Battery and Energy Storage Systems in Industrial Applications

A special issue of Electronics (ISSN 2079-9292). This special issue belongs to the section "Industrial Electronics".

Deadline for manuscript submissions: closed (31 October 2025) | Viewed by 973

Special Issue Editor


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Guest Editor
Department of Smart Mobility Engineering, Kyungpook National University, Daegu-si 41566, Republic of Korea
Interests: lithium ion battery; battery management system; power conversion circuit; artificial intelligence; nonlinear dynamics

Special Issue Information

Dear Colleagues,

Battery and Energy Storage System in industrial applications are currently facing increasingly severe challenges, such as enhancement of energy density, extends of lifespan, improvement of safety and faster charging technology, integrated with renewable energy for environment and sustainability.

This Special Issue aims to cultivate novel, safe, systematic and economic approaches to the industrial application of electronics technology in energy system. We require to not only clarify the problems concerning energy storage and system technology itself, but also actively cooperate to figure out other interdisciplinary problems in energy storage systems. The topics include, but are not limited to, the following:

  • Energy storage device using electrochemistry
  • Energy storage device manufacturing technology
  • Electrochemical analysis technology
  • Energy storage system
  • Battery management system
  • Power conversion system related with energy storage device and system
  • Battery pack and module technology for industry applications
  • Battery artificial intelligence technology

Prof. Dr. Jimin Oh
Guest Editor

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Keywords

  • energy storage devices
  • battery management systems
  • energy storage materials
  • energy conversion system
  • artificial intelligence

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Published Papers (1 paper)

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Research

14 pages, 2089 KB  
Article
State of Charge (SoC) Estimation with Electrochemical Impedance Spectroscopy (EIS) Data Using Different Ensemble Machine Learning Algorithms
by Ernest Ozoemela Ezugwu, Indranil Bhattacharya, Adeloye Ifeoluwa Ayomide and Mary Vinolisha Antony Dhason
Electronics 2