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Energies
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Sustainable Transportation Systems with Electric and Autonomous Vehicles
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Sustainable Transportation Systems with Electric and Autonomous Vehicles

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

Deadline for manuscript submissions: 10 September 2025 | Viewed by 589

Special Issue Editor

Dr. Yi He

E-Mail Website
Guest Editor
Center for Integrated Mobility Sciences, National Renewable Energy Laboratory, Golden, CO 80401, USA
Interests: sustainable transportation system; charging infrastructure planning; smart charging management; network modeling and optimization
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Driven by the urgent need to address environmental challenges, reduce greenhouse gas emissions, and create more efficient, resilient urban systems, sustainable transportation has been recognized as a global goal for decades. Sustainable transportation aims to reduce the environmental impact of mobility by promoting cleaner and advanced technologies, such as electric and autonomous vehicles. Electric vehicles (EVs) contribute by reducing dependence on fossil fuels and lowering greenhouse gas emissions, while autonomous vehicles (AVs) enhance safety, reduce traffic congestion, and optimize energy use through smarter routing and platooning. Together, they can transform urban mobility, freight logistics, and public transit, making transportation systems not only more sustainable, but also more accessible and efficient. This Special Issue aims to explore and highlight the most recent developments in electric and autonomous vehicle technologies, addressing key aspects like energy efficiency, autonomous driving algorithms, vehicle safety, infrastructure planning, etc.

Potential research topics include, but are not limited to, the following:

  1. Planning and policy innovations for electric and/or autonomous vehicles.
  2. Public perception and acceptance of electric and/or autonomous vehicles.
  3. Vehicle-to-Grid (V2G) systems and their environmental benefits.
  4. Advancements in battery technology and their implications for EV sustainability.
  5. Freight transportation with electric and autonomous vehicles.
  6. Life cycle assessment of electric vehicles vs. conventional vehicles.
  7. The role of artificial intelligence in enhancing autonomous vehicle performance.

Dr. Yi He
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

  • sustainable transportation
  • electric vehicle
  • autonomous vehicle
  • electric vehicle use
  • autonomous vehicle policy
  • battery technology
  • cost analysis

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Published Papers (1 paper)

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Research

30 pages, 5167 KiB  
Article
Revolutionizing Electric Vehicle Charging Stations with Efficient Deep Q Networks Powered by Multimodal Bioinspired Analysis for Improved Performance
by Sugunakar Mamidala, Yellapragada Venkata Pavan Kumar and Rammohan Mallipeddi
Energies 2025, 18(7), 1750; https://doi.org/10.3390/en18071750 - 31 Mar 2025
Viewed by 324
Abstract
The rapid growth of electric vehicle (EV) adoption presents significant challenges in planning efficient charging infrastructure, including suboptimal station placement, energy consumption, and rising infrastructural costs. The conventional methods, such as grey wolf optimization (GWO), fail to address real-time user demand and dynamic [...] Read more.
The rapid growth of electric vehicle (EV) adoption presents significant challenges in planning efficient charging infrastructure, including suboptimal station placement, energy consumption, and rising infrastructural costs. The conventional methods, such as grey wolf optimization (GWO), fail to address real-time user demand and dynamic factors like fluctuating grid loads and environmental impact. These approaches rely on fixed models, often leading to inefficient energy use, higher operational costs, and increased traffic congestion. This paper proposes a novel framework that integrates deep Q networks (DQNs) for real-time charging optimization, coupled with multimodal bioinspired algorithms like ant lion optimization (ALO) and moth flame optimization (MFO). Unlike conventional geographic placement models that overlook evolving travel patterns, this system dynamically adapts to user behavior, optimizing both onboard and offboard charging systems. The DQN enables continuous learning from changing demand and grid conditions, while ALO and MFO identify optimal station locations, reducing energy consumption and emissions. The proposed framework incorporates dynamic pricing and demand response strategies. These adjustments help balance energy usage, reducing costs and preventing overloading of the grid during peak times, offering real-time adaptability, optimized station placement, and energy efficiency. To improve the performance of the system, the proposed framework ensures more sustainable, cost-effective EV infrastructural planning, minimized environmental impacts, and enhanced charging efficiency. From the results for the proposed system, we recorded various performance parameters such as the installation cost, which decreased to USD 1200 per unit, i.e., a 20% cost efficiency increase, optimal energy utilization increases to 85% and 92% during peak hours and off-peak hours respectively, a charging slot availability increase to 95%, a 30% carbon emission reduction, and 95% performance retention under the stress condition. Further, the power quality is improved by reducing the sag, swell, flicker, and notch by 2 V, 3 V, 0.05 V, and 0.03 V, respectively, with an increase in efficiency to 89.9%. This study addresses critical gaps in real-time flexibility, cost-effective station deployment, and grid resilience by offering a scalable and intelligent EV charging solution. Full article
(This article belongs to the Special Issue Sustainable Transportation Systems with Electric and Autonomous Vehicles)
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