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Applied Sciences
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Vehicle-to-Grid Systems: The Trends and Smart Grid Interaction Technologies
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Vehicle-to-Grid Systems: The Trends and Smart Grid Interaction Technologies

A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Energy Science and Technology".

Deadline for manuscript submissions: 20 August 2024 | Viewed by 6779

Special Issue Editors

Dr. Truong Minh Ngoc Bui

E-Mail Website
Guest Editor
Energy Innovation Centre, WMG, University of Warwick, Coventry CV4 7AL, UK
Interests: EV/HEV dynamic modelling; control and simulation; vehicle supervisory control; battery energy storage; energy management systems; battery management systems; vehicle-to-grid; smart grids
Special Issues, Collections and Topics in MDPI journals
Dr. Sheikh Muhammad

E-Mail Website
Guest Editor
School of Engineering, University of Warwick, Coventry CV4 7AL, UK
Interests: electric vehicles; ev powertrain; energy storage; power electronics; smart grids, v2g; battery testing and charatcerisation
Special Issues, Collections and Topics in MDPI journals
Dr. Truong Quang Dinh

E-Mail Website
Guest Editor
WMG, University of Warwick, Coventry CV4 7AL, UK
Interests: lithium-ion batteries; battery thermal management; mechatronics; control theories and applications; modelling; management and control of energy systems; advanced propulsion systems
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

The development of smart grids allows electric vehicles to play a new role, vehicle-to-grid (V2G), in an electricity–power interaction between the electric vehicle and the power grid by delivering electricity back to the grid or controlling the charging rate. This Special Issue aims to collect high-quality reviews and research articles on the topic of vehicle-to-grid applications. We encourage researchers from various fields within the journal’s scope to contribute papers that highlight the latest research and developments in their fields or to invite relevant experts and colleagues to do so. Topics of interest for this Special Issue include, but are not limited to:

  • State-of-the-art technologies and new developments for V2G applications
  • Review articles on V2G demonstrator projects and learning
  • Small/large-scale V2G integration and application
  • EV interface standards and protocols with charging infrastructure that permit aggregator control of EV batteries
  • Aggregator control, scheduling in V2G systems
  • Battery conditioning and smart charge strategies for improved V2G operations
  • Energy management system in V2G systems
  • Understanding the impact of battery degradation and a lifetime participating in V2G schemes
  • Security and privacy perspective in V2G networks
  • Environmental and socio-economic benefits and challenges of V2G systems

Dr. Truong Minh Ngoc Bui
Dr. Sheikh Muhammad
Dr. Truong Quang Dinh
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. Applied Sciences 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 2400 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 vehicle
  • energy management system
  • smart grids
  • vehicle to grid
  • energy storage
  • battery degradation
  • energy arbitrage
  • ev charging infreastructure
  • smart charge
  • load balancing
  • load leveling
  • grid stability
  • aggregator control
  • vehicle-to-grid demonstrator
  • cyber security

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Published Papers (5 papers)

