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Data Mining Applications for Charging of Electric Vehicles II
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Data Mining Applications for Charging of Electric Vehicles II

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

Deadline for manuscript submissions: 18 September 2024 | Viewed by 5446

Special Issue Editors

Prof. Dr. Ľuboš Buzna

E-Mail Website
Guest Editor
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
Dr. Pasquale De Falco

E-Mail Website
Guest Editor
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

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 II”.

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
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 (4 papers)

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Research

27 pages, 3976 KiB  
Article
A Feasibility Study of Profiting from System Imbalance Using Residential Electric Vehicle Charging Infrastructure
by Marián Tomašov, Milan Straka, Dávid Martinko, Peter Braciník and Ľuboš Buzna
Energies 2023, 16(23), 7820; https://doi.org/10.3390/en16237820 - 28 Nov 2023
Viewed by 700
Abstract
Residential chargers are going to become the standard in the near future. Their operational cycles are closely tied to users’ daily routines, and the power consumption fluctuates between zero and peak levels. These types of installations are particularly challenging for the grid, especially [...] Read more.
Residential chargers are going to become the standard in the near future. Their operational cycles are closely tied to users’ daily routines, and the power consumption fluctuates between zero and peak levels. These types of installations are particularly challenging for the grid, especially concerning the balance of electricity production and consumption. Using battery storage in conjunction with renewable sources (e.g., photovoltaic power plants) represents a flexible solution for grid stabilization, but it is also associated with additional costs. Nowadays, grid authorities penalize a destabilization of the grid resulting from an increased imbalance between electricity generation and consumption and reward contributions to the system balance. Hence, there is a motivation for larger prosumers to make use of this mechanism to reduce their operational costs by better aligning their energy needs with the grid. This study explores the possibility of utilizing battery storage when it is not needed to fulfil its primary function of supporting charging electric vehicles, to generate some additional profit from providing a counter-imbalance. To test this idea, we develop an optimization model that maximizes the economic profit, considering system imbalance penalties/rewards, photovoltaic production, electric vehicle charging demand, and battery storage utilization. By means of computer simulation, we assess the overall operational costs while varying key installation parameters such as battery capacity and power, the installed power of photovoltaic panels and the prediction model’s accuracy. We identify conditions when counter-imbalance has proven to be a viable way to reduce installation costs. These conditions include temporal distribution of charging demand, electricity prices and photovoltaic production. For the morning time window, with a suitable setting of the installation parameters, the cost reduction reaches up to 14% compared to the situation without counter-imbalance. Full article
(This article belongs to the Special Issue Data Mining Applications for Charging of Electric Vehicles II)
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22 pages, 5534 KiB  
Article
PSO-Based Identification of the Li-Ion Battery Cell Parameters
by Tadeusz Białoń, Roman Niestrój and Wojciech Korski
Energies 2023, 16(10), 3995; https://doi.org/10.3390/en16103995 - 09 May 2023
Cited by 2 | Viewed by 1342
Abstract
The article describes the results of research aimed at identifying the parameters of the equivalent circuit of a lithium-ion battery cell, based on the results of HPPC (hybrid pulse power characterization) tests. The OCV (open circuit voltage) characteristic was determined, which was approximated [...] Read more.
The article describes the results of research aimed at identifying the parameters of the equivalent circuit of a lithium-ion battery cell, based on the results of HPPC (hybrid pulse power characterization) tests. The OCV (open circuit voltage) characteristic was determined, which was approximated using functions of various types, while making their comparison. The internal impedance of the cell was also identified in the form of a Thevenin RC circuit with one or two time constants. For this purpose, the HPPC pulse transients were approximated with a multi-exponential function. All of the mentioned approximations were carried out using an original method developed for this purpose, based on the PSO (particle swarm optimization) algorithm. As a result of the optimization experiments, the optimal configuration of the PSO algorithm was found. Three different cognition methods have been analyzed here: GB (global best), LB (local best), and FIPS (fully informed particle swarm). Three different swarm topologies were used: ring lattice, von Neumann, and FDR (fitness distance ratio). The choice of the cognition factor value was also analyzed, in order to provide a proper PSO convergence. The identified parameters of the cell model were used to build simulation models. Finally, the simulation results were compared with the results of the laboratory CDC (charge depleting cycle) test. Full article
