prikaz prve stranice dokumenta Neural Network-Based Prediction of Vehicle Fuel Consumption Based on Driving Cycle Data
  Download
PDF 3.15 MB
Scientific paper - Original scientific paper
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

Institutional repository: Repository of Faculty of Mechanical Engineering and Naval Architecture University of Zagreb

Cite this document

Topić, J., Škugor, B. & 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, no. 2, 2022, pp. 744-756. https://doi.org/10.3390/su14020744

Topić, Jakov, Branimir Škugor and Joško Deur. "Neural Network-Based Prediction of Vehicle Fuel Consumption Based on Driving Cycle Data." Sustainability 14, no. 2 (2022): 744-756. https://doi.org/10.3390/su14020744

Topić, J., Škugor, B. and Deur, J. (2022) 'Neural Network-Based Prediction of Vehicle Fuel Consumption Based on Driving Cycle Data', Sustainability, 14(2), pp. 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]. 2022 January 10 [cited 2024 July 13];14(2):744-756. doi: 10.3390/su14020744

J. Topić, B. Škugor and J. Deur, "Neural Network-Based Prediction of Vehicle Fuel Consumption Based on Driving Cycle Data", Sustainability, vol. 14, no. 2, pp. 744-756, January 2022. [Online]. Available at: https://urn.nsk.hr/urn:nbn:hr:235:795319. [Accessed: 13 July 2024]

Metadata
Title (english)Neural Network-Based Prediction of Vehicle Fuel Consumption Based on Driving Cycle Data
AuthorJakov Topić
AuthorBranimir Škugor
AuthorJoško Deur
Author's institutionUniversity of Zagreb
Faculty of Mechanical Engineering and Naval Architecture
(Department of Robotics and Production System Automation)
(Chair of Engineering Automation)
Scientific / art field,
discipline and subdiscipline		TECHNICAL SCIENCES
Mechanical Engineering
Abstract (english)
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.
Keywords (english)
Languageenglish
Publication typeScientific paper - Original scientific paper
Publication statusPublished
Peer reviewPeer review - international
Publication versionPublished version
Journal titleSustainability
Numberingvol. 14, no. 2, pp. 744-756
p-ISSN2071-1050
DOIhttps://doi.org/10.3390/su14020744
URN:NBNurn:nbn:hr:235:795319
Publication2022-01-10
Document URLhttps://www.mdpi.com/2071-1050/14/2/744
Type of resourceText
Access conditionsOpen access
Terms of useLicence Attribution 4.0 International (CC BY 4.0) icon
Created on2024-07-05 11:21:31