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Fast-Charging Station for Electric Vehicles: Challenges and Issues
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Fast-Charging Station for Electric Vehicles: Challenges and Issues

A special issue of World Electric Vehicle Journal (ISSN 2032-6653).

Deadline for manuscript submissions: 15 June 2025 | Viewed by 24858

Special Issue Editors

Dr. Tao Chen

E-Mail Website
Guest Editor
School of Electrical Engineering, Southeast University, Nanjing 210018, China
Interests: renewable energy; power systems; electric vehicles; machine learning
Special Issues, Collections and Topics in MDPI journals
Dr. Xiangjun Quan

E-Mail Website
Guest Editor
1. School of Electrical Engineering, Southeast University, Nanjing 210018, China
2. Jiangsu Provincial Key Laboratory of Smart Grid Technology and Equipment, Nanjing 210018, China
Interests: advanced power electronics control; grid synchronization; renewable energy integration and smart grids; grid-forming and lower-inertia system
Special Issues, Collections and Topics in MDPI journals
Dr. Yuanshi Zhang

E-Mail Website
Guest Editor
School of Electrical Engineering, Southeast University, Nanjing 214135, China
Interests: electric power system qualitative control; flexible DC power transmission; fine power grid control
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

We are inviting submissions to a Special Issue on the “Fast-Charging Station for Electric Vehicles: Challenges and Issues”. Today, there is growing attention focused on more efficient and fast charging services for an increasing number of electric vehicles (EVs) to guarantee sustainable societal prosperity all over the world. Various types of EVs, as well as critical charging infrastructures, are de facto becoming important agents of reducing carbon footprints and fossil fuel consumption in most countries. However, EVs have always been suffering from two major bottlenecks, namely range anxiety and lacking ubiquitous charging service. In the future, extremely high EV penetration will lead to huge energy demand on distribution networks and need different modes of access to fast-charging stations, such as the home, workplace, and/or public charging. We are pleased to invite you to contribute to this topic, which focuses on discussing the key factors and challenges for planning EV fast charging infrastructure. It aims to lay a solid foundation for the methodology that can be used to improve the design and implementation of fast-charging service infrastructure. Papers are solicited and cover aspects of EV charging infrastructure and service, including the following topics and any other relevant topics that may not be directly specified.

  • Planning of EV (fast) charging infrastructure;
  • Fast charging standard and policy;
  • EV smart charging scheduling;
  • Fast charging hardware and equipment;
  • Data analytics for charging infrastructure and service;
  • Battery swapping and business model;
  • EV charging demand forecasting;
  • Power grid interaction of (fast-) charging stations;
  • Charging pricing mechanism design;
  • Smart charging for renewable energy integration;
  • Power and transport network nexus;
  • Business models for charging infrastructure development;
  • Impact of charging demand on distribution network;
  • Intelligent transportation system with fast charging station;
  • Grid support for the fast-charging station;
  • Distributed energy resource scheduling and aggregation.

Dr. Tao Chen
Dr. Xiangjun Quan
Dr. Yuanshi Zhang
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. World Electric Vehicle Journal is an international peer-reviewed open access monthly 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 1400 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.

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Published Papers (7 papers)

