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Electric Vehicles: Latest Advances and Prospects for Sustainable Energy Systems...
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Electric Vehicles: Latest Advances and Prospects for Sustainable Energy Systems and Sustainable Mobility

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

Deadline for manuscript submissions: closed (10 March 2026) | Viewed by 24656

Special Issue Editors

Dr. Gonçalo O. Duarte

E-Mail Website
Guest Editor
ISEL—Instituto Superior de Engenharia de Lisboa, Instituto Politécnico de Lisboa, 1959 Lisbon, Portugal
Interests: electrical vehicles; energy consumption; on-road data collection; internal combustion engines; hybrid and plug-in hybrid vehicles; exhaust emissions
Dr. Patricia Baptista

E-Mail Website
Guest Editor
IN+ Center for Innovation, Technology and Policy Research, Instituto Superior Técnico, Lisbon, Portugal
Interests: low-carbon systems; life-cycle assessment; energy and environmental impacts
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

In recent years, electric vehicle sales have surged, replacing both light- and heavy-duty vehicles. This trend extends to individual mobility solutions, including mopeds, bicycles, and more.

This Special Issue focuses on the most recent technological breakthroughs, specifically in the design and performance of electric vehicle propulsion systems. We welcome contributions to energy management algorithms, encompassing propulsion system components and considerations for driving and comfort constraints, such as vehicle HVACs. Additionally, research on battery advancements, charging technologies, and the life cycle performance of these technologies is encouraged.

The real-world operation of electric vehicles, whether for individual or professional mobility purposes, must be scrutinized. The usage of electric propulsion in different areas of the transportation sector requires a shift from the typical energy sources to electricity. Consequently, the growing energy demand stemming from this trend will necessitate additional support from the electricity network, highlighting the imperative for designing a more robust and resilient energy system, whether on a local or national scale.

Research on these topics is welcome, including case study analyses with a focus on technological development, real-world applications, case studies, forecasting scenarios or comprehensive reviews.

Dr. Gonçalo O. Duarte
Dr. Patricia Baptista
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. 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
  • propulsion system
  • energy use
  • electricity production

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Published Papers (6 papers)

