Latest revision as of 20:50, 22 March 2021

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

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

• 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 under the license http://creativecommons.org/licenses/by/3.0/

• https://www.mdpi.com/1996-1073/13/6/1412,

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

• https://www.mdpi.com/1996-1073/13/6/1412,

https://ideas.repec.org/a/gam/jeners/v13y2020i6p1412-d333869.html,

https://academic.microsoft.com/#/detail/3011148748

• https://www.mdpi.com/1996-1073/13/6/1412/pdf,

http://dx.doi.org/10.3390/en13061412

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