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Research

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21 pages, 1066 KiB  
Article
An Integrated Analysis of Electric Battery Charging Station Selection—Thailand Inspired
by Adisak Suvittawat and Nutchanon Suvittawat
World Electr. Veh. J. 2024, 15(9), 418; https://doi.org/10.3390/wevj15090418 - 13 Sep 2024
Viewed by 1236
Abstract
The growing adoption of electric vehicles (EVs) necessitates a well-distributed network of charging stations. However, selecting optimal locations for these stations is a complex issue influenced by geographic, demographic, technical, and economic factors. This study aims to fill the gaps in previous research [...] Read more.
The growing adoption of electric vehicles (EVs) necessitates a well-distributed network of charging stations. However, selecting optimal locations for these stations is a complex issue influenced by geographic, demographic, technical, and economic factors. This study aims to fill the gaps in previous research by providing a comprehensive analysis of factors influencing the selection of EV battery charging stations. This research focuses on integrating geographic, demographic, technical, and infrastructure considerations to inform strategic placement decisions. A quantitative approach was employed, using questionnaires distributed to 300 entrepreneurs in Thailand’s EV charging station sector. The data were analyzed using descriptive statistics and structural equation modeling (SEM) to evaluate the relationships among the influencing factors. The results reveal that technical and infrastructure factors significantly impact economic and financial considerations, which in turn influence the selection of charging stations. Additionally, geographic and demographic factors play a crucial role in shaping economic outcomes and the strategic placement of these stations. A holistic approach that integrates these diverse factors is essential for the strategic deployment of EV charging infrastructure, which supports increased EV adoption and contributes to environmental sustainability. Full article
(This article belongs to the Special Issue Fast-Charging Station for Electric Vehicles: Challenges and Issues)
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20 pages, 3111 KiB  
Article
Short-Term Charging Load Prediction of Electric Vehicles with Dynamic Traffic Information Based on a Support Vector Machine
by Qipei Zhang, Jixiang Lu, Wenteng Kuang, Lin Wu and Zhaohui Wang
World Electr. Veh. J. 2024, 15(5), 189; https://doi.org/10.3390/wevj15050189 - 28 Apr 2024
Viewed by 906
Abstract
This study proposes a charging demand forecasting model for electric vehicles (EVs) that takes into consideration the characteristics of EVs with transportation and mobile load. The model utilizes traffic information to evaluate the influence of traffic systems on driving and charging behavior, specifically [...] Read more.
This study proposes a charging demand forecasting model for electric vehicles (EVs) that takes into consideration the characteristics of EVs with transportation and mobile load. The model utilizes traffic information to evaluate the influence of traffic systems on driving and charging behavior, specifically focusing on the characteristics of EVs with transportation and mobile load. Additionally, it evaluates the effect of widespread charging on the distribution network. An urban traffic network model is constructed based on the multi-intersection features, and a traffic network–distribution network interaction model is determined according to the size of the urban road network. Type classification simplifies the charging and discharging characteristics of EVs, enabling efficient aggregation of EVs. The authors have built a singular EV transportation model and an EV charging queue model is established. The EV charging demand is forecasted and then used as an input in the support vector machine (SVM) model. The final projection value for EV charging load is determined by taking into account many influencing elements. Compared to the real load, the proposed method’s feasibility and effectiveness are confirmed. Full article
(This article belongs to the Special Issue Fast-Charging Station for Electric Vehicles: Challenges and Issues)
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12 pages, 8904 KiB  
Article
Comparison of EV Fast Charging Protocols and Impact of Sinusoidal Half-Wave Fast Charging Methods on Lithium-Ion Cells
by Sai Bhargava Althurthi, Kaushik Rajashekara and Tutan Debnath
World Electr. Veh. J. 2024, 15(2), 54; https://doi.org/10.3390/wevj15020054 - 6 Feb 2024
Cited by 2 | Viewed by 2138
Abstract
In electric vehicle fast charging systems, it is important to minimize the effect of fast charging on the grid and it is also important to operate the charging system at high efficiencies. In order to achieve these objectives, in this paper, a sinusoidal [...] Read more.
In electric vehicle fast charging systems, it is important to minimize the effect of fast charging on the grid and it is also important to operate the charging system at high efficiencies. In order to achieve these objectives, in this paper, a sinusoidal half-wave DC current charging protocol and a sinusoidal half-wave pulsed current charging protocol are proposed for the fast charging of Li-ion batteries. A detailed procedure is presented for implementing the following proposed methods: (a) a pre-defined half-sine wave current function and (b) a pulsed half-sine wave current method. Unlike the conventional full-wave sinusoidal ripple current charging protocols, the proposed study does not utilize any sinusoidal full-wave ripple. The performance of these new charging methods on Ni-Co-Al-type Li-cells is studied and compared with the existing constant current and positive pulsed current charging protocols, which have been discussed in the existing literature. In addition, the changes in the electrochemical impedance spectrograph of each cell are examined to study the effects of the applied charging methods on the internal resistance of the Li cell. Finally, the test results are presented for 250 life cycles of charging at 2C (C: charging rate) and the degradation in cell capacities are compared among the four different methods for the Ni-Co-Al-type Li cell. Full article
(This article belongs to the Special Issue Fast-Charging Station for Electric Vehicles: Challenges and Issues)
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14 pages, 2063 KiB  
Article
Electric Vehicle and Photovoltaic Power Scenario Generation under Extreme High-Temperature Weather
by Xiaofei Li, Chi Li and Chen Jia
World Electr. Veh. J. 2024, 15(1), 11; https://doi.org/10.3390/wevj15010011 - 2 Jan 2024
Cited by 3 | Viewed by 2021
Abstract
