EconPapers    
Economics at your fingertips  
 

Modeling the Charging Behaviors for Electric Vehicles Based on Ternary Symmetric Kernel Density Estimation

Lixing Chen, Xueliang Huang and Hong Zhang
Additional contact information
Lixing Chen: School of Electrical &Information Engineering, Jiangsu University of Technology, Changzhou 213001, China
Xueliang Huang: School of Electrical Engineering, Southeast University, Nanjing 210096, China
Hong Zhang: School of Electrical &Information Engineering, Jiangsu University of Technology, Changzhou 213001, China

Energies, 2020, vol. 13, issue 7, 1-17

Abstract: The accurate modeling of the charging behaviors for electric vehicles (EVs) is the basis for the charging load modeling, the charging impact on the power grid, orderly charging strategy, and planning of charging facilities. Therefore, an accurate joint modeling approach of the arrival time, the staying time, and the charging capacity for the EVs charging behaviors in the work area based on ternary symmetric kernel density estimation (KDE) is proposed in accordance with the actual data. First and foremost, a data transformation model is established by considering the boundary bias of the symmetric KDE in order to carry out normal transformation on distribution to be estimated from all kinds of dimensions to the utmost extent. Then, a ternary symmetric KDE model and an optimum bandwidth model are established to estimate the transformed data. Moreover, an estimation evaluation model is also built to transform simulated data that are generated on a certain scale with the Monte Carlo method by means of inverse transformation, so that the fitting level of the ternary symmetric KDE model can be estimated. According to simulation results, a higher fitting level can be achieved by the ternary symmetric KDE method proposed in this paper, in comparison to the joint estimation method based on the edge KDE and the ternary t-Copula function. Moreover, data transformation can effectively eliminate the boundary effect of symmetric KDE.

Keywords: electric vehicles; charging behaviors; kernel density estimation; optimum bandwidth (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2020
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (5)

Downloads: (external link)
https://www.mdpi.com/1996-1073/13/7/1551/pdf (application/pdf)
https://www.mdpi.com/1996-1073/13/7/1551/ (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:gam:jeners:v:13:y:2020:i:7:p:1551-:d:337436

Access Statistics for this article

Energies is currently edited by Ms. Agatha Cao

More articles in Energies from MDPI
Bibliographic data for series maintained by MDPI Indexing Manager ().

 
Page updated 2025-03-19
Handle: RePEc:gam:jeners:v:13:y:2020:i:7:p:1551-:d:337436