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Future Transportation
Special Issues
Advanced Research on Electric Vehicles
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Advanced Research on Electric Vehicles

A special issue of Future Transportation (ISSN 2673-7590).

Deadline for manuscript submissions: 20 August 2026 | Viewed by 1380

Special Issue Editors

Dr. Rajesh Ravi

E-Mail Website
Guest Editor
LERMA Laboratory, School of Aerospace and Automotive Engineering, International University of Rabat, Rabat 11100, Morocco
Interests: electric vehicles; advanced combustion engines; heat exchangers; waste heat recovery; bio-fuels; pollution control
Dr. Mustapha Faqir

E-Mail Website
Guest Editor
LERMA Laboratory, School of Aerospace and Automotive Engineering, International University of Rabat, Rabat 11100, Morocco
Interests: electric vehicles; desalination; CPV; solar farm monitoring; advanced materials for aeronautics

Special Issue Information

Dear Colleagues,

The global imperative for sustainable transportation has catalyzed an unprecedented paradigm shift toward electric vehicle technologies, fundamentally transforming the automotive and transportation sectors. Electric vehicles represent a critical nexus of interdisciplinary research, encompassing advanced electrical engineering, materials science, control theory, computational intelligence, and sustainable energy systems. The electrification of transportation infrastructure constitutes not only a technological transition, but also a comprehensive reimagining of mobility ecosystems wherein vehicles function as integral components of intelligent, interconnected energy networks. Contemporary research in electric vehicle systems demands rigorous investigation across multiple scales of analysis, from the molecular-level mechanisms governing electrochemical energy storage to the macroscopic integration of vehicle fleets within smart grid architectures. The inherent complexity of electric vehicle systems necessitates sophisticated modeling approaches, advanced control methodologies, and innovative design paradigms that transcend traditional automotive engineering boundaries. Furthermore, the accelerating deployment of electric vehicles worldwide presents profound challenges in infrastructure development, grid stability, thermal management, and the optimization of energy utilization across diverse operational contexts.

This Special Issue solicits original, high-quality research contributions that advance the theoretical foundations, methodological innovations, and practical implementations of electric vehicle technologies. We particularly encourage submissions that demonstrate rigorous analytical frameworks, experimental validation, or computational studies that significantly enhance our understanding of electric vehicle systems and their integration within sustainable transportation networks. Both fundamental investigations and application-oriented research that address critical technological barriers are welcomed.

The scope of this Special Issue encompasses, but is not limited to, the following research domains:

  • Behavioral models of electric vehicle users.
  • Electrification of public transport systems.
  • Impact of EV adoption on traffic flow and congestion.
  • Economic models for EV incentives and policy impacts.
  • Electric vehicle supply chains and their modeling in transport systems.
  • Data-driven approaches for EV transport modeling.
  • Electric vehicle integration with alternative transport modes (e.g., micromobility, shared mobility like e-bikes and e-scooters).
  • Decarbonization pathways for transport systems through EV adoption.
  • Advanced electric powertrain architectures and electric machine design.
  • Battery management systems and state estimation methodologies.
  • Thermal management strategies for electric vehicles.
  • Charging technologies and infrastructure development.
  • Vehicle-to-grid integration and bidirectional power flow.
  • Vehicle dynamics and advanced control systems.
  • Autonomous and intelligent electric vehicle systems.
  • Structural design methodologies and lightweight materials.
  • Range prediction and energy-efficient routing methodologies.
  • Integration of renewable energy sources with electric vehicle charging ecosystems.
  • Life cycle assessment and sustainability analysis.

Dr. Rajesh Ravi
Dr. Mustapha Faqir
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 250 words) can be sent to the Editorial Office for assessment.

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. Future Transportation 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 1200 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
  • battery technology
  • electric powertrain systems
  • charging infrastructure
  • vehicle-to-grid integration
  • autonomous electric vehicles
  • sustainable transportation

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Published Papers (3 papers)

