energies-logo

Journal Browser

Journal Browser

Data Mining Applications for Charging of Electric Vehicles

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

Deadline for manuscript submissions: closed (10 February 2022) | Viewed by 25895

Special Issue Editors

Faculty of Management Science and Informatics, University of Žilina, Žilina, Slovakia
Interests: optimization; data science; modelling; intelligent transportation systems
Special Issues, Collections and Topics in MDPI journals
Department of Engineering, University of Napoli Parthenope, 80133 Naples, Italy
Interests: electrical power systems; electric vehicles; optimization models; data analysis; forecasting techniques
Special Issues, Collections and Topics in MDPI journals
Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen, 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. Dr. Ľuboš Buzna
Dr. Pasquale De Falco
Dr. 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 submissions that pass pre-check are 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 2600 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.

Keywords

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

Published Papers (5 papers)

Order results
Result details
Select all
Export citation of selected articles as:

Research

Jump to: Review

26 pages, 3009 KiB  
Article
Combining Ad Hoc Text Mining and Descriptive Analytics to Investigate Public EV Charging Prices in the United States
by David Trinko, Emily Porter, Jamie Dunckley, Thomas Bradley and Timothy Coburn
Energies 2021, 14(17), 5240; https://0-doi-org.brum.beds.ac.uk/10.3390/en14175240 - 24 Aug 2021
Cited by 5 | Viewed by 2987
Abstract
Electric vehicle (EV) charging infrastructure is present all over the United States, but charging prices vary greatly, both in amount and in the methods by which they are assessed. For this paper, we interpret and analyze charging price information from PlugShare, a crowd-sourced [...] Read more.
Electric vehicle (EV) charging infrastructure is present all over the United States, but charging prices vary greatly, both in amount and in the methods by which they are assessed. For this paper, we interpret and analyze charging price information from PlugShare, a crowd-sourced EV charging data platform. Because prices in these data exist in a semi-structured textual format, an ad hoc text mining approach is used to extract quantitative price information. Descriptive analytics of the processed dataset demonstrate how the prices of EV charging vary with charging level (Direct Current Fast Charging versus Level 2), geographic location, network provider, and location type. Our research indicates that a great deal of diversity and flexibility exists in structuring the prices of EV charging to enable incentives for shaping charging behaviors, but that it has yet to be widely standardized or utilized. Comparisons with estimates of the levelized cost of EV charging illustrate some of the challenges associated with operating and using these stations. Full article
(This article belongs to the Special Issue Data Mining Applications for Charging of Electric Vehicles)
Show Figures

Figure 1

20 pages, 7038 KiB  
Article
Consumer Preferences for Electric Vehicle Charging Infrastructure Based on the Text Mining Method
by Yuan-Yuan Wang, Yuan-Ying Chi, Jin-Hua Xu and Jia-Lin Li
Energies 2021, 14(15), 4598; https://0-doi-org.brum.beds.ac.uk/10.3390/en14154598 - 29 Jul 2021
Cited by 9 | Viewed by 3191
Abstract
The construction of charging infrastructure has a positive effect on promoting the diffusion of new energy vehicles (NEVs). This study uses natural language processing (NLP) technology to explore consumer preferences for charging infrastructure from consumer comments posted on public social media. The findings [...] Read more.
The construction of charging infrastructure has a positive effect on promoting the diffusion of new energy vehicles (NEVs). This study uses natural language processing (NLP) technology to explore consumer preferences for charging infrastructure from consumer comments posted on public social media. The findings show that consumers in first-tier cities pay more attention to charging infrastructure, and the number of comments accounted for 36% of the total. In all comments, consumers are most concerned about charging issues, national policy support, driving range, and installation of private charging piles. Among the charging modes of charging piles, direct current (DC) fast charging is more popular with consumers. The inability to find public charging piles in time to replenish power during travel or high energy consumption caused by air conditioning is the main reason for consumers’ range anxiety. Increasing battery performance, improving charging convenience, and construction of battery swap station are the main ways consumers prefer to increase driving range. Consumers’ preference for charging at home is the main reason for their high attention to the installation of private charging piles. However, the lack of fixed parking spaces and community properties have become the main obstacles to the installation of private charging piles. In addition, consumers in cities with different development levels pay different amounts of attention to each topic of charging infrastructure. Consumers in second-tier and above cities are most concerned about charging issues. Consumers in third-tier and above cities pay significantly more attention to the installation of private charging piles than consumers in fourth-tier and fifth-tier cities. Consumers in each city have almost the same amount of attention to driving range. Full article
(This article belongs to the Special Issue Data Mining Applications for Charging of Electric Vehicles)
Show Figures