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Research

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19 pages, 5045 KB  
Article
Quantifying Energy Transfer Impacts of Dynamic Wireless Charging for Light-Duty EVs in Lisbon, Portugal
by José Carvalho, Patrícia C. Baptista and Gonçalo O. Duarte
Energies 2026, 19(9), 2055; https://doi.org/10.3390/en19092055 - 24 Apr 2026
Viewed by 320
Abstract
Dynamic wireless power transfer can reduce electric vehicles’ charging downtime and range anxiety, but its benefits depend on route characteristics and system design. This work develops an integrated numerical framework combining (i) route-specific drive-cycle analysis, (ii) identification of candidate charging segments based on [...] Read more.
Dynamic wireless power transfer can reduce electric vehicles’ charging downtime and range anxiety, but its benefits depend on route characteristics and system design. This work develops an integrated numerical framework combining (i) route-specific drive-cycle analysis, (ii) identification of candidate charging segments based on speed, stops and slope constraints, (iii) a physics-informed inductive wireless power transfer model and (iv) a Matlab/Simulink vehicle energy model to quantify energy demand, transferred energy and state-of-charge evolution. Two vehicle types (a passenger light-duty vehicle and a light commercial van) and multiple Lisbon Metropolitan Area routes are analyzed, including commuting, ride-hailing and urban logistics operations. Results show that low-speed, stop-rich urban corridors achieve the highest transfer rates (typically 0.4 kWh/km and over 2 kWh for more than 15 stops in the analyzed cases), whereas expressway deployments are much less effective (down to 0.1 kWh/km and 0.5 kWh below 5 stops) unless congestion lowers average speeds. The proposed workflow provides a replicable basis to identify candidate segments and to size wireless power transfer and corridor length for city-scale deployment scenarios. Full article
(This article belongs to the Special Issue Electric Vehicles: Latest Advances and Prospects for Sustainable Energy Systems and Sustainable Mobility)
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20 pages, 2262 KB  
Article
A Comparative Life Cycle Assessment of Carbon Emissions for Battery Electric Vehicle Types
by Yan Zhu, Jie Zhang and Yan Long
Energies 2026, 19(2), 377; https://doi.org/10.3390/en19020377 - 13 Jan 2026
Viewed by 1396
Abstract
While battery electric vehicles (BEVs) are pivotal for transport decarbonization, existing life cycle assessments (LCAs) often confound vehicle design effects with inter-brand manufacturing variations. In this study, a comparative cradle-to-grave LCA was conducted for three distinct BEV segments—a sedan, an SUV, and an [...] Read more.
While battery electric vehicles (BEVs) are pivotal for transport decarbonization, existing life cycle assessments (LCAs) often confound vehicle design effects with inter-brand manufacturing variations. In this study, a comparative cradle-to-grave LCA was conducted for three distinct BEV segments—a sedan, an SUV, and an MPV, produced by a single manufacturer on a shared platform. Leveraging detailed bills of materials, plant-level energy data, and region-specific emission factors for a functional unit of 150,000 km, we quantify greenhouse gas emissions across the full life cycle. Results show the total emissions scale with vehicle size from 25 to 31 t CO2-eq. However, the MPV exhibits the highest functional carbon efficiency, with the lowest emissions per unit of interior volume. Material production and operational electricity use dominate the emission profile, with end-of-life metal recycling providing a 15–20% mitigation credit. Scenario modeling reveals that grid decarbonization can slash life cycle emissions by around 30%, while advanced battery recycling offers a further 15–18% reduction. These findings highlight that the climate benefits of BEVs are closely linked to progress in power system decarbonization, and provide references for future optimization of low-carbon vehicle production and reuse. Full article
(This article belongs to the Special Issue Electric Vehicles: Latest Advances and Prospects for Sustainable Energy Systems and Sustainable Mobility)
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17 pages, 5848 KB  
Article
Highly Reliable Power Circuit Configuration with SiC Chopper Module for Hybrid Fuel Cell and Battery Power System for Urban Air Mobility (UAM) Applications
by Moon-Seop Choi and Chong-Eun Kim
Energies 2025, 18(12), 3197; https://doi.org/10.3390/en18123197 - 18 Jun 2025
Cited by 1 | Viewed by 1113
Abstract
This paper proposes a high-reliability power conversion system optimized for Urban Air Mobility (UAM) applications, which utilizes silicon carbide (SiC) chopper modules within a hybrid fuel cell and battery structure. The system features a redundant power configuration that employs both a main and [...] Read more.
This paper proposes a high-reliability power conversion system optimized for Urban Air Mobility (UAM) applications, which utilizes silicon carbide (SiC) chopper modules within a hybrid fuel cell and battery structure. The system features a redundant power configuration that employs both a main and an auxiliary battery to ensure continuous and stable power supply, even under emergency or fault conditions. By integrating SiC-based power converters, the proposed system achieves high efficiency, low switching losses, and enhanced thermal performance, which are crucial for the space- and weight-constrained environment of UAM platforms. Furthermore, a robust control strategy is implemented to enable smooth transitions between multiple power sources, maintaining operational stability and safety. System-level simulations were conducted using PowerSIM to validate the performance and reliability of the proposed architecture. The results demonstrate its effectiveness, making it a strong candidate for future UAM power systems requiring lightweight, efficient, and fault-tolerant power solutions. Full article
(This article belongs to the Special Issue Electric Vehicles: Latest Advances and Prospects for Sustainable Energy Systems and Sustainable Mobility)
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17 pages, 3490 KB  
Article
Electric Vehicle Power Consumption Modelling Method Based on Improved Ant Colony Optimization-Support Vector Regression
by Jiaan Zhang, Wenxin Liu, Zhenzhen Wang and Ruiqing Fan
Energies 2024, 17(17), 4339; https://doi.org/10.3390/en17174339 - 30 Aug 2024
Cited by 6 | Viewed by 1875
Abstract
Accurate forecasting of electric vehicle (EV) power consumption per unit mileage serves as the cornerstone for determining diurnal variations in EV charging loads. To enhance the prediction accuracy of EV power consumption per unit mileage, this paper proposes a modelling method grounded in [...] Read more.
Accurate forecasting of electric vehicle (EV) power consumption per unit mileage serves as the cornerstone for determining diurnal variations in EV charging loads. To enhance the prediction accuracy of EV power consumption per unit mileage, this paper proposes a modelling method grounded in an improved Ant Colony Optimization-Support Vector Regression (ACO-SVR) framework. This method integrates the effects of both temperature and speed on the power consumption per unit mileage of EVs. Initially, we analyze the influence mechanism of driving speed and ambient temperature on EV power consumption, elucidating the relationship between power consumption per unit mileage and these factors. Subsequently, we construct an ACO-SVR model utilizing an improved ant colony optimization algorithm, fitting the relationship between power consumption, speed, and temperature to derive the EV power consumption per unit mileage model. Finally, leveraging operational data from EVs in Guangdong, Hong Kong, and Macao as a case study, we validate the energy consumption model of EVs by considering factors such as ambient temperature and driving speed. The results demonstrate that the model proposed in this paper is both accurate and effective. Full article
(This article belongs to the Special Issue Electric Vehicles: Latest Advances and Prospects for Sustainable Energy Systems and Sustainable Mobility)
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22 pages, 1939 KB  
Article
Comparative Life Cycle Assessment of Electric and Internal Combustion Engine Vehicles
by Andrey Kurkin, Evgeny Kryukov, Olga Masleeva, Yaroslav Petukhov and Daniil Gusev
Energies 2024, 17(11), 2747; https://doi.org/10.3390/en17112747 - 4 Jun 2024
Cited by 12 | Viewed by 16821
Abstract
This article is devoted to the ecological comparison of electric and internal combustion engine vehicles throughout their entire life cycle, from mining to recycling. A scientifically based approach to a comprehensive environmental assessment of the impact of vehicles on the environment has been [...] Read more.
This article is devoted to the ecological comparison of electric and internal combustion engine vehicles throughout their entire life cycle, from mining to recycling. A scientifically based approach to a comprehensive environmental assessment of the impact of vehicles on the environment has been developed. To analyze the impact on the environmental situation, aspects such as the consumption of natural resources, waste generation, electricity consumption, emission of harmful substances into the atmosphere, water consumption, and greenhouse gas emissions are taken into consideration. As a result of comparing the environmental impacts of vehicles, it was found that natural resources consumption and production of industrial waste from electric vehicles (EV) is 6 times higher than from internal combustion engine vehicles (ICEV). Harmful substance emissions and greenhouse gas emissions from EV production are 1.65 and 1.5 times higher, respectively. The EV total electricity consumption is 1.4 times higher than that of ICEVs. At the same time, it was revealed that during operation, EVs have higher energy consumption and emit more harmful substances into the atmosphere, but EVs produce less greenhouse gas emissions. It means that at different life cycle stages, EVs have a much higher negative impact on the environment compared to gasoline engine vehicles. Full article
(This article belongs to the Special Issue Electric Vehicles: Latest Advances and Prospects for Sustainable Energy Systems and Sustainable Mobility)
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Review

