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Scientific paper - Original scientific paper
Neural Network-Based Modeling of Electric Vehicle Energy Demand and All Electric Range
Energies, 12 (2019), 7; 1396-1416. https://doi.org/10.3390/en12071396
Topić, Jakov; Škugor, Branimir; Deur, Joško

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Topić, J., Škugor, B. & Deur, J. (2019). Neural Network-Based Modeling of Electric Vehicle Energy Demand and All Electric Range. Energies, 12. (7), 1396-1416. doi: 10.3390/en12071396

Topić, Jakov, et al. "Neural Network-Based Modeling of Electric Vehicle Energy Demand and All Electric Range." Energies, vol. 12, no. 7, 2019, pp. 1396-1416. https://doi.org/10.3390/en12071396

Topić, Jakov, Branimir Škugor and Joško Deur. "Neural Network-Based Modeling of Electric Vehicle Energy Demand and All Electric Range." Energies 12, no. 7 (2019): 1396-1416. https://doi.org/10.3390/en12071396

Topić, J., Škugor, B. and Deur, J. (2019) 'Neural Network-Based Modeling of Electric Vehicle Energy Demand and All Electric Range', Energies, 12(7), pp. 1396-1416. doi: 10.3390/en12071396

Topić J, Škugor B, Deur J. Neural Network-Based Modeling of Electric Vehicle Energy Demand and All Electric Range. Energies [Internet]. 2019 April 11 [cited 2024 November 25];12(7):1396-1416. doi: 10.3390/en12071396

J. Topić, B. Škugor and J. Deur, "Neural Network-Based Modeling of Electric Vehicle Energy Demand and All Electric Range", Energies, vol. 12, no. 7, pp. 1396-1416, April 2019. [Online]. Available at: https://urn.nsk.hr/urn:nbn:hr:235:364932. [Accessed: 25 November 2024]

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Metadata
Title (english)Neural Network-Based Modeling of Electric Vehicle Energy Demand and All Electric Range
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)
A deep neural network-based approach of energy demand modeling of electric vehicles (EV) is proposed in this paper. The model-based prediction of energy demand is based on driving cycle time series used as a model input, which is properly preprocessed and transformed into 1D or 2D static maps to serve as a static input to the neural network. Several deep feedforward neural network architectures are considered for this application along with different model input formats. Two energy demand models are derived, where the first one predicts the battery state-of-charge and fuel consumption at destination for an extended range electric vehicle, and the second one predicts the vehicle all-electric range. The models are validated based on a separate test dataset when compared to the one used in neural network training, and they are compared with the traditional response surface approach to illustrate effectiveness of the method proposed.
Keywords (english)
Languageenglish
Publication typeScientific paper - Original scientific paper
Publication statusPublished
Peer reviewPeer review - international
Publication versionPublished version
Journal titleEnergies
Numberingvol. 12, no. 7, pp. 1396-1416
p-ISSN1996-1073
DOIhttps://doi.org/10.3390/en12071396
URN:NBNurn:nbn:hr:235:364932
Publication2019-04-11
Document URLhttps://www.mdpi.com/1996-1073/12/7/1396
Type of resourceText
Access conditionsOpen access
Terms of useLicence Attribution 4.0 International (CC BY 4.0) icon
Created on2024-07-05 12:50:28