Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
7.3
3.2
Journals
Energies
Special Issues
Integrated Solutions for Transportation and Energy Systems: Advancing Sustainable Mobility...
energies-logo
Submit to Special Issue Submit Abstract to Special Issue Review for Energies Propose a Special Issue

Journal Menu

► Journal Menu

Journal Browser

► Journal Browser
share announcement

Integrated Solutions for Transportation and Energy Systems: Advancing Sustainable Mobility and Power Infrastructure

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

Deadline for manuscript submissions: 25 February 2026 | Viewed by 1403

Special Issue Editors

Dr. Yanxia Wang

E-Mail Website
Guest Editor
College of Metropolitan Transportation, Beijing University of Technology, Beijing 100124, China
Interests: electrical vehicle; smart grid; optimization of transportation and energy systems
Special Issues, Collections and Topics in MDPI journals
Dr. Yihuan Li

E-Mail Website
Guest Editor
School of Control and Computer Engineering, North China Electric Power University, Beijing 102206, China
Interests: energy storage systems; machine learning; modeling and monitoring; optimization
Dr. Dapeng Yan

E-Mail Website
Guest Editor
School of Electrical Engineering, Xi’an Jiaotong University, Xi’an 710049, China
Interests: knowledge representation and reasoning; energy system sensing and assessment decision-making
Dr. Huibo Bi

E-Mail Website
Guest Editor
College of Metropolitan Transportation, Beijing University of Technology, Beijing 100124, China
Interests: smart energy; optimization and intelligent control
Dr. Shaojun Gan

E-Mail Website
Guest Editor
College of Metropolitan Transportation, Beijing University of Technology, Beijing 100124, China
Interests: artificial intelligence; maritime transportation control and optimization
Dr. Yiqiao Li

E-Mail Website
Guest Editor
Department of Civil Engineering, City College of New York, New York, NY 10025, USA
Interests: artificial intelligence in transportation; intelligent transportation systems; traffic monitoring; transportation data science; vehicle characterization

Special Issue Information

Dear Colleagues,

The convergence of transportation and energy systems is reshaping traditional infrastructure paradigms. As EV adoption continues to increase exponentially, it is creating new demands for power grids while simultaneously offering potential grid support services through vehicle-to-grid (V2G) technology. The intermittent nature of renewable energy sources such as solar and wind power introduces additional complexity in grid management, necessitating innovative solutions for energy storage and demand response. Furthermore, the development of smart grid technologies enables more sophisticated control and optimization strategies but also requires the careful consideration of system reliability, cybersecurity, and economic viability.

This Special Issue will explore innovative solutions that bridge transportation and energy systems, focusing on the synergies between electric mobility, renewable energy integration, and smart energy management. The intersection of these domains presents unique opportunities for technological innovation, policy development, and business model creation. By addressing the technical, economic, and social aspects of integrated transportation–energy systems, this Special Issue will contribute to the development of sustainable infrastructure that can support the transition to a low-carbon future. Recent advancements in artificial intelligence, Internet of Things (IoT), and distributed energy systems have opened up new possibilities for optimizing these integrated systems, making this an opportune time to examine cutting-edge research and practical applications in this field.

Scope and Objectives:

For this Special Issue, we invite high-quality original research articles, comprehensive reviews, and case studies that delve into a wide range of topics at the intersection of transportation and energy systems, with an emphasis on sustainable and smart mobility solutions. Specific themes include, but are not limited to, the following:

  1. Electric Vehicle Integration and Smart Charging Infrastructure
  • Advanced charging station placement optimization;
  • Vehicle-to-grid (V2G) technology implementation;
  • Smart charging strategies and load management;
  • User behavior analysis and charging demand prediction;
  • Economic and environmental impact assessment of EV adoption.
  1. Virtual Power Plants and Energy Networks
  • Aggregation of distributed energy resources, including EVs;
  • Real-time demand response strategies;
  • Energy trading mechanisms and market design;
  • Grid stability and reliability enhancement;
  • Advanced control algorithms for virtual power plants.
  1. Smart Transportation Energy Management Systems
  • Artificial intelligence applications in energy dispatch;
  • Big data analytics for transportation energy optimization;
  • Internet of Things (IoT) integration in transportation infrastructure;
  • Predictive maintenance and system reliability;
  • Cybersecurity in integrated energy–transportation systems.
  1. Policy and Economic Framework for Integrated Systems
  • Business models for integrated energy–transportation services;
  • Incentive mechanisms for sustainable transportation;
  • Environmental impact assessment methodologies;
  • Social acceptance and public engagement strategies.
  1. Advanced Modeling and Optimization Techniques for Transportation and Energy Systems
  • Multi-scale modeling approaches to complex systems;
  • Data-driven modeling techniques and validation methodologies;
  • Knowledge-based systems for transportation management;
  • Intelligent sensor networks for infrastructure monitoring;
  • Artificial intelligent technologies in traffic and energy management.