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Research

24 pages, 6251 KiB  
Article
A New Adaptive Strategy for Enhancing the Stability of Isolated Grids through the Integration of Renewable Energy and V2G Management
by Edisson Villa-Ávila, Paul Arévalo, Danny Ochoa-Correa, Vinicio Iñiguez-Morán and Francisco Jurado
Appl. Sci. 2024, 14(14), 6380; https://doi.org/10.3390/app14146380 - 22 Jul 2024
Viewed by 598
Abstract
The integration of renewable energy sources into isolated microgrids introduces significant power fluctuations due to their intermittent nature. This study addresses the need for advanced power smoothing methods to enhance the stability of isolated networks. An innovative adaptive strategy is presented, combining photovoltaic [...] Read more.
The integration of renewable energy sources into isolated microgrids introduces significant power fluctuations due to their intermittent nature. This study addresses the need for advanced power smoothing methods to enhance the stability of isolated networks. An innovative adaptive strategy is presented, combining photovoltaic solar generation with vehicle-to-grid technology, utilizing an enhanced adaptive moving average filter with fuzzy logic control. The primary objective is to dynamically optimize the time frame of the Li-ion battery energy storage system for immediate power stabilization, leveraging the high energy density and rapid response capabilities inherent in electric vehicle batteries. The methodology encompasses data acquisition from photovoltaic panels, definition of fuzzy logic control rules, and implementation of the proposed method within a computer-controlled system connected to a bidirectional three-phase inverter. Experimental results highlight the proposed method’s superiority over conventional moving averages and ramp-rate filters. Full article
(This article belongs to the Special Issue Vehicle-to-Grid Systems: The Trends and Smart Grid Interaction Technologies)
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19 pages, 710 KiB  
Article
Optimization Challenges in Vehicle-to-Grid (V2G) Systems and Artificial Intelligence Solving Methods
by Marc Escoto, Antoni Guerrero, Elnaz Ghorbani and Angel A. Juan
Appl. Sci. 2024, 14(12), 5211; https://doi.org/10.3390/app14125211 - 15 Jun 2024
Viewed by 649
Abstract
Vehicle-to-grid (V2G) systems play a key role in the integration of electric vehicles (EVs) into smart grids by enabling bidirectional energy flows between EVs and the grid. Optimizing V2G operations poses significant challenges due to the dynamic nature of energy demand, grid constraints, [...] Read more.
Vehicle-to-grid (V2G) systems play a key role in the integration of electric vehicles (EVs) into smart grids by enabling bidirectional energy flows between EVs and the grid. Optimizing V2G operations poses significant challenges due to the dynamic nature of energy demand, grid constraints, and user preferences. This paper addresses the optimization challenges in V2G systems and explores the use of artificial intelligence (AI) methods to tackle these challenges. The paper provides a comprehensive analysis of existing work on optimization in V2G systems and identifies gaps where AI-driven algorithms, machine learning, metaheuristic extensions, and agile optimization concepts can be applied. Case studies and examples demonstrate the efficacy of AI-driven algorithms in optimizing V2G operations, leading to improved grid stability, cost optimization, and user satisfaction. Furthermore, agile optimization concepts are introduced to enhance flexibility and responsiveness in V2G optimization. The paper concludes with a discussion on the challenges and future directions for integrating AI-driven methods into V2G systems, highlighting the potential for these intelligent algorithms and methods. Full article
(This article belongs to the Special Issue Vehicle-to-Grid Systems: The Trends and Smart Grid Interaction Technologies)
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29 pages, 12794 KiB  
Article
An EV SRM Drive and Its Interconnected Operations Integrated into Grid, Microgrid, and Vehicle
by Wei-Kai Gu, Chen-Wei Yang and Chang-Ming Liaw
Appl. Sci. 2024, 14(7), 3032; https://doi.org/10.3390/app14073032 - 4 Apr 2024
Viewed by 665
Abstract
This paper presents an electric vehicle (EV) switched reluctance motor (SRM) drive with incorporated operation capabilities integrated into the utility grid, the microgrid, and another EV. The motor drive DC-link voltage is established from the battery through an interleaved boost/buck converter with fault [...] Read more.
