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Challenges and Future Trends of Energy Management Systems for Electric Vehicles

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

Deadline for manuscript submissions: 25 August 2025 | Viewed by 3032

Special Issue Editor


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Guest Editor
UCL Biochemical Engineering, University College London, London WC1E 6BT, UK
Interests: electric vehicle platoon system; data processing; AI; biomanufacturing

Special Issue Information

Dear Colleagues,

The rapid advancement of electric vehicles (EVs) is reshaping the automotive industry, accelerating the transition to sustainable transportation and reducing our reliance on fossil fuels. Central to this evolution is the Energy Management System (EMS), a crucial component that optimizes the use of energy within the vehicle, ensuring the efficiency, reliability, and longevity of the energy storage and powertrain components.

An effective EMS is essential for managing the complex interactions between the battery, electric motor, and other vehicle systems. It balances energy supply and demand, manages thermal conditions, and extends the overall range and lifespan of the vehicle. However, as the demand for longer driving ranges, faster charging times, and higher safety standards grows, EMS technology faces new challenges and must evolve to meet these demands.

This Special Issue will present and disseminate the latest advancements in the theory, design, modeling, application, and control of Energy Management Systems for electric vehicles. We invite researchers and practitioners to share their most recent findings, addressing both the current challenges and future trends that will shape the development of EMS technology.

Topics of interest for publication include, but are not limited to, the following:

  • Advances in energy optimization algorithms for EVs;
  • Integration of renewable energy sources with EMS in EVs;
  • Thermal management strategies in EMSs;
  • Real-time energy management and control systems;
  • EMS for hybrid electric vehicles (HEVs) and plug-in hybrid electric vehicles (PHEVs);
  • Predictive energy management techniques using AI and machine learning;
  • Impact of new battery chemistries on EMS design and operation;
  • Energy management in Vehicle-to-Grid (V2G) and Vehicle-to-Everything (V2X) systems;
  • EMS cybersecurity and data management;
  • EMS standardization and regulatory challenges;
  • Hardware and software co-design for EMS optimization;
  • Safety and reliability assessments of EMS in EVs.

Dr. Handong Li
Guest Editor

Manuscript Submission Information

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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

  • energy management systems (EMSs)
  • electric vehicles (EVs)
  • energy optimization
  • thermal management
  • real-time control
  • predictive energy management
  • EMS cybersecurity
  • V2G and V2X systems
  • safety and reliability
  • hybrid and plug-in hybrid vehicles

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Published Papers (3 papers)

