Latest revision as of 13:48, 12 February 2021

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:

• http://dx.doi.org/10.3390/en12071396 under the license https://creativecommons.org/licenses/by

• https://www.mdpi.com/1996-1073/12/7/1396/pdf under the license https://creativecommons.org/licenses/by/4.0

• https://www.mdpi.com/1996-1073/12/7/1396,

https://doaj.org/toc/1996-1073 under the license cc-by

• https://www.mdpi.com/1996-1073/12/7/1396,

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

• https://www.mdpi.com/1996-1073/12/7/1396/pdf,

http://dx.doi.org/10.3390/en12071396

under the license https://creativecommons.org/licenses/by/4.0/