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Energies
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Energy Management and Control System of Electric Vehicles
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Energy Management and Control System of Electric Vehicles

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

Deadline for manuscript submissions: closed (20 April 2026) | Viewed by 5533

Special Issue Editors

Dr. Daniel Chindamo

E-Mail Website
Guest Editor
Department of Mechanical and Industrial Engineering, University of Brescia, I-25123 Brescia, Italy
Interests: vehicle dynamics; HEVs
Special Issues, Collections and Topics in MDPI journals
Dr. Giulia Sandrini

E-Mail Website
Guest Editor
Department of Mechanical and Industrial Engineering, University of Brescia, I-25123 Brescia, Italy
Interests: automotive engineering; electric vehicles; energy consumption; energy management

Special Issue Information

Dear Colleagues,

This Special Issue, entitled "Energy Management and Control System of Electric Vehicles", delves into the vital innovations and technological advancements required to enhance the performance, efficiency, and sustainability of electric vehicles (EVs). As global concerns over fossil fuel dependence and carbon emissions continue to escalate, the adoption of EVs has emerged as a key component in the pursuit of cleaner, greener transportation. However, as EVs become increasingly popular, their energy management and control systems face unprecedented challenges, especially in achieving optimal energy utilization, extending battery life, and enhancing overall driving performance.

The core focus of this Special Issue is on advanced methodologies for managing energy consumption in EVs, including battery management systems (BMS), regenerative braking strategies, thermal management, and real-time energy distribution controls. Battery management, in particular, plays a pivotal role, as it is responsible for monitoring battery health, preventing overcharging or overheating, and maximizing lifespan. Furthermore, strategies like regenerative braking help to recuperate energy typically lost in braking, effectively increasing driving range and enhancing efficiency.

Another critical aspect covered is the role of artificial intelligence (AI) and machine learning (ML) in EV energy management systems. These technologies allow for the development of predictive models that optimize energy use by forecasting driving patterns, battery status, and environmental conditions. Control algorithms, powered by AI, can adapt to different driving modes, manage power distribution between the motor and other auxiliary systems, and improve EV range and efficiency.

This Special Issue also examines the integration of renewable energy sources, such as solar and wind, with EV charging stations, aiming to reduce reliance on the traditional power grid. Vehicle-to-grid (V2G) technology is explored as a means to turn EVs into mobile energy storage units, providing power back to the grid during peak demand periods, which has the potential to stabilize grid loads and contribute to energy resilience.

As a whole, this Special Issue presents an in-depth view of the technologies and strategies that will be used to shape the future of EV energy management and control systems. With advancements in energy management algorithms, AI-driven control systems, and renewable energy integration, this Special Issue seeks to propel the field towards more robust, efficient, and sustainable EV solutions that will meet the demands of both modern drivers and an evolving energy landscape.

Dr. Daniel Chindamo
Dr. Giulia Sandrini
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 vehicle energy management
  • battery management
  • regenerative braking
  • vehicle-to-grid (V2G) integration

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Published Papers (5 papers)

