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Energy Transition Targets in Future Energy Systems: The Role of Electric Vehicles, Storage Technologies, Smart Grids and Hydrogen Systems

A special issue of Energies (ISSN 1996-1073). This special issue belongs to the section "C: Energy Economics and Policy".

Deadline for manuscript submissions: 30 May 2025 | Viewed by 1962

Special Issue Editor


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Guest Editor
Department of Business Development and Technology, Aarhus University, Birk Centerpark 15, 7400 Herning, Denmark
Interests: business development; new technologies; electric vehicles; smart cities; automotive markets; energy supply chains; investments; renewable energy sources; alternative fuels; innovation
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Several initiatives in decarbonizing the road-transport system are focusing on policy strategies, the transformation of the energy system and the deployment of charging infrastructure to further promote the adoption of electric vehicles as well as of zero-emission vehicles (ZEVs).

However, scaling up and deploying the number of innovative low-carbon technologies currently at high technology readiness is needed to reach the 2030 emission objectives. Technologies that are still in the pilot and demonstration phases and at even lower development levels are crucial for reaching the 2050 emission targets. The most pressing challenge is to speed up innovation projects to be ready for the market. To help reach this goal, this Special Issue aims to bring together the latest research and developments in this field, with a particular focus on addressing the challenges of EVs, analyzing the infrastructure within smart cities and exploring the role of AI. The topics covered in this Special Issue include but are not limited to the following:

  • Grid integration and management of renewable energy sources;
  • Battery technology and charging infrastructure;
  • Vehicle-to-grid (V2G), vehicle-to-home (V2H), and vehicle-to-vehicle (V2V) technologies;
  • Comparative LCA analysis of EVs and ZEVs;
  • Smart mobility and electrified transportation systems;
  • Energy policy and regulation of EVs in smart grids;
  • The role of AI;
  • Standards and interoperability of EV infrastructure systems.

This Special Issue invites original research articles, review papers, and short communications presenting state-of-the-art research, new methodologies, and innovative solutions to advance the field. All submissions will undergo a rigorous peer-review process to ensure the quality and relevance of the research presented. We expect this Special Issue to become a valuable resource for researchers, engineers, and practitioners to share their latest findings and to contribute to advancing the field.

Dr. Evanthia A. Nanaki
Guest Editor

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
  • zero-emission vehicles
  • renewable energy
  • energy and transportation
  • scenarios and forecasting
  • emerging technologies
  • hydrogen
  • fuel cells
  • smart cities
  • smart grids
  • vehicle-to-grid (V2G)
  • vehicle-to-home (V2H)
  • vehicle-to-vehicle (V2V)
  • supercapacitors
  • artificial intelligence
  • carbon neutrality

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Published Papers (1 paper)

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Research

17 pages, 1875 KiB  
Article
Toward Sustainable Mobility: AI-Enabled Automated Refueling for Fuel Cell Electric Vehicles
by Sofia Polymeni, Vasileios Pitsiavas, Georgios Spanos, Quentin Matthewson, Antonios Lalas, Konstantinos Votis and Dimitrios Tzovaras
Energies 2024, 17(17), 4324; https://doi.org/10.3390/en17174324 - 29 Aug 2024
Viewed by 879
Abstract
With the global transportation sector being a major contributor to greenhouse gas (GHG) emissions, transitioning to cleaner and more efficient forms of transportation is essential for mitigating climate change and improving air quality. Toward sustainable mobility, Fuel Cell Electric Vehicles (FCEVs) have emerged [...] Read more.
With the global transportation sector being a major contributor to greenhouse gas (GHG) emissions, transitioning to cleaner and more efficient forms of transportation is essential for mitigating climate change and improving air quality. Toward sustainable mobility, Fuel Cell Electric Vehicles (FCEVs) have emerged as a promising solution offering zero-emission transportation without sacrificing performance or range. However, FCEV adoption still faces significant challenges regarding refueling infrastructure. This work proposes an innovative refueling automation service for FCEVs to facilitate the refueling procedure and to increase the fuel cell lifetime, by leveraging (i) Big Data, namely, real-time mobility data and (ii) Machine Learning (ML) for the energy consumption forecasting to dynamically adjust refueling priorities. The proposed service was evaluated on a simulated FCEV energy consumption dataset, generated using both the Future Automotive Systems Technology Simulator and real-time data, including traffic information and details from a real-world on demand Public Transportation service in the Geneva Canton region. The experimental results showcased that all three ML algorithms achieved high accuracy in forecasting the vehicle’s energy consumption with very low errors on the order of 10% and below 20% for the normalized Mean Absolute Error and normalized Root Mean Squared Error metrics, respectively, indicating the high potential of the suggested service. Full article
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