The model was developed using artificial neural networks simulated in the Stuttgart Neural Network Simulator package. The data for the model was obtained from long-term 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.
REFERENCES (29)
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....
2.
Bergmeir C, Benítez JM. Neural Networks in R Using the Stuttgart Neural Network Simulator: RSNNS. Journal of Statistical Software 2012; 47(7): 1-26. https://doi.org/10.18637/jss.v....
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.....
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.....
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/sjsut....
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.enpo....
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/....
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.trib....
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/....
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.appl....
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.....
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/WHISPE....
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.....
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/sjsut....
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.....
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.1....
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....
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/sjsut....
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/....
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.apen....
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.....
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.....
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.jcle....
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.jcle....
25.
Wierzbicki S. Evaluation of the effectiveness of on-board diagnostic systems in controlling exhaust gas emissions from motor vehicles. Diagnostyka. 2019, 20:(4):75-79. https://doi.org/10.29354/diag/....
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....
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.engg....
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....
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.phys....
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