Dr. Yanxia Wang
Dr. Yihuan Li
Dr. Dapeng Yan
Dr. Huibo Bi
Dr. Shaojun Gan
Dr. Yiqiao Li
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

  • electric vehicle and smart charging infrastructure
  • virtual power plants
  • transportation energy management systems
  • policy and economic framework
  • advanced modeling and control
  • optimization techniques
  • energy storage and smart grid

Benefits of Publishing in a Special Issue

  • Ease of navigation: Grouping papers by topic helps scholars navigate broad scope journals more efficiently.
  • Greater discoverability: Special Issues support the reach and impact of scientific research. Articles in Special Issues are more discoverable and cited more frequently.
  • Expansion of research network: Special Issues facilitate connections among authors, fostering scientific collaborations.
  • External promotion: Articles in Special Issues are often promoted through the journal's social media, increasing their visibility.
  • Reprint: MDPI Books provides the opportunity to republish successful Special Issues in book format, both online and in print.

Further information on MDPI's Special Issue policies can be found here.

Published Papers (2 papers)

Download All Papers
Order results
Result details
Show export options expand_more Show export options expand_less
Select all
Export citation of selected articles as:

Research

26 pages, 15026 KB  
Article
Interactive Optimization of Electric Bus Scheduling and Overnight Charging
by Zvonimir Dabčević and Joško Deur
Energies 2025, 18(16), 4440; https://doi.org/10.3390/en18164440 - 21 Aug 2025
Viewed by 662
Abstract
The transition to fully electric bus (EB) fleets introduces new challenges in coordinating daily operations and managing charging energy needs, while accounting for infrastructure constraints. The paper proposes a three-stage optimization framework that integrates EB scheduling with overnight charging under realistic depot layout [...] Read more.
The transition to fully electric bus (EB) fleets introduces new challenges in coordinating daily operations and managing charging energy needs, while accounting for infrastructure constraints. The paper proposes a three-stage optimization framework that integrates EB scheduling with overnight charging under realistic depot layout constraints. In the first stage, a mixed-integer linear program (MILP) determines the minimum number of EBs with ample batteries and related schedules to complete all timetabled trips. With the fleet size fixed, the second stage minimizes the EB battery capacity by optimizing trip assignments. In the third stage, charging schedules are iteratively optimized for different numbers of chargers to minimize charger power capacity and charging cost, while ensuring each EB is fully recharged before its first trip on the following day. The matrix-shape depot layout imposes spatial and operational constraints that restrict the charging and movement of EBs based on their parking positions, with EBs remaining stationary overnight. The entire process is repeated by incrementing the fleet size until a saturation point is reached, beyond which no further reduction in battery capacity is observed. This results in a Pareto frontier showing trade-offs between required battery capacity, number of chargers, charger power capacity, and charging cost. The proposed method is applied to a real-world airport parking shuttle service, demonstrating its potential to reduce the battery size and charging infrastructure demands while maintaining full operational feasibility. Full article
(This article belongs to the Special Issue Integrated Solutions for Transportation and Energy Systems: Advancing Sustainable Mobility and Power Infrastructure)
►▼ Show Figures

Figure 1

26 pages, 2444 KB  
Article
A Multi-Stage Feature Selection and Explainable Machine Learning Framework for Forecasting Transportation CO2 Emissions
by Mohammad Ali Sahraei, Keren Li and Qingyao Qiao
Energies 2025, 18(15), 4184; https://doi.org/10.3390/en18154184 - 7 Aug 2025
Viewed by 426
Abstract
The transportation sector is a major consumer of primary energy and is a significant contributor to greenhouse gas emissions. Sustainable transportation requires identifying and quantifying factors influencing transport-related CO2 emissions. This research aims to establish an adaptable, precise, and transparent forecasting structure [...] Read more.
The transportation sector is a major consumer of primary energy and is a significant contributor to greenhouse gas emissions. Sustainable transportation requires identifying and quantifying factors influencing transport-related CO2 emissions. This research aims to establish an adaptable, precise, and transparent forecasting structure for transport CO2 emissions of the United States. For this reason, we proposed a multi-stage method that incorporates explainable Machine Learning (ML) and Feature Selection (FS), guaranteeing interpretability in comparison to conventional black-box models. Due to high multicollinearity among 24 initial variables, hierarchical feature clustering and multi-step FS were applied, resulting in five key predictors: Total Primary Energy Imports (TPEI), Total Fossil Fuels Consumed (FFT), Annual Vehicle Miles Traveled (AVMT), Air Passengers-Domestic and International (APDI), and Unemployment Rate (UR). Four ML methods—Support Vector Regression, eXtreme Gradient Boosting, ElasticNet, and Multilayer Perceptron—were employed, with ElasticNet outperforming the others with RMSE = 45.53, MAE = 30.6, and MAPE = 0.016. SHAP analysis revealed AVMT, FFT, and APDI as the top contributors to CO2 emissions. This framework aids policymakers in making informed decisions and setting precise investments. Full article
(This article belongs to the Special Issue Integrated Solutions for Transportation and Energy Systems: Advancing Sustainable Mobility and Power Infrastructure)
►▼ Show Figures

Figure 1

Show export options expand_more Show export options expand_less
Select all
Export citation of selected articles as:
 
Displaying articles 1-2
Back to TopTop