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EV (Electric Vehicle) Energy Storage and Battery Management

A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Energy Science and Technology".

Deadline for manuscript submissions: 30 November 2025 | Viewed by 629

Special Issue Editors


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Guest Editor

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Guest Editor
Institut de Recerca en Energia de Catalunya, 08930 Barcelona, Spain
Interests: energy storage; control systems; advanced modelling; grid integration
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Emerging and disruptive technologies in power electronics and energy storage are pivotal to advancing the automotive industry and renewable energy networks. The global push toward electrification, decarbonization, and smart grid integration demands innovative solutions that enhance efficiency, reliability, and sustainability. Breakthroughs in wide-bandgap semiconductors, next-generation battery systems, and intelligent energy management are transforming energy conversion, storage, and distribution. However, challenges persist in scaling these technologies for real-world applications, including thermal management, cost reduction, lifecycle durability, and interoperability within complex energy ecosystems. Bridging the gap between laboratory innovations and industrial deployment is critical to achieving net-zero goals and fostering resilient energy infrastructures.

This Special Issue seeks cutting-edge research addressing the integration of advanced power electronics and energy storage systems into automotive and renewable energy applications. Topics of interest include, but are not limited to, the following:

  • Battery management system (BMS) design and development;
  • Wide-bandgap (SiC, GaN) devices for high-efficiency power conversion;
  • Solid-state batteries, supercapacitors, and hybrid energy storage solutions;
  • Vehicle-to-grid (V2G) integration and bidirectional charging systems;
  • Thermal management and safety protocols for high-density energy storage;
  • AI-driven energy management and predictive maintenance in smart grids;
  • Sustainable materials and circular economy approaches for battery production;
  • Fast-charging technologies and wireless power transfer systems;
  • Case studies on renewable energy microgrids and electrified transportation networks.

We welcome original research, comprehensive reviews, and comparative studies that highlight technological advancements, address scalability challenges, and demonstrate practical applications. Submissions emphasizing interdisciplinary approaches and real-world impacts are particularly encouraged.

Dr. Levon Gevorkov
Dr. Lluís Trilla
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. Applied Sciences 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 2400 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

  • power electronics
  • energy storage systems
  • automotive electrification
  • renewable energy integration
  • wide-bandgap semiconductors
  • battery management
  • smart grids
  • sustainable technologies

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Published Papers (1 paper)

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Research

15 pages, 5573 KB  
Article
Cell State-of-Charge Estimation with Limited Voltage Sensor Measurements
by Owais Ogdeh, Luke Nuculaj, Ali Irshayyid, Zhaodong Zhou and Jun Chen
Appl. Sci. 2025, 15(18), 10127; https://doi.org/10.3390/app151810127 - 17 Sep 2025
Viewed by 406
Abstract
This paper presents a practical experiment for estimating the state-of-charge (SOC) of individual cells in a series-connected heterogeneous lithium-ion battery pack, where only the terminal voltage of the battery pack is measured. To deal with real-time computation constraints, the dense extended Kalman filter [...] Read more.
This paper presents a practical experiment for estimating the state-of-charge (SOC) of individual cells in a series-connected heterogeneous lithium-ion battery pack, where only the terminal voltage of the battery pack is measured. To deal with real-time computation constraints, the dense extended Kalman filter (DEKF) algorithm has been proposed in the literature, which has a significantly lower computational complexity compared to the regular extended Kalman filter for this specific estimation problem. This work supplements the existing work by conducting a real-world experiment to validate the performance of the DEKF. Specifically, experiments involving a battery pack of three cells connected in series were conducted, where the battery pack was discharged under a constant current load. A genetic algorithm was applied to identify missing model parameters, as well as tuning the DEKF for optimal convergence and accurate SOC estimation. Our experimental results confirm that the proposed DEKF accurately estimates the SOC of each cell regardless of the hardware limitations and uncertainty, making it suitable for low-cost, real-time battery management systems. In particular, the SOC estimation error can be kept well under 1% even if the initial estimate is far from the true SOC. Full article
(This article belongs to the Special Issue EV (Electric Vehicle) Energy Storage and Battery Management)
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