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Neural Network-Based Prediction of Vehicle Fuel Consumption Based on Driving Cycle Data
Sustainability, 14 (2022), 2; 744-756. https://doi.org/10.3390/su14020744
Topić, Jakov; Škugor, Branimir; Deur, Joško

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Topić, J., Škugor, B. i Deur, J. (2022). Neural Network-Based Prediction of Vehicle Fuel Consumption Based on Driving Cycle Data. Sustainability, 14. (2), 744-756. doi: 10.3390/su14020744

Topić, Jakov, et al. "Neural Network-Based Prediction of Vehicle Fuel Consumption Based on Driving Cycle Data." Sustainability, vol. 14, br. 2, 2022, str. 744-756. https://doi.org/10.3390/su14020744

Topić, Jakov, Branimir Škugor i Joško Deur. "Neural Network-Based Prediction of Vehicle Fuel Consumption Based on Driving Cycle Data." Sustainability 14, br. 2 (2022): 744-756. https://doi.org/10.3390/su14020744

Topić, J., Škugor, B. i Deur, J. (2022) 'Neural Network-Based Prediction of Vehicle Fuel Consumption Based on Driving Cycle Data', Sustainability, 14(2), str. 744-756. doi: 10.3390/su14020744

Topić J, Škugor B, Deur J. Neural Network-Based Prediction of Vehicle Fuel Consumption Based on Driving Cycle Data. Sustainability [Internet]. 10.01.2022. [pristupljeno 27.07.2024.];14(2):744-756. doi: 10.3390/su14020744

J. Topić, B. Škugor i J. Deur, "Neural Network-Based Prediction of Vehicle Fuel Consumption Based on Driving Cycle Data", Sustainability, vol. 14, br. 2, str. 744-756, Siječanj 2022. [Online]. Dostupno na: https://urn.nsk.hr/urn:nbn:hr:235:795319. [Citirano: 27.07.2024.]

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Podaci o radu
Naslov (engleski)Neural Network-Based Prediction of Vehicle Fuel Consumption Based on Driving Cycle Data
AutorJakov Topić
AutorBranimir Škugor
AutorJoško Deur
Autorova ustanovaSveučilište u Zagrebu
Fakultet strojarstva i brodogradnje
(Zavod za robotiku i automatizaciju proizvodnih sustava)
(Katedra za strojarsku automatiku)
Znanstveno / umjetničko
područje, polje i grana		TEHNIČKE ZNANOSTI
Strojarstvo
Sažetak (engleski)
This paper deals with fuel consumption prediction based on vehicle velocity, acceleration, and road slope time series inputs. Several data-driven models are considered for this purpose, including linear regression models and neural network-based ones. The emphasis is on accounting for the road slope impact when forming the model inputs, in order to improve the prediction accuracy. A particular focus is devoted to conversion of length-varying driving cycles into fixed dimension inputs suitable for neural networks. The proposed prediction algorithms are parameterized and tested based on GPS- and CAN-based tracking data recorded on a number of city buses during their regular operation. The test results demonstrate that a proposed neural network-based approach provides a favorable prediction accuracy and reasonable execution speed, thus making it suitable for various applications such as vehicle routing optimization, synthetic driving cycle validation, transport planning and similar.
Ključne riječi (engleski)
Jezikengleski
Vrsta publikacijeZnanstveni rad - Izvorni znanstveni rad
Status objaveObjavljen
Vrsta recenzijeRecenziran - međunarodna recenzija
Verzija publikacijeObjavljena verzija rada (izdavačev PDF)
Naslov časopisaSustainability
Brojčani podacivol. 14, br. 2, str. 744-756
p-ISSN2071-1050
DOIhttps://doi.org/10.3390/su14020744
URN:NBNurn:nbn:hr:235:795319
Datum objave publikacije2022-01-10
URL dokumentahttps://www.mdpi.com/2071-1050/14/2/744
Vrsta resursaTekst
Prava pristupaOtvoreni pristup
Uvjeti korištenjaIkona licence Imenovanje 4.0 međunarodna (CC BY 4.0)
Datum i vrijeme pohrane2024-07-05 11:21:31