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Research

28 pages, 13090 KB  
Article
Energy-Economic-Environmental (3E) Optimisation of Grid-Connected Electric Vehicle Charging Station for a University Campus in Caparica, Portugal
by S. M. Masum Ahmed, Annamaria Bagaini, João Martins, Edoardo Croci and Enrique Romero-Cadaval
Energies 2026, 19(6), 1466; https://doi.org/10.3390/en19061466 - 14 Mar 2026
Viewed by 900
Abstract
Approximately one quarter of the European Union’s (EU’s) CO2 emissions originate from the transport sector, of which road transport, such as cars and heavy-duty vehicles, contributes roughly 72%. Moreover, according to the European Automobile Manufacturers’ Association, 92% of cars in the EU [...] Read more.
Approximately one quarter of the European Union’s (EU’s) CO2 emissions originate from the transport sector, of which road transport, such as cars and heavy-duty vehicles, contributes roughly 72%. Moreover, according to the European Automobile Manufacturers’ Association, 92% of cars in the EU are internal combustion engine vehicles powered by fossil fuels. Therefore, boosting the adoption of Electric Vehicles (EVs) is considered one of the most prominent solutions for reducing GHG emissions and achieving the EU’s climate targets. To increase EV adoption and fulfil the demand of EV users, adequate EV Charging Stations (EVCSs) are required. Nevertheless, since most EVCSs are supplied by electricity grids that remain predominantly fossil fuel-based, their operation entails substantial indirect GHG emissions. A prominent approach to reducing grid-related emissions is integrating renewable energy sources (RESs) with EVCSs, thereby lowering emissions and alleviating grid stress. Although promising, the energy, economic, and environmental (3E) benefits of this integration remain insufficiently explored. Therefore, this study develops and applies a 3E optimisation framework to assess the feasibility and performance of RES-powered EVCS at NOVA University Lisbon (UNL). Data was collected from the UNL parking area, such as time of arrival, and time of departure. Also, a rule-based algorithm was developed to curate data and estimate the EVCS load profile. Furthermore, HOMER optimisation software was employed to evaluate four scenarios, including (i) an EVCS based on PV, Wind Turbine (WT), and the grid, (ii) an EVCS based on PV and the grid, (iii) an EVCS based on WT and the grid, and (iv) an EVCS based only on energy withdrawal from the grid (base scenario). Under the adopted techno-economic assumptions, in the most optimised scenario, economic and environmental analyses illustrate significant improvements over the base scenario: CO2 emissions are five times lower, and cost of energy is significantly lower, resulting in significantly lower EV charging costs for users. The results demonstrate that, through developed feasibility studies, researchers, decision-makers, and stakeholders can reach better conclusions about EVCS planning and management. Full article
(This article belongs to the Special Issue Energy Management and Control System of Electric Vehicles)
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31 pages, 5337 KB  
Article
Energy Management in Multi-Source Electric Vehicles Through Multi-Objective Whale Particle Swarm Optimization Considering Aging Effects
by Nikolaos Fesakis, Christos Megagiannis, Georgia Eirini Lazaridou, Efstratia Sarafoglou, Aristotelis Tzouvaras and Athanasios Karlis
Energies 2026, 19(1), 154; https://doi.org/10.3390/en19010154 - 27 Dec 2025
Cited by 1 | Viewed by 902
Abstract
As the adoption of electric vehicles increases, hybrid energy storage systems (HESS) combining batteries and supercapacitors mitigate the conflict between high energy capacity and power demand, particularly during acceleration and transient loads. However, frequent current fluctuations accelerate battery degradation, reducing long-term performance. This [...] Read more.
As the adoption of electric vehicles increases, hybrid energy storage systems (HESS) combining batteries and supercapacitors mitigate the conflict between high energy capacity and power demand, particularly during acceleration and transient loads. However, frequent current fluctuations accelerate battery degradation, reducing long-term performance. This study presents a multi-objective Whale–Particle Swarm Optimization Algorithm (MOWPSO) for tuning the control parameters of a HESS composed of a lithium-ion battery and a supercapacitor. The proposed full-active configuration with dual bidirectional DC converters enables precise current sharing and independent regulation of energy and power flow. The optimization framework minimizes four objectives: mean battery current amplitude, cumulative aging index, final state-of-charge deviation, and an auxiliary penalty term promoting consistent battery–supercapacitor cooperation. The algorithm operates offline to identify Pareto-optimal controller settings under the Federal Test Procedure 75 cycle, while the selected compromise solution governs real-time current distribution. Robustness is assessed through multi-seed hypervolume analysis, and results demonstrate over 20% reduction in battery aging and approximately 25% increase in effective cycle life compared to battery-only, rule-based and metaheuristic algorithm strategies control. Cross-cycle validation under highway and worldwide driving profiles confirms the controller’s adaptability and stable current-sharing performance without re-tuning. Full article
(This article belongs to the Special Issue Energy Management and Control System of Electric Vehicles)
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21 pages, 7540 KB  
Article
MILP-Based Optimization of Electric Bus Charging Considering Battery Degradation and Environmental Factors Under TOU Pricing
by Ye-Bin Seo, Sung-Won Park and Sung-Yong Son
Energies 2025, 18(22), 6028; https://doi.org/10.3390/en18226028 - 18 Nov 2025
Viewed by 806
Abstract
The transition from conventional fossil-fueled buses to electric buses (EBs) is accelerating in the global public transportation sector. However, owing to the limitations of battery lifespan and capacity, EBs have a shorter driving range than conventional buses, and their power consumption is highly [...] Read more.