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Research

26 pages, 6981 KiB  
Article
A Hybrid Blockchain Solution for Electric Vehicle Energy Trading: Balancing Proof of Work and Proof of Stake
by Sid-Ali Amamra
Energies 2025, 18(7), 1840; https://doi.org/10.3390/en18071840 - 5 Apr 2025
Viewed by 304
Abstract
This research presents an innovative blockchain-based solution for the charging and energy trading of electric vehicles (EVs). By combining the strengths of two prominent consensus mechanisms, Proof of Work (PoW) and Proof of Stake (PoS), the proposed system balances security, decentralization, and energy [...] Read more.
This research presents an innovative blockchain-based solution for the charging and energy trading of electric vehicles (EVs). By combining the strengths of two prominent consensus mechanisms, Proof of Work (PoW) and Proof of Stake (PoS), the proposed system balances security, decentralization, and energy efficiency. PoW secures the blockchain, while PoS enhances energy efficiency and scalability, key factors in meeting the growing demand for EV infrastructure. The system’s decentralized nature allows for EV owners, charging stations, and stakeholders to interact and transact transparently, without relying on centralized entities. The research conducts a comprehensive simulation to assess the performance of the proposed hybrid blockchain model, demonstrating significant improvements in cost-effectiveness, scalability, and energy management. Additionally, dynamic pricing mechanisms within the blockchain enable real-time energy trading, optimizing charging times and balancing grid demand efficiently. Through the use of smart contracts, automated pricing adjustments, and incentive-driven user behaviors, the proposed system paves the way for more sustainable, cost-effective, and efficient energy solutions in the future. Full article
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19 pages, 2779 KiB  
Article
Risk Preferences of EV Fleet Aggregators in Day-Ahead Market Bidding: Mean-CVaR Linear Programming Model
by Izabela Zoltowska
Energies 2025, 18(1), 93; https://doi.org/10.3390/en18010093 - 29 Dec 2024
Viewed by 640
Abstract
This paper introduces a mean profit- conditional value-at-risk (CVaR) model for purchasing electricity on the day-ahead market (DA) by electric vehicles fleet aggregator (EVA). EVA controls electric vehicles (EVs) during their workplace parking, enabling smart charging and cost savings by accessing market prices [...] Read more.
This paper introduces a mean profit- conditional value-at-risk (CVaR) model for purchasing electricity on the day-ahead market (DA) by electric vehicles fleet aggregator (EVA). EVA controls electric vehicles (EVs) during their workplace parking, enabling smart charging and cost savings by accessing market prices that are potentially lower than flat rates available during home charging. The proposed stochastic linear programming model leverages market price scenarios to optimize aggregated charging schedules, which serve as templates for constructing effective DA bidding curves. It integrates an aspiration/reservation-based formulation of the mean profit-risk criteria, specifically Conditional Value at Risk (CVaR) to address the EVA’s risk aversion. By incorporating interactive analysis, the framework ensures adaptive and robust charging schedules and bids tailored to the aggregator’s risk preferences. Its ability to balance profitability with risk is validated in case studies. This approach provides a practical and computationally efficient tool for EV aggregators of global companies that can benefit from the workplace charging their fleets thanks to buying energy in the DA market. Full article
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21 pages, 3072 KiB  
Article
Reinforcement Learning for EV Fleet Smart Charging with On-Site Renewable Energy Sources
by Handong Li, Xuewu Dai, Stephen Goldrick, Richard Kotter, Nauman Aslam and Saleh Ali
Energies 2024, 17(21), 5442; https://doi.org/10.3390/en17215442 - 31 Oct 2024
Cited by 2 | Viewed by 1484
Abstract
In 2020, the transportation sector was the second largest source of carbon emissions in the UK and in Newcastle upon Tyne, responsible for about 33% of total emissions. To support the UK’s target of reaching net zero emissions by 2050, electric vehicles (EVs) [...] Read more.
In 2020, the transportation sector was the second largest source of carbon emissions in the UK and in Newcastle upon Tyne, responsible for about 33% of total emissions. To support the UK’s target of reaching net zero emissions by 2050, electric vehicles (EVs) are pivotal in advancing carbon-neutral road transportation. Optimal EV charging requires a better understanding of the unpredictable output from on-site renewable energy sources (ORES). This paper proposes an integrated EV fleet charging schedule with a proximal policy optimization method based on a framework for deep reinforcement learning. For the design of the reinforcement learning environment, mathematical models of wind and solar power generation are created. In addition, the multivariate Gaussian distributions derived from historical weather and EV fleet charging data are utilized to simulate weather and charging demand uncertainty in order to create large datasets for training the model. The optimization problem is expressed as a Markov decision process (MDP) with operational constraints. For training artificial neural networks (ANNs) through successive transition simulations, a proximal policy optimization (PPO) approach is devised. The optimization approach is deployed and evaluated on a real-world scenario comprised of council EV fleet charging data from Leicester, UK. The results show that due to the design of the rewards function and system limitations, the charging action is biased towards the time of day when renewable energy output is maximum (midday). The charging decision by reinforcement learning improves the utilization of renewable energy by 2–4% compared to the random charging policy and the priority charging policy. This study contributes to the reduction in battery charging and discharging, electricity sold to the grid to create benefits and the reduction in carbon emissions. Full article
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