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Electric Vehicles for Sustainable Transport and Energy: 2nd Edition
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Electric Vehicles for Sustainable Transport and Energy: 2nd Edition

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

Deadline for manuscript submissions: 31 August 2025 | Viewed by 4358

Special Issue Editors

Dr. David Jiménez

E-Mail Website
Guest Editor
Information Processing and Telecommunication Center, Universidad Politécnica de Madrid (UPM), 28040 Madrid, Spain
Interests: electric vehicle; energy management and optimization; vehicle-to-grid (V2G) integration
Special Issues, Collections and Topics in MDPI journals
Dr. Jesus Fraile-Ardanuy

E-Mail Website
Guest Editor
Information Processing and Telecommunication Center, Universidad Politécnica de Madrid (UPM), 28040 Madrid, Spain
Interests: renewable energy; distributed generation; electric vehicle integration; smart grids/microgrids; energy storage systems
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

"Electric Vehicles for Sustainable Transport and Energy: 2nd Edition" is a Special Issue dedicated to advancing the understanding and application of modeling and simulation techniques in the realm of Electric Vehicles (EVs). This issue aims to explore innovative research and cutting-edge developments that can contribute to the sustainable transformation of transport and energy sectors.

The scope of this Special Issue includes, but is not limited to, the following:

  • EV Powertrain Modeling: in-depth analysis of electric vehicle powertrain components, such as batteries, motors, and control systems, using advanced modeling and simulation approaches.
  • Energy Management and Optimization: research on energy management strategies, charging infrastructure, and optimization techniques to enhance the efficiency and reliability of EVs within smart grid systems.
  • Vehicle-to-Grid (V2G) Integration: investigations into bidirectional power flow and the potential for EVs to serve as distributed energy resources, supporting grid stability and demand response.
  • Life Cycle Assessment (LCA) of EVs: studies on the environmental impact of EVs throughout their entire life cycle, including manufacturing, usage, and end-of-life considerations.
  • EV Fleet Modeling: application of simulations to study the impact of electrified fleets on urban transportation systems and the environment.
  • Policy and Economics: assessment of policies, incentives, and economic models that promote the adoption of EVs and sustainable transportation practices.
  • Virtual Prototyping and Testing: use of simulation tools to develop virtual prototypes and conduct safety and performance testing, reducing the need for physical prototypes.
  • Interoperability and Standardization: exploration of standardization efforts to facilitate the integration of EVs into existing infrastructure and ensure seamless interoperability.
  • Vehicle Automation and Autonomous EVs: examination of the role of modeling and simulation in the development and validation of autonomous electric vehicles.

Contributors to this Special Issue are encouraged to present original research, case studies, and reviews that advance the state of the art in EV modeling and simulation, enabling the transition towards greener and more sustainable transportation and energy systems.

Dr. David Jiménez
Dr. Jesus Fraile-Ardanuy
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

  • EV powertrain modeling
  • energy management and optimization
  • vehicle-to-grid (V2G) integration
  • life cycle assessment (LCA) of EVs
  • EV fleet modeling
  • policy and economics
  • virtual prototyping and testing
  • interoperability and standardization
  • vehicle automation and autonomous EVs

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Related Special Issue

Published Papers (3 papers)

