Scipediacontent: Scipediacontent moved page Draft Content 745718613 to Skugor et al 2019a

2021-02-12T12:48:37Z

Scipediacontent moved page Draft Content 745718613 to Skugor et al 2019a

Scipediacontent: Created page with " == 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 deman..."

2021-02-12T12:48:34Z

Created page with " == 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 deman..."

New page

== 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.

Document type: Article

== Full document ==
<pdf>Media:Draft_Content_745718613-beopen223-2681-document.pdf</pdf>

== Original document ==

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

* [http://dx.doi.org/10.3390/en12071396 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 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://www.mdpi.com/1996-1073/12/7/1396],
: [https://doaj.org/toc/1996-1073 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],
: [https://www.mdpi.com/1996-1073/12/7/1396/pdf https://www.mdpi.com/1996-1073/12/7/1396/pdf],
: [https://ideas.repec.org/a/gam/jeners/v12y2019i7p1396-d221850.html https://ideas.repec.org/a/gam/jeners/v12y2019i7p1396-d221850.html],
: [https://academic.microsoft.com/#/detail/2938932713 https://academic.microsoft.com/#/detail/2938932713]

* [https://www.mdpi.com/1996-1073/12/7/1396/pdf https://www.mdpi.com/1996-1073/12/7/1396/pdf],
: [http://dx.doi.org/10.3390/en12071396 http://dx.doi.org/10.3390/en12071396]

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

← Older revision	Revision as of 12:48, 12 February 2021
(No difference)

Skugor et al 2019a - Revision history

Scipediacontent: Scipediacontent moved page Draft Content 745718613 to Skugor et al 2019a

Scipediacontent: Created page with " == 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 deman..."