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53 pages, 3028 KB  
Review
Optimization and Machine Learning for Electric Vehicles Management in Distribution Networks: A Review
by Stefania Conti, Giovanni Aiello, Salvatore Coco, Antonino Laudani, Santi Agatino Rizzo, Nunzio Salerno, Giuseppe Marco Tina and Cristina Ventura
Energies 2026, 19(4), 986; https://doi.org/10.3390/en19040986 - 13 Feb 2026
Viewed by 1612
Abstract
The growing penetration of Electric Vehicles (EVs) in power distribution networks presents both challenges and opportunities for grid operators and planners. This paper provides a comprehensive review of recent advances in optimization techniques and machine learning (ML) approaches for the efficient management of [...] Read more.
The growing penetration of Electric Vehicles (EVs) in power distribution networks presents both challenges and opportunities for grid operators and planners. This paper provides a comprehensive review of recent advances in optimization techniques and machine learning (ML) approaches for the efficient management of EV charging and integration in low- and medium-voltage distribution systems. Optimization methods are analyzed with reference to their objectives—such as load flattening, voltage regulation, loss minimization, and infrastructure cost reduction—and their capability to handle multi-objective, stochastic, and real-time constraints. Concurrently, the role of ML is explored in load forecasting, user behavior modeling, anomaly detection, and adaptive control strategies. Particular attention is given to hybrid approaches that combine optimization algorithms (e.g., MILP, heuristic methods) with data-driven models (e.g., neural networks, reinforcement learning), highlighting their effectiveness in enhancing grid flexibility and resilience. This review adopts a unified system-level perspective that links EV management objectives, optimization techniques, and machine learning-based solutions within distribution networks. In addition, particular attention is devoted to data availability, reproducibility, and practical deployment aspects, with the aim of identifying current limitations and providing actionable insights for future research and real-world applications. This study aims to support the development of intelligent energy management strategies for EVs, fostering a sustainable and reliable evolution of distribution networks. Full article
(This article belongs to the Special Issue Electric Vehicles: Latest Advances and Prospects for Sustainable Energy Systems and Sustainable Mobility)
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