(This article belongs to the Special Issue Data Mining Applications for Charging of Electric Vehicles II)
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24 pages, 5792 KiB  
Article
Women Will Drive the Demand for EVs in the Middle East over the Next 10 Years—Lessons from Today’s Kuwait and 1960s USA
by Andri Ottesen, Sumayya Banna and Basil Alzougool
Energies 2023, 16(9), 3756; https://doi.org/10.3390/en16093756 - 27 Apr 2023
Cited by 1 | Viewed by 1051
Abstract
The Middle East, Gulf Cooperation Council Countries (GCC), and Kuwait, in particular, are currently experiencing a similar transition as the USA in the 1970s regarding the empowerment and independence of women, fueled by a declining birth rate from four per women to less [...] Read more.
The Middle East, Gulf Cooperation Council Countries (GCC), and Kuwait, in particular, are currently experiencing a similar transition as the USA in the 1970s regarding the empowerment and independence of women, fueled by a declining birth rate from four per women to less than two. In addition, the percentage of women with university degrees has been increasing at a logarithmic rate every decade since the 1960s in the USA and since 1990 in Kuwait, resulting in women comprising well over half of all university graduates. This has led to women obtaining better jobs and enjoying greater independence to make their own decisions. In the 1960s, Toyota and other Japanese car manufactures used this phenomenon to penetrate the US market, with significant success. Their selling points were lower maintenance requirements, higher reliability, safety, better environment friendliness and slicker interior designs, the last being especially adapted to women’s tastes. We believe that Chinese and Korean electric vehicle (EV) manufacturers will employ the same playbook with similar success, as the Middle East accelerates its readiness for the EV mainstream market. In this study, this prediction was supported by a quantitative questionnaire of 234 educated female Kuwaiti drivers from the ages of 18 to 40 in Kuwait regarding their preferences regarding EVs. The findings indicate that potential female buyers favor EVs for their environmental benefits, regardless of their demographics. Moreover, potential female consumers are highly willing to purchase EVs in the future under three conditions: infrastructure availability, environmental development, and affordability. We believe that this group, in particular, will present the greatest opportunity to EV manufacturers over the next 10 years. Full article
(This article belongs to the Special Issue Data Mining Applications for Charging of Electric Vehicles II)
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22 pages, 540 KiB  
Article
Predicting and Managing EV Charging Demand on Electrical Grids: A Simulation-Based Approach
by Pramote Jaruwatanachai, Yod Sukamongkol and Taweesak Samanchuen
Energies 2023, 16(8), 3562; https://doi.org/10.3390/en16083562 - 20 Apr 2023
Cited by 5 | Viewed by 1914
Abstract
Electric vehicles (EVs) are becoming increasingly popular, and it is important for utilities to understand their charging characteristics to accurately estimate the demand on the electrical grid. In this work, we developed simulation models for different EV charging scenarios in the home sector. [...] Read more.
Electric vehicles (EVs) are becoming increasingly popular, and it is important for utilities to understand their charging characteristics to accurately estimate the demand on the electrical grid. In this work, we developed simulation models for different EV charging scenarios in the home sector. We used them to predict maximum demand based on the increasing penetration of EV consumers. We comprehensively reviewed the literature on EV charging technologies, battery capacity, charging situations, and the impact of EV loads. Our results suggest a method for visualizing the impact of EV charging loads by considering factors such as state of charge, arrival time, charging duration, rate of charge, maximum charging power, and involvement rate. This method can be used to model load profiles and determine the number of chargers needed to meet EV user demand. We also explored the use of a time-of-use (TOU) tariff as a demand response strategy, which encourages EV owners to charge their vehicles off-peak in order to avoid higher demand charges. Our simulation results show the effects of various charging conditions on load profiles and indicate that the current TOU price strategy can accommodate a 20% growth in EV consumers, while the alternative TOU price strategy can handle up to a 30% penetration level. Full article
(This article belongs to the Special Issue Data Mining Applications for Charging of Electric Vehicles II)
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