Special Issue "Data Mining Applications for Charging of Electric Vehicles"

A special issue of Energies (ISSN 1996-1073). This special issue belongs to the section "Electric Vehicles".

Deadline for manuscript submissions: 30 June 2021.

Special Issue Editors

Prof. Ľuboš Buzna
Guest Editor
Faculty of Management Science and Informatics, University of Žilina, Slovakia
Interests: optimization; data science; modelling; intelligent transportation systems
Dr. Pasquale De Falco
Guest Editor
Department of Engineering, University of Napoli Parthenope, Naples, Italy
Interests: energy forecasting; energy data analysis; renewable energy; dynamic rating of power system components; smart grids
Special Issues and Collections in MDPI journals
Assoc. Prof. Zhile Yang
Guest Editor
Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, China
Interests: artificial intelligence; evolutionary computation; electric vehicles; smart grids; energy forecasting

Special Issue Information

Dear Colleagues,

We are inviting submissions of original research and review papers to the Special Issue entitled “Data Mining Applications for Charging of Electric Vehicles”.

Electric mobility has the potential to contribute to improving energy security and mitigating greenhouse gas emissions. Recent data and available outlooks indicate continuous growth of electric vehicle (EV) sales and penetration. However, the share of EVs on roads compared to vehicles with an internal combustion engine, is still fairly small. The large-scale deployment of EVs is associated with significant policy, technical, environmental, and planning challenges, indicating the need for methods that are able to provide efficient and reliable support for decision making to guide the transition toward higher penetration of EVs. In recent years, due to the growing intelligence of EV infrastructure, the availability of field data of on-road and charging EVs has significantly improved, providing new research opportunities.

The main aim of this Special Issue is to gather novel data-centric methods and applications by combining modeling with field data in the following, but not limited to, domains relevant to EVs:

  • Assessment of EV impacts, such as economic, environmental, technical, social, etc. impacts
  • Integration of EV charging into smart grids
  • EV load forecasting
  • EV sales forecasting
  • EV charging infrastructure planning
  • Charging strategies for EVs in public transport
  • Data-driven approaches to battery management
  • EV users’ charging behavior
  • EV users’ attitude analyses
  • EV charging data management

Prof. Ľuboš Buzna
Dr. Pasquale De Falco
Assoc. Prof. Zhile Yang
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All papers will be peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Energies is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 1800 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.


  • Electric vehicles
  • Charging infrastructure
  • Decisions making support
  • Data science
  • Machine learning
  • Statistical analysis
  • Optimization
  • Simulation

Published Papers (1 paper)

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Open AccessArticle
Modeling the Charging Behaviors for Electric Vehicles Based on Ternary Symmetric Kernel Density Estimation
Energies 2020, 13(7), 1551; https://doi.org/10.3390/en13071551 - 26 Mar 2020
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 [...] Read more.
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. Full article
(This article belongs to the Special Issue Data Mining Applications for Charging of Electric Vehicles)
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