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Advances in Charging Systems and Charging Management Strategies for Battery Electric Vehicles

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A special issue of Batteries (ISSN 2313-0105). This special issue belongs to the section "Battery Modelling, Simulation, Management and Application".

Deadline for manuscript submissions: 31 October 2026 | Viewed by 44359

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Special Issue Editors

Prof. Dr. Omar Hegazy

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Guest Editor

EPOWERS Research Group, MOBI Research Centre for Electromobility, Vrije Universiteit Brussel, Brussels, Belgium
Interests: power electronics and electric powertrain design; energy and power management strategies for Evs; smart charging infrastructure and V2X technologies; reliability, lifetime prediction, digital twin (DT) of EV components; battery management systems (BMS) and next-gen battery integration
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Dr. Steven Wilkins

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Guest Editor

1. Senor Research Scientist, Powertrains Department, TNO, Automotive Campus 30, NL-5708 JZ Helmond, The Netherlands
2. Assistant Professor, EPE Group, Technische Universiteit Eindhoven (TU/e), Postbus 513, 5600 MB Eindhoven, The Netherlands
Interests: modelling and simulation of electrified powertains; energy management strategies; energy storage systems; battery management systems; state estimation
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Elecrification in cities has gained a growing interest in the transport industry towards more electrified and energy-efficient drivetrains to improve air-quality. Thus, charging systems and infrastructure and their charging management strategies have a significant impact on battery systems in terms of aging, sizing and thermal management, and on the drivetrain in terms of efficiency, total cost of ownership (TCO) and power electronics interfaces (i.e., on/off chargers, DC/DC converters, etc.). Furthermore, the charging systems and their control systems should be optimally designed and controlled; taking into account their impact on the grid in terms of stability, power quality, etc.

Therefore, this Special Issue is focused on recent advances in charging systems and charging management strategies that address the above-mentioned aspects and go beyond the state-of-the-art.

Prospective authors are invited to submit original contributions/articles for review and for possible publication in this Special Issue. Topics of interest include (but are not limited to):

On/Off Charging Systems;
Charging Management Strategies;
Energy Management Strategies;
New Architecture for EV and PHEV for both light-duty and heavy-duty vehicles;
Energy Storage systems for fast charging capabilities;
Battery Aging and State of Heath estimation (SoH);
Vehicle-to- grid (V2G), vehicle-to-home (V2H), Grid –to-vehicle (G2V) technologies;
Charging systems and grid quality

Prof. Dr. Omar Hegazy
Prof. Dr. Steven Wilkins
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. Batteries is an international peer-reviewed open access monthly 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 2700 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

Battery Electric Vehicles (passenger cars, electric buses, electric vans, etc.)
Charging systems
Energy management strategies
Power electronics converters
V2G and G2V
Battery Aging
Battery Thermal management strategies
State of health esitmations

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Published Papers (7 papers)

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Research

Jump to: Review

23 pages, 2752 KB

Open AccessArticle

Deep Neural Network Optimization for Lithium-Ion Battery State of Health Prediction in Electric Vehicles: Outperforming Hybrid Models

by Saad El Fallah, Jaouad Kharbach, Jonas Vanagas, Ahmed Lakhssassi, Hassan Qjidaa and Mohammed Ouazzani Jamil

Batteries 2026, 12(2), 52; https://doi.org/10.3390/batteries12020052 - 4 Feb 2026

Viewed by 424

Abstract

It is now crucial to accurately monitor the state of health (SoH) of batteries in a setting where the use of electric vehicles (EVs) and renewable energy technologies is still growing. To solve this issue and evaluate the SoH, this paper makes use of deep learning technology. The suggested method incorporates voltage, current, and temperature data, which are important indications of the SoH and can potentially be obtained directly from the battery management system (BMS). Although deep neural networks (DNNs) have previously been employed for SoH estimation, our study distinguishes itself by implementing a robust, completely configurable DNN application in MATLAB/Simulink R2019a. This design enables the adjustment of activation functions, layer depth, and neuron count to adapt to different battery aging conditions. To achieve optimal performance, numerous configurations were examined, highlighting the relevance of hyperparameter setting. Our technique avoids traditional feature engineering while providing a practical, adaptive, and accurate SoH estimate framework appropriate for real-world integration. The precision of the improved model was then verified against a Li-ion battery dataset with various discharge profiles given by the national aeronautics and space administration (NASA). The collected findings revealed that the proposed method is more accurate and robust than other regularly used models. The DNN model achieved a Mean absolute error (MAE) of 1.433% and a Coefficient of determination of 0.99998, outperforming previous methods such as CNN-BiGRU, which reported an MAE of 2.448% in a recent publication. This study demonstrates the reliable performance of the DNN in predicting the SoH of Li-ion cells. Full article

(This article belongs to the Special Issue Advances in Charging Systems and Charging Management Strategies for Battery Electric Vehicles)

