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Neural Network-Based Modeling of Electric Vehicle Energy Demand and All Electric Range

Faculty of Mechanical Engineering and Naval Architecture, University of Zagreb, 10000 Zagreb, Croatia
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This paper was originally published as Topić, J.; Škugor, B.; Deur, J. Neural network-based modelling of energy demand and all electric range of an extended range electric vehicle. In Proceedings of the 13th Conference on Sustainable Development of Energy, Water and Environment Systems (SDEWES), Palermo, Italy, 30 September–4 October 2018.
Energies 2019, 12(7), 1396; https://doi.org/10.3390/en12071396
Received: 18 March 2019 / Revised: 7 April 2019 / Accepted: 8 April 2019 / Published: 11 April 2019
(This article belongs to the Special Issue Energy Saving in Public Transport)
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Abstract

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. View Full-Text
Keywords: electric vehicles; deep neural networks; energy demand modeling; SoC at destination; fuel consumption; all-electric range; big data electric vehicles; deep neural networks; energy demand modeling; SoC at destination; fuel consumption; all-electric range; big data
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).
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Topić, J.; Škugor, B.; Deur, J. Neural Network-Based Modeling of Electric Vehicle Energy Demand and All Electric Range. Energies 2019, 12, 1396.

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