The transition from conventional fossil-fueled buses to electric buses (EBs) is accelerating in the global public transportation sector. However, owing to the limitations of battery lifespan and capacity, EBs have a shorter driving range than conventional buses, and their power consumption is highly variable depending on the ambient temperature. In addition, battery lifespans are affected by charging and discharging cycles and battery age over time in all situations, which requires a method of operation that considers these factors. In this study, we estimated the driving, heating, and cooling energy consumptions based on the dispatch schedule and actual power consumption of EBs. The estimated energy consumption was then used as an input to plan the amount of charging power by time of day to optimize the charging and battery degradation costs. The optimization methodology employed mixed-integer linear programming (MILP), which facilitates discrete charging decision-making and ensures an optimum solution for operation costs by taking cost factors into account. In this phase, the scenarios were configured according to the time-of-use (TOU) charging cost and whether or not battery degradation. Battery degradation can be divided into cycle and calendar aging. The scenarios that considered both TOU and battery degradation reduced the average operating costs by approximately 1.43, 12.3, and 5.69% in spring/fall, summer, and winter, respectively, compared with scenarios that did not consider either. Full article
(This article belongs to the Special Issue Energy Management and Control System of Electric Vehicles)
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21 pages, 10106 KB  
Article
Distributed Hierarchical Control with Cost Optimization and Priority-Based Dispatch for Workplace EV Charging: A Field Study
by Anna Malkova, Simone Striani, Jan Martin Zepter and Mattia Marinelli
Energies 2025, 18(21), 5581; https://doi.org/10.3390/en18215581 - 23 Oct 2025
Cited by 2 | Viewed by 980
Abstract
Electric vehicle (EV) charging presents both a challenge and an opportunity for modern power systems, particularly in workplace environments with grid constraints and dynamic energy pricing. This study presents a real-life implementation and experimental validation of a hierarchical distributed control system for smart [...] Read more.
Electric vehicle (EV) charging presents both a challenge and an opportunity for modern power systems, particularly in workplace environments with grid constraints and dynamic energy pricing. This study presents a real-life implementation and experimental validation of a hierarchical distributed control system for smart EV charging. The proposed architecture combines upper-level receding horizon optimization with lower-level priority-based dispatch, enabling cost-efficient energy allocation and fair distribution among EVs. The system was deployed at the Risø campus of the Technical University of Denmark (DTU) and tested over two days under realistic operational conditions, including heterogeneous EV behavior and limited grid capacity. The control system demonstrated autonomous operation, responsiveness to price signals, and effective coordination between control layers. High energy delivery rates were achieved, nearly 100% on the first test day and close to 90% on the second, despite operating under a constrained energy budget. The study also documents practical challenges encountered during deployment, such as charger communication faults and EV-side issues, and proposes adaptation strategies. These results confirm the feasibility of distributed smart charging in real-world conditions and provide actionable insights for future implementations. Full article
(This article belongs to the Special Issue Energy Management and Control System of Electric Vehicles)
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16 pages, 941 KB  
Article
Multidimensional Comparison of Electric and Combustion Vehicles: A Clustering Based Analysis from the Polish Market
by Jakub Kubiczek and Julianna Koczy
Energies 2025, 18(21), 5554; https://doi.org/10.3390/en18215554 - 22 Oct 2025
Viewed by 1011
Abstract
Electrification of transport is advancing, yet debate continues over whether battery electric vehicles (EVs) are a like-for-like and affordable alternative to internal-combustion engine (ICE) cars. Positioned in a rapidly evolving mainstream market, this study examines structural similarity and relative pricing of EVs versus [...] Read more.
Electrification of transport is advancing, yet debate continues over whether battery electric vehicles (EVs) are a like-for-like and affordable alternative to internal-combustion engine (ICE) cars. Positioned in a rapidly evolving mainstream market, this study examines structural similarity and relative pricing of EVs versus ICE models available in Poland in 2025. Data on 373 base passenger-car models (excluding hybrids) were analyzed using two clustering methods: k-means and k-medoids. The optimal number of clusters was determined by 23 validity indices, identifying three clusters. The significance of mean price differences between EVs and non-EVs within the specified clusters was tested using a permutation test. Results indicate no statistically meaningful EV price premium within clusters: no EV price exceeded two standard deviations above its cluster mean, and no cluster consisted exclusively of EVs, which points to strong technical similarity across powertrains. Additionally, permutation tests indicated no differences within clusters, except in the cluster with the best technical parameters, where non-EV cars were more expensive, which suggests that the premium segment of the market continues to be dominated by combustion cars. These findings, which show that electric vehicles are price-comparable to non-EVs, challenge the perception that EVs are systematically more expensive and demonstrate that, within market segments defined by technical characteristics. Therefore, the evidence suggests that EVs are becoming a genuine competitive alternative to ICE cars in the Polish market. Full article
(This article belongs to the Special Issue Energy Management and Control System of Electric Vehicles)
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