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26 pages, 1730 KB

Open AccessArticle

Two-Stage Game-Based Charging Optimization for a Competitive EV Charging Station Considering Uncertain Distributed Generation and Charging Behavior

by Shaohua Han, Hongji Zhu, Jinian Pang, Xuan Ge, Fuju Zhou and Min Wang

Batteries 2026, 12(1), 16; https://doi.org/10.3390/batteries12010016 - 1 Jan 2026

Viewed by 521

Abstract

The widespread adoption of electric vehicles (EVs) has turned charging demand into a substantial load on the power grid. To satisfy the rapidly growing demand of EVs, the construction of charging infrastructure has received sustained attention in recent years. As charging stations become more widespread, how to attract EV users in a competitive charging market while optimizing the internal charging process is the key to determine the charging station’s operational efficiency. This paper tackles this issue by presenting the following contributions. Firstly, a simulation method based on prospect theory is proposed to simulate EV users’ preferences in selecting charging stations. The selection behavior of EV users is simulated by establishing coupling relationship among the transportation network, power grid, and charging network as well as the model of users’ preference. Secondly, a two-stage joint stochastic optimization model for a charging station is developed, which considers both charging pricing and energy control. At the first stage, a Stackelberg game is employed to determine the day-ahead optimal charging price in a competitive market. At the second stage, real-time stochastic charging control is applied to maximize the operational profit of the charging station considering renewable energy integration. Finally, a scenario-based Alternating Direction Method of Multipliers (ADMM) approach is introduced in the first stage for optimal pricing learning, while a simulation-based Rollout method is applied in the second stage to update the real-time energy control strategy based on the latest pricing. Numerical results demonstrate that the proposed method can achieve as large as 33% profit improvement by comparing with the competitive charging stations considering 1000 EV integration. Full article

(This article belongs to the Special Issue Advances in Charging Systems and Charging Management Strategies for Battery Electric Vehicles)

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19 pages, 4836 KB

Open AccessArticle

Experimental Study of Pouch-Type Battery Cell Thermal Characteristics Operated at High C-Rates

by Marius Vasylius, Deivydas Šapalas, Benas Dumbrauskas, Valentinas Kartašovas, Audrius Senulis, Artūras Tadžijevas, Pranas Mažeika, Rimantas Didžiokas, Ernestas Šimkutis and Lukas Januta

Batteries 2026, 12(1), 14; https://doi.org/10.3390/batteries12010014 - 28 Dec 2025

Viewed by 707

Abstract

This paper investigates pouch-type lithium-ion battery cells with a nominal voltage of 3.7 V and a nominal capacity of 57 Ah. A numerical model of the cell was developed and implemented using the NTGK method, which accurately predicts electrochemical and thermal processes. The results of numerical modeling matched with the experimental results of battery cell temperature measurements—the average deviation was about 4.5%; therefore, it can be considered reliable for further engineering research and construction of battery modules. In the experimental part of the paper, the battery cell was loaded in various C-rates (from 0.5 to 2 C), using heat flux sensors, thermocouples, and a thermal imaging camera. The studies revealed that the highest temperature is in the tabs area of cells. The temperature on the face of the cell surface exceeds 35 °C already from a load of 1.35 C, which accelerates cell degradation and reduces the number of cycles. Thermal imaging revealed uneven temperature distribution, whereby the top of the cell heats up more than the bottom of the cell and the temperature gradient can reach 2–4 °C. It was observed that during faster charge/discharge modes, the temperature rises from the tabs of the cell, and during slower ones, more in the middle face surface of the cell. The studies highlight the need to apply additional cooling solutions, especially cooling of the upper cell face, to ensure durability and uniform heat distribution. Full article

(This article belongs to the Special Issue Advances in Charging Systems and Charging Management Strategies for Battery Electric Vehicles)

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21 pages, 2857 KB

Open AccessArticle

Distributed Energy Storage Configuration Method for AC/DC Hybrid Distribution Network Based on Bi-Level Optimization

by Jianjun Zhao, Jianqi Wang, Mengke Gao, Yinfeng Sun, Yang Li, Zhenhao Wang and Xu Zhao

Batteries 2026, 12(1), 9; https://doi.org/10.3390/batteries12010009 - 26 Dec 2025

Viewed by 346

Abstract

Aiming at prominent voltage quality problems in AC/DC hybrid distribution networks with a high proportion of distributed energy and diversified loads, this paper proposes a bi-level energy storage system (ESS) optimization model. The upper level optimizes the ESS configuration with the goal of minimizing the cost, and the lower level optimizes the real-time running state of the ESS. Considering multiple constraints, the improved PSO algorithm and the Gurobi solver are used to solve the problem. The test on the modified IEEE-33 node system verified that the model effectively improved voltage quality and reduced power system costs, which provides theoretical and engineering support for the scientific configuration of the ESS. Full article

(This article belongs to the Special Issue Advances in Charging Systems and Charging Management Strategies for Battery Electric Vehicles)