In recent years, with the intensification of global warming, extreme weather has become more frequent, intensifying the uncertainty of new energy output and load power, and seriously affecting the safe operation of power systems. Scene generation is an effective method to solve the [...] Read more.
In recent years, with the intensification of global warming, extreme weather has become more frequent, intensifying the uncertainty of new energy output and load power, and seriously affecting the safe operation of power systems. Scene generation is an effective method to solve the uncertainty problem of stochastic planning of integrated systems of new energy generation. Therefore, this paper proposes a scenario generation and scenario reduction model of photovoltaic (PV) output and electric vehicle (EV) load power under extreme weather based on the copula function. Firstly, the non-parametric kernel density estimation method is used to fit a large number of sample data. The kernel density estimation expressions of PV and EV powers under extreme weather conditions are obtained and the corresponding goodness of fit tests are carried out. Then, a variety of joint distribution models based on the copula function are established to judge the goodness of fit of each model, and the optimal copula function is selected as the joint probability distribution function by combining the Kendall and Spearman correlation coefficients of each model. Finally, the optimal copula joint probability distribution is used to generate PV and EV power scenarios. The data of extremely hot weather in a certain province were selected for an example analysis. The results show that the output scenario obtained conforms to the correlation under this extreme weather, and has higher accuracy in reflecting the actual PV output and load power in this province under this extreme weather, which can provide a reference for reliability analyses of power systems and power grid planning. Full article
(This article belongs to the Special Issue Fast-Charging Station for Electric Vehicles: Challenges and Issues)
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13 pages, 464 KiB  
Article
Modeling EV Charging Station Loads Considering On-Road Wireless Charging Capabilities
by Walied Alfraidi, Mohammad Shalaby and Fahad Alaql
World Electr. Veh. J. 2023, 14(11), 313; https://doi.org/10.3390/wevj14110313 - 19 Nov 2023
Cited by 2 | Viewed by 2259
Abstract
Electric vehicle (EV) customers are expected to charge EV batteries at a rapid EV charging station or via on-road wireless EV charging systems when possible, as per their charging needs to successfully complete any remaining trips and reach their destination. When on-road wireless [...] Read more.
Electric vehicle (EV) customers are expected to charge EV batteries at a rapid EV charging station or via on-road wireless EV charging systems when possible, as per their charging needs to successfully complete any remaining trips and reach their destination. When on-road wireless EV charging systems are considered as an alternative charging method for EVs, this can affect the load of a rapid EV charging station in terms of time and magnitude. Hence, this paper presents a probabilistic framework for estimating the arrival rate of EVs at an EV rapid charging station, considering the availability of on-road wireless charging systems as an alternative charging method. The proposed model incorporates an Electric Vehicle Decision Tree that predicts the times when EVs require rapid charging based on realistic transportation data. A Monte Carlo simulation approach is used to capture uncertainties in EV user decisions regarding charging types. A queuing model is then developed to estimate the charging load for multiple EVs at the charging station, with and without the consideration of on-road EV wireless charging systems. A case study and simulation results considering a 32-bus distribution system and the US National Household Travel Survey (NHTS) data are presented and discussed to demonstrate the impact of on-road wireless EV charging on the loads of an rapid EV charging station. It is observed that having on-road wireless EV charging as complementary charging to EV charging stations helps to significantly reduce the peak load of the charging station, which improves the power system capacity and defers the need for system upgrades. Full article
(This article belongs to the Special Issue Fast-Charging Station for Electric Vehicles: Challenges and Issues)
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28 pages, 3663 KiB  
Article
A Critical Review of NIO’s Business Model
by Alessandro Pisano, Manuel Saba and Jair Arrieta Baldovino
World Electr. Veh. J. 2023, 14(9), 251; https://doi.org/10.3390/wevj14090251 - 7 Sep 2023
Cited by 2 | Viewed by 14469
Abstract
The present study reports a critical review of NIO′s business model considering the evolving landscape of the electric vehicle market and servicing. The objective of this study is to develop a comprehensive framework that facilitates the identification of key elements characterizing a company’s [...] Read more.
The present study reports a critical review of NIO′s business model considering the evolving landscape of the electric vehicle market and servicing. The objective of this study is to develop a comprehensive framework that facilitates the identification of key elements characterizing a company’s business model and highlights ongoing transformations crucial for adaptation and survival in a rapidly changing environmental context. Focusing on the case study of NIO, a relatively young Chinese original equipment manufacturer (OEM) specializing in high-tech electric cars, the research delves into the challenging scenario of the Chinese electric vehicle market, which recently faced a bubble in 2023. The market proliferation, supply chain disruptions, and price wars triggered by Tesla have resulted in a survival struggle for numerous automotive startups, leaving larger companies with increasing market shares. Despite facing adversities, NIO managed to secure a promising segment catering to premium-range battery electric vehicles (BEVs), establishing a competitive advantage through differentiation. By pursuing ambitious investments, the company aims to create economies of scope and achieve cost leadership, venturing into new market sectors and vertically integrating the production chain. Given NIO’s agility in adapting to market conditions, aggressive entry into new segments, and a strategic vision for the future, it serves as an excellent candidate for testing and validating the proposed framework. The research sheds light on NIO’s trajectory and offers insights into its potential for sustained growth in the dynamic electric vehicle market. Full article
(This article belongs to the Special Issue Fast-Charging Station for Electric Vehicles: Challenges and Issues)
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Review