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Research

19 pages, 9676 KB  
Article
A Modular AI Framework for Electric Truck Fleet Transition: Addressing Multi-Dimensional Complexity Through Organizational Readiness
by Christina Rehmeier and Lars Boserup Iversen
Future Transp. 2026, 6(2), 89; https://doi.org/10.3390/futuretransp6020089 - 17 Apr 2026
Abstract
The transition from diesel to electric trucks faces a critical adoption gap despite technological maturity and favorable economics. This study identifies multi-dimensional planning complexity, spanning technical, economic, operational, and organizational dimensions, as a primary barrier that existing decision support tools fail to address. [...] Read more.
The transition from diesel to electric trucks faces a critical adoption gap despite technological maturity and favorable economics. This study identifies multi-dimensional planning complexity, spanning technical, economic, operational, and organizational dimensions, as a primary barrier that existing decision support tools fail to address. Through systematic literature review and analysis of Danish transport sector data, we develop the AI-Readiness Framework for Fleet Electrification (ARFFE), a modular decision support system adapted to different organizational readiness levels. Our secondary data analysis illustrates that two frequently overlooked factors, the CO2-differentiated road tax savings of 430,000–465,000 DKK over five years and charging strategy decisions creating cost differences of 930,000 DKK, have greater economic impact than traditionally emphasized factors. The framework comprises five progressive modules mapped across four readiness stages and four planning dimensions, creating an integrated decision support system for evaluating an estimated 50,000+ scenarios. This research contributes theoretically by proposing AI as a “mediating technology” in socio-technical transitions and practically by providing an actionable framework illustrated through Danish transport sector analysis. Full article
(This article belongs to the Special Issue Advanced Research on Electric Vehicles)
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17 pages, 1027 KB  
Article
Performance Comparison of Rule-Based, ECMS, and DP Control Strategies for Mild Hybrid Electric Vehicles
by Gulnora Shermuxammad Yakhshilikova and Sanjarbek Ruzimov
Future Transp. 2026, 6(2), 58; https://doi.org/10.3390/futuretransp6020058 - 5 Mar 2026
Viewed by 426
Abstract
This study introduces and compares online rule-based and optimization-based energy management strategies for a mild hybrid electric vehicle, with their performance evaluated against an offline Dynamic Programming benchmark. A structured rule-based strategy is proposed to enforce engine operation along its optimal efficiency line, [...] Read more.
This study introduces and compares online rule-based and optimization-based energy management strategies for a mild hybrid electric vehicle, with their performance evaluated against an offline Dynamic Programming benchmark. A structured rule-based strategy is proposed to enforce engine operation along its optimal efficiency line, while the remaining power demand is balanced by the electric motor. To achieve charge-sustaining battery operation, a soft state of charge regulation mechanism is incorporated. An Equivalent Consumption Minimization Strategy (ECMS) is also developed using a precise formulation of battery equivalent fuel consumption computed from instantaneous engine and electric path efficiencies, instead of constant efficiencies used in the literature. DP, which provides a globally optimal solution over the entire driving cycle, is employed as a benchmark for assessing the rule-based and ECMS strategies. The control strategies are compared under charge-sustaining conditions, considering engine and motor operation characteristics, overall fuel consumption, and battery usage intensity. Furthermore, the influence of load shifting between the internal combustion engine and the electric motor on overall vehicle performance is analyzed. Fuel consumption decreases by 13.5% relative to the engine-only baseline with the proposed ECMS with precise equivalent fuel consumption, and DP yields an additional 1.6% benefit. Compared with the developed rule-based controller, ECMS nearly halves the battery usage intensity, and DP provides 3.1% further reduction relative to ECMS. Full article
(This article belongs to the Special Issue Advanced Research on Electric Vehicles)
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30 pages, 1710 KB  
Article
Potential Analysis of a Novel Disposition Approach for Mixed-Electrified Truck Fleets Using Bidirectional Charging for Vehicle-to-Grid Integration
by Tom Winkler, Marcel Brödel, Niclas Klein, Anna Paper and Markus Lienkamp
Future Transp. 2026, 6(1), 50; https://doi.org/10.3390/futuretransp6010050 - 20 Feb 2026
Viewed by 511
Abstract
Global greenhouse gas emissions must be reduced to meet the targets of the Paris Climate Accords. This study quantifies the potential energy cost savings of a holistic disposition approach for mixed-electrified heavy-duty truck fleets. Electrifying heavy-duty trucks reduces energy costs compared to traditional [...] Read more.
Global greenhouse gas emissions must be reduced to meet the targets of the Paris Climate Accords. This study quantifies the potential energy cost savings of a holistic disposition approach for mixed-electrified heavy-duty truck fleets. Electrifying heavy-duty trucks reduces energy costs compared to traditional diesel-powered baselines. On-site energy generation further decreases electrification expenses. Bidirectional vehicle-to-grid participation also contributes to lowering energy costs. A mixed-integer linear programming optimization algorithm has been developed to incorporate these three approaches into the fleet’s disposition decisions. Real-world data have been utilized, including commercial order datasets, diesel prices, on-site-generated electrical energy prices, and vehicle-to-grid prices. Cost savings start at an average of 17% for small fleets with limited electrification and unfavorable price scenarios. However, they can reach net revenue generation for large fleets with high electrification and favorable price scenarios. A daily surplus of fleet energy costs can be achieved, with vehicle-to-grid revenues surpassing the costs of energy consumed. Ensuring battery electric heavy-duty trucks are available during high-revenue periods and operating during low-revenue times can lower overall fleet energy costs for commercial operators and improve power grid stability. By turning energy costs into net surpluses, this approach provides a financial incentive that could accelerate the transition to greenhouse-gas-neutral transport. Full article
(This article belongs to the Special Issue Advanced Research on Electric Vehicles)
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