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19 pages, 2601 KB

Open AccessArticle

Charge Scheduling of Electric Vehicle Fleets: Maximizing Battery Remaining Useful Life Using Machine Learning Models

by David Geerts, Róbinson Medina, Wilfried van Sark and Steven Wilkins

Batteries 2024, 10(2), 60; https://doi.org/10.3390/batteries10020060 - 15 Feb 2024

Cited by 9 | Viewed by 4000

Abstract

Reducing greenhouse emissions can be done via the electrification of the transport industry. However, there are challenges related to the electrification such as the lifetime of vehicle batteries as well as limitations on the charging possibilities. To cope with some of these challenges, a charge scheduling method for fleets of electric vehicles is presented. Such a method assigns the charging moments (i.e., schedules) of fleets that have more vehicles than chargers. While doing the assignation, the method also maximizes the total Remaining Useful Life (RUL) of all the vehicle batteries. The method consists of two optimization algorithms. The first optimization algorithm determines charging profiles (i.e., charging current vs time) for individual vehicles. The second algorithm finds the charging schedule (i.e., the order in which vehicles are connected to a charger) that maximizes the RUL in the batteries of the entire fleet. To reduce the computational effort of predicting the battery RUL, the method uses a Machine Learning (ML) model. Such a model predicts the RUL of an individual battery while taking into account common stress factors and fabrication-related differences per battery. Simulation results show that charging a single vehicle as late as possible maximizes the RUL of that single vehicle, due to the lower battery degradation. Simulations also show that the ML model accurately predicts the RUL, while taking into account fabrication-related variability in the battery. Additionally, it was shown that this method schedules the charging moments of a fleet, leading to an increased total RUL of all the batteries in the vehicle fleet. Full article

(This article belongs to the Special Issue Advances in Charging Systems and Charging Management Strategies for Battery Electric Vehicles)

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Review

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35 pages, 2556 KB

Open AccessReview

Review of Active Front-End Rectifiers in EV DC Charging Applications

by Assel Zhaksylyk, Haaris Rasool, Ekaterina Abramushkina, Sajib Chakraborty, Thomas Geury, Mohamed El Baghdadi and Omar Hegazy

Batteries 2023, 9(3), 150; https://doi.org/10.3390/batteries9030150 - 27 Feb 2023

Cited by 26 | Viewed by 15303

Abstract

Active Front-End (AFE) rectifiers have regained momentum as the demand for highpower Electric Vehicle (EV) charging infrastructure increases exponentially. AFE rectifiers have high efficiency and reliability, and they minimize the disturbances that could be generated due to the operation of the EV charging systems by reducing harmonic distortion and operating close to the Unity Power Factor (UPF). The purpose of this review is to present the current state-of-the-art AFE rectifiers used in fast chargers, focusing on the comparison between different AFE topologies and their components, as well as modular AFE solutions. Furthermore, different control strategies of AFE converters are presented and compared. Some of their more widely used control techniques, namely Voltage Oriented Control (VOC), Direct Power Control (DPC), Hysteresis Current Control (HCC), and Model Predictive Control (MPC), have been implemented, and their performance compared. Centralized and distributed control systems are compared for operating parallel AFE rectifiers for modular, fast charging systems. An overview of cooling systems and reliability evaluation tools is also presented. Finally, trends and future outlooks are analyzed. Full article

(This article belongs to the Special Issue Advances in Charging Systems and Charging Management Strategies for Battery Electric Vehicles)

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36 pages, 4388 KB

Open AccessReview

A Review of DC Fast Chargers with BESS for Electric Vehicles: Topology, Battery, Reliability Oriented Control and Cooling Perspectives

by Hakan Polat, Farzad Hosseinabadi, Md. Mahamudul Hasan, Sajib Chakraborty, Thomas Geury, Mohamed El Baghdadi, Steven Wilkins and Omar Hegazy

Batteries 2023, 9(2), 121; https://doi.org/10.3390/batteries9020121 - 8 Feb 2023

Cited by 44 | Viewed by 20313

Abstract

The global promotion of electric vehicles (EVs) through various incentives has led to a significant increase in their sales. However, the prolonged charging duration remains a significant hindrance to the widespread adoption of these vehicles and the broader electrification of transportation. While DC-fast chargers have the potential to significantly reduce charging time, they also result in high power demands on the grid, which can lead to power quality issues and congestion. One solution to this problem is the integration of a battery energy storage system (BESS) to decrease peak power demand on the grid. This paper presents a review of the state-of-the-art use of DC-fast chargers coupled with a BESS. The focus of the paper is on industrial charger architectures and topologies. Additionally, this paper presents various reliability-oriented design methods, prognostic health monitoring techniques, and low-level/system-level control methods. Special emphasis is placed on strategies that can increase the lifetime of these systems. Finally, the paper concludes by discussing various cooling methods for power electronics and stationary/EV batteries. Full article

(This article belongs to the Special Issue Advances in Charging Systems and Charging Management Strategies for Battery Electric Vehicles)

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