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22 pages, 2678 KiB  
Review
A Comprehensive Review of Optimizing Multi-Energy Multi-Objective Distribution Systems with Electric Vehicle Charging Stations
by Mahesh Kumar, Aneel Kumar, Amir Mahmood Soomro, Mazhar Baloch, Sohaib Tahir Chaudhary and Muzamil Ahmed Shaikh
World Electr. Veh. J. 2024, 15(11), 523; https://doi.org/10.3390/wevj15110523 - 14 Nov 2024
Viewed by 443
Abstract
Electric vehicles worldwide provide numerous key advantages in the energy sector. They are advantageous over fossil fuel vehicles in many aspects: for example, they consume no fuel, are economical, and only require charging the internal batteries, which power the motor for propulsion. Thus, [...] Read more.
Electric vehicles worldwide provide numerous key advantages in the energy sector. They are advantageous over fossil fuel vehicles in many aspects: for example, they consume no fuel, are economical, and only require charging the internal batteries, which power the motor for propulsion. Thus, due to their numerous advantages, research is necessary to improve the technological aspects that can enhance electric vehicles’ overall performance and efficiency. However, electric vehicle charging stations are the key hindrance to their adoption. Charging stations will affect grid stability and may lead to altering different parameters, e.g., power losses and voltage deviation when integrated randomly into the distribution system. The distributed generation, along with charging stations with the best location and size, can be a solution that mitigates the above concerns. Metaheuristic techniques can be used to find the optimal siting and sizing of distributed generations and electric vehicle charging stations. This review provides an exhaustive review of various methods and scientific research previously undertaken to optimize the placement and dimensions of electric vehicle charging stations and distributed generation. We summarize the previous work undertaken over the last five years on the multi-objective placement of distributed generations and electric vehicle charging stations. Key areas have focused on optimization techniques, technical parameters, IEEE networks, simulation tools, distributed generation types, and objective functions. Future development trends and current research have been extensively explored, along with potential future advancement and gaps in knowledge. Therefore, at the conclusion of this review, the optimization of electric vehicle charging stations and distributed generation presents both the practical and theoretical importance of implementing metaheuristic algorithms in real-world scenarios. In the same way, their practical integration will provide the transportation system with a robust and sustainable solution. Full article
(This article belongs to the Special Issue Fast-Charging Station for Electric Vehicles: Challenges and Issues)
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