Scipediacontent: Scipediacontent moved page Draft Content 798844445 to 238,295ew

2021-03-22T19:50:25Z

Scipediacontent moved page Draft Content 798844445 to 238,295ew

Scipediacontent: Created page with " == Abstract == Electric vehicles (EVs) have attracted growing attention in recent years. However, most existing research has not utilized actual traffic data and has not con..."

2021-03-22T19:49:42Z

Created page with " == Abstract == Electric vehicles (EVs) have attracted growing attention in recent years. However, most existing research has not utilized actual traffic data and has not con..."

New page

== Abstract ==

Electric vehicles (EVs) have attracted growing attention in recent years. However, most existing research has not utilized actual traffic data and has not considered real psychological decision-making of owners in analyzing the charging demand. On this basis, an urban EV fast-charging demand forecasting model based on a data-driven approach and human decision-making behavior is presented in this paper. In this methodology, Didi ride-hailing order trajectory data are firstly taken as the original dataset. Through data mining and fusion technology, the regenerated data and rules of traffic operation are obtained. Then, the single EV model with driving and charging behavior parameters is established. Furthermore, a human behavior decision-making model based on Regret Theory is introduced, which comprises the utility of time consumption and charging cost to plan driving paths and recommend fast-charging stations for vehicles. The rules obtained from data mining together with established models are combined to construct the &lsquo

Electric Vehicles&ndash

Power Grid&ndash

Traffic Network&rsquo

fusion architecture. At last, the actual urban traffic network in Nanjing is selected as an example to design the fast-charging demand load experiments in different scenarios. The results demonstrate that this proposed model is able to effectively predict the spatio-temporal distribution characteristics of urban fast-charging demands, and it more realistically simulates the decision-making psychology of owners&rsquo

charging behavior.

Document type: Article

== Full document ==
<pdf>Media:Draft_Content_798844445-beopen1152-5164-document.pdf</pdf>

== Original document ==

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

* [http://dx.doi.org/10.3390/en13061412 http://dx.doi.org/10.3390/en13061412] under the license https://creativecommons.org/licenses/by

* [https://www.mdpi.com/1996-1073/13/6/1412/pdf https://www.mdpi.com/1996-1073/13/6/1412/pdf] under the license http://creativecommons.org/licenses/by/3.0/

* [https://www.mdpi.com/1996-1073/13/6/1412 https://www.mdpi.com/1996-1073/13/6/1412],
: [https://doaj.org/toc/1996-1073 https://doaj.org/toc/1996-1073] under the license cc-by

* [https://www.mdpi.com/1996-1073/13/6/1412 https://www.mdpi.com/1996-1073/13/6/1412],
: [https://ideas.repec.org/a/gam/jeners/v13y2020i6p1412-d333869.html https://ideas.repec.org/a/gam/jeners/v13y2020i6p1412-d333869.html],
: [https://academic.microsoft.com/#/detail/3011148748 https://academic.microsoft.com/#/detail/3011148748]

* [https://www.mdpi.com/1996-1073/13/6/1412/pdf https://www.mdpi.com/1996-1073/13/6/1412/pdf],
: [http://dx.doi.org/10.3390/en13061412 http://dx.doi.org/10.3390/en13061412]

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

← Older revision	Revision as of 19:50, 22 March 2021
(No difference)

238,295ew - Revision history

Scipediacontent: Scipediacontent moved page Draft Content 798844445 to 238,295ew

Scipediacontent: Created page with " == Abstract == Electric vehicles (EVs) have attracted growing attention in recent years. However, most existing research has not utilized actual traffic data and has not con..."