This paper presents an electric vehicle (EV) switched reluctance motor (SRM) drive with incorporated operation capabilities integrated into the utility grid, the microgrid, and another EV. The motor drive DC-link voltage is established from the battery through an interleaved boost/buck converter with fault tolerance. The varied DC-link voltage can improve driving performance and reduce battery energy consumption over a wide speed range. Through a well-designed current control scheme, speed control scheme, and dynamic commutation tuning scheme, the established SRM drive possesses good performance in the motor driving mode. During deceleration, the regenerative braking energy can be effectively recovered to the battery. When the EV is in idle mode, the grid-to-vehicle (G2V) charging operation can be conducted through the bidirectional switch mode rectifier (SMR) and CLLC resonant converter. Satisfactory charging performance with good line drawn power quality and galvanic isolation is preserved. Conversely, the vehicle-to-grid (V2G) discharging operation can be performed. The EV can make movable energy storage device applications. Finally, the interconnected operations of the developed EV SRM drive to vehicle and microgrid are presented. Through vehicle-to-vehicle (V2V) operation, it can supply energy to the nearby EV when the battery is exhausted and needs roadside assistance. In addition, microgrid-to-vehicle (M2V) and vehicle-to-microgrid (V2M) operations can also be conductible. The EV battery can be charged from the microgrid. Conversely, it can also provide energy support to the microgrid. Full article
(This article belongs to the Special Issue Vehicle-to-Grid Systems: The Trends and Smart Grid Interaction Technologies)
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16 pages, 5823 KiB  
Article
Measurement of Regional Electric Vehicle Adoption Using Multiagent Deep Reinforcement Learning
by Seung Jun Choi and Junfeng Jiao
Appl. Sci. 2024, 14(5), 1826; https://doi.org/10.3390/app14051826 - 23 Feb 2024
Cited by 1 | Viewed by 1254
Abstract
This study explores the socioeconomic disparities observed in the early adoption of Electric Vehicles (EVs) in the United States. A multiagent deep reinforcement learning-based policy simulator was developed to address the disparities. The model, tested using data from Austin, Texas, indicates that neighborhoods [...] Read more.
This study explores the socioeconomic disparities observed in the early adoption of Electric Vehicles (EVs) in the United States. A multiagent deep reinforcement learning-based policy simulator was developed to address the disparities. The model, tested using data from Austin, Texas, indicates that neighborhoods with higher incomes and a predominantly White demographic are leading in EV adoption. To help low-income communities keep pace, we introduced tiered subsidies and incrementally increased their amounts. In our environment, with the reward and policy design implemented, the adoption gap began to narrow when the incentive was equivalent to an increase in promotion from 20% to 30%. Our study’s framework provides a new means for testing policy scenarios to promote equitable EV adoption. We encourage future studies to extend our foundational study by adding specifications. Full article
(This article belongs to the Special Issue Vehicle-to-Grid Systems: The Trends and Smart Grid Interaction Technologies)
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46 pages, 1065 KiB  
Article
Multi-Objective Routing Optimization in Electric and Flying Vehicles: A Genetic Algorithm Perspective
by Muhammad Alolaiwy, Tarik Hawsawi, Mohamed Zohdy, Amanpreet Kaur and Steven Louis
Appl. Sci. 2023, 13(18), 10427; https://doi.org/10.3390/app131810427 - 18 Sep 2023
Cited by 5 | Viewed by 2333
Abstract
The advent of electric and flying vehicles (EnFVs) has brought significant advancements to the transportation industry, offering improved sustainability, reduced congestion, and enhanced mobility. However, the efficient routing of messages in EnFVs presents unique challenges that demand specialized algorithms to address their specific [...] Read more.
The advent of electric and flying vehicles (EnFVs) has brought significant advancements to the transportation industry, offering improved sustainability, reduced congestion, and enhanced mobility. However, the efficient routing of messages in EnFVs presents unique challenges that demand specialized algorithms to address their specific constraints and objectives. This study analyzes several case studies that investigate the effectiveness of genetic algorithms (GAs) in optimizing routing for EnFVs. The major contributions of this research lie in demonstrating the capability of GAs to handle complex optimization problems with multiple objectives, enabling the simultaneous consideration of factors like energy efficiency, travel time, and vehicle utilization. Moreover, GAs offer a flexible and adaptive approach to finding near-optimal solutions in dynamic transportation systems, making them suitable for real-world EnFV networks. While GAs show promise, there are also limitations, such as computational complexity, difficulty in capturing real-world constraints, and potential sub-optimal solutions. Addressing these challenges, the study highlights several future research directions, including the integration of real-time data and dynamic routing updates, hybrid approaches with other optimization techniques, consideration of uncertainty and risk management, scalability for large-scale routing problems, and enhancing energy efficiency and sustainability in routing. By exploring these avenues, researchers can further improve the efficiency and effectiveness of routing algorithms for EnFVs, paving the way for their seamless integration into modern transportation systems. Full article
(This article belongs to the Special Issue Vehicle-to-Grid Systems: The Trends and Smart Grid Interaction Technologies)
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