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Research

35 pages, 9007 KiB  
Article
AI-Driven Predictive Control for Dynamic Energy Optimization in Flying Cars
by Mohammed Gronfula and Khairy Sayed
Energies 2025, 18(7), 1781; https://doi.org/10.3390/en18071781 - 2 Apr 2025
Viewed by 618
Abstract
This study presents an AI-driven energy management system (EMS) for a hybrid electric flying car, integrating multiple power sources—including solid-state batteries, Li-ion batteries, fuel cells, solar panels, and wind turbines—to optimize power distribution across various flight phases. The proposed EMS dynamically adjusts power [...] Read more.
This study presents an AI-driven energy management system (EMS) for a hybrid electric flying car, integrating multiple power sources—including solid-state batteries, Li-ion batteries, fuel cells, solar panels, and wind turbines—to optimize power distribution across various flight phases. The proposed EMS dynamically adjusts power allocation during takeoff, cruise, landing, and ground operations, ensuring optimal energy utilization while minimizing losses. A MATLAB-based simulation framework is developed to evaluate key performance metrics, including power demand, state of charge (SOC), system efficiency, and energy recovery through regenerative braking. The findings show that by optimizing renewable energy collecting, minimizing battery depletion, and dynamically controlling power sources, AI-based predictive control dramatically improves energy efficiency. While carbon footprint assessment emphasizes the environmental advantages of using renewable energy sources, SOC analysis demonstrates that regenerative braking prolongs battery life and lowers overall energy use. AI-optimized energy distribution also lowers overall operating costs while increasing reliability, according to life-cycle cost assessment (LCA), which assesses the economic sustainability of important components. Sensitivity analysis under sensor noise and environmental disturbances further validates system robustness, demonstrating that efficiency remains above 84% even under adverse conditions. These findings suggest that AI-enhanced hybrid propulsion can significantly improve the sustainability, economic feasibility, and real-world performance of future flying car systems, paving the way for intelligent, low-emission aerial transportation. Full article
(This article belongs to the Special Issue Electric Vehicles for Sustainable Transport and Energy: 2nd Edition)
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21 pages, 1658 KiB  
Article
A Comprehensive Analysis of the Economic Implications, Challenges, and Opportunities of Electric Vehicle Adoption in Indonesia
by Natalina Damanik, Risa Saraswani, Dzikri Firmansyah Hakam and Dea Mardha Mentari
Energies 2025, 18(6), 1384; https://doi.org/10.3390/en18061384 - 11 Mar 2025
Viewed by 2338
Abstract
Electric vehicles (EVs) are a recognized solution for lowering greenhouse gas emissions and decreasing oil dependency, especially in Indonesia. Existing studies have explored the economic impact, challenges, and opportunities of EV adoption separately, lacking a holistic analysis. This study addresses this gap by [...] Read more.
Electric vehicles (EVs) are a recognized solution for lowering greenhouse gas emissions and decreasing oil dependency, especially in Indonesia. Existing studies have explored the economic impact, challenges, and opportunities of EV adoption separately, lacking a holistic analysis. This study addresses this gap by providing a comprehensive assessment of the economic implications, challenges, and opportunities of EV adoption in Indonesia through a systematic literature review of 65 peer-reviewed articles, industry reports, and reputable publications from 2016 to 2024. The document analysis involved keyword-based literature selection, content analysis of economic metrics, and synthesis into key thematic areas. The findings reveal that EV sales in Indonesia have been rising annually, influenced by cost, driving range, environmental impact, technological features, charging infrastructure, battery, and government policies and incentives. EV adoption has positively impacted Indonesia’s GDP, attracted Foreign Direct Investment (FDI), created jobs, and reduced fuel consumption and imports. However, several challenges persist, including high EV costs, inadequate charging infrastructure, societal readiness, battery replacement costs and waste management, and limited model variety. Despite these challenges, opportunities exist in the form of market growth, FDI from nickel resources, energy security, job creation, and industrial expansion. Recommendations for creating a conducive EV ecosystem are provided for relevant stakeholders. Full article
(This article belongs to the Special Issue Electric Vehicles for Sustainable Transport and Energy: 2nd Edition)
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13 pages, 3354 KiB  
Article
On-Line Parameter Identification and SOC Estimation for Lithium-Ion Batteries Based on Improved Sage–Husa Adaptive EKF
by Xuan Tang, Hai Huang, Xiongwu Zhong, Kunjun Wang, Fang Li, Youhang Zhou and Haifeng Dai
Energies 2024, 17(22), 5722; https://doi.org/10.3390/en17225722 - 15 Nov 2024
Cited by 3 | Viewed by 968
Abstract
For the Battery Management System (BMS) to manage and control the battery, State of Charge (SOC) is an important battery performance indicator. In order to identify the parameters of the LiFePO4 battery, this paper employs the forgetting factor recursive least squares (FFRLS) [...] Read more.
For the Battery Management System (BMS) to manage and control the battery, State of Charge (SOC) is an important battery performance indicator. In order to identify the parameters of the LiFePO4 battery, this paper employs the forgetting factor recursive least squares (FFRLS) method, which considers the computational volume and model correctness, to determine the parameters of the LiFePO4 battery. On this basis, the two resistor-capacitor equivalent circuit model is selected for estimating the SOC of the Li-ion battery by combining the extended Kalman filter (EKF) with the Sage–Husa adaptive algorithm. The positivity is improved by modifying the system noise estimation matrix. The paper concludes with a MATLAB 2016B simulation, which serves to validate the SOC estimation algorithm. The results demonstrate that, in comparison to the conventional EKF, the enhanced EKF exhibits superior estimation precision and resilience to interference, along with enhanced convergence during the estimation process. Full article
(This article belongs to the Special Issue Electric Vehicles for Sustainable Transport and Energy: 2nd Edition)
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