Figure 1

21 pages, 5261 KiB  
Article
Data-Driven Charging Demand Prediction at Public Charging Stations Using Supervised Machine Learning Regression Methods
by Ahmad Almaghrebi, Fares Aljuheshi, Mostafa Rafaie, Kevin James and Mahmoud Alahmad
Energies 2020, 13(16), 4231; https://0-doi-org.brum.beds.ac.uk/10.3390/en13164231 - 16 Aug 2020
Cited by 101 | Viewed by 7589
Abstract
Plug-in Electric Vehicle (PEV) user charging behavior has a significant influence on a distribution network and its reliability. Generally, monitoring energy consumption has become one of the most important factors in green and micro grids; therefore, predicting the charging demand of PEVs (the [...] Read more.
Plug-in Electric Vehicle (PEV) user charging behavior has a significant influence on a distribution network and its reliability. Generally, monitoring energy consumption has become one of the most important factors in green and micro grids; therefore, predicting the charging demand of PEVs (the energy consumed during the charging session) could help to efficiently manage the electric grid. Consequently, three machine learning methods are applied in this research to predict the charging demand for the PEV user after a charging session starts. This approach is validated using a dataset consisting of seven years of charging events collected from public charging stations in the state of Nebraska, USA. The results show that the regression method, XGBoost, slightly outperforms the other methods in predicting the charging demand, with an RMSE equal to 6.68 kWh and R2 equal to 51.9%. The relative importance of input variables is also discussed, showing that the user’s historical average demand has the most predictive value. Accurate prediction of session charging demand, as opposed to the daily or hourly demand of multiple users, has many possible applications for utility companies and charging networks, including scheduling, grid stability, and smart grid integration. Full article
(This article belongs to the Special Issue Data Mining Applications for Charging of Electric Vehicles)
Show Figures

Figure 1

17 pages, 4780 KiB  
Article
Modeling the Charging Behaviors for Electric Vehicles Based on Ternary Symmetric Kernel Density Estimation
by Lixing Chen, Xueliang Huang and Hong Zhang
Energies 2020, 13(7), 1551; https://0-doi-org.brum.beds.ac.uk/10.3390/en13071551 - 26 Mar 2020
Cited by 13 | Viewed by 2154
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 [...] 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)
Show Figures

Graphical abstract

Review

Jump to: Research

35 pages, 1058 KiB  
Review
A Review of Electric Vehicle Load Open Data and Models
by Yvenn Amara-Ouali, Yannig Goude, Pascal Massart, Jean-Michel Poggi and Hui Yan
Energies 2021, 14(8), 2233; https://0-doi-org.brum.beds.ac.uk/10.3390/en14082233 - 16 Apr 2021
Cited by 54 | Viewed by 8095
Abstract
The field of electric vehicle charging load modelling has been growing rapidly in the last decade. In light of the Paris Agreement, it is crucial to keep encouraging better modelling techniques for successful electric vehicle adoption. Additionally, numerous papers highlight the lack of [...] Read more.
The field of electric vehicle charging load modelling has been growing rapidly in the last decade. In light of the Paris Agreement, it is crucial to keep encouraging better modelling techniques for successful electric vehicle adoption. Additionally, numerous papers highlight the lack of charging station data available in order to build models that are consistent with reality. In this context, the purpose of this article is threefold. First, to provide the reader with an overview of the open datasets available and ready to be used in order to foster reproducible research in the field. Second, to review electric vehicle charging load models with their strengths and weaknesses. Third, to provide suggestions on matching the models reviewed to six datasets found in this research that have not previously been explored in the literature. The open data search covered more than 860 repositories and yielded around 60 datasets that are relevant for modelling electric vehicle charging load. These datasets include information on charging point locations, historical and real-time charging sessions, traffic counts, travel surveys and registered vehicles. The models reviewed range from statistical characterization to stochastic processes and machine learning and the context of their application is assessed. Full article
(This article belongs to the Special Issue Data Mining Applications for Charging of Electric Vehicles)
Show Figures

Figure 1

Back to TopTop