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.

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The different versions of the original document can be found in:

https://doaj.org/toc/1996-1073 under the license cc-by
https://www.mdpi.com/1996-1073/12/7/1396/pdf,
https://ideas.repec.org/a/gam/jeners/v12y2019i7p1396-d221850.html,
https://academic.microsoft.com/#/detail/2938932713
http://dx.doi.org/10.3390/en12071396
under the license https://creativecommons.org/licenses/by/4.0/
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Published on 01/01/2019

Volume 2019, 2019
DOI: 10.3390/en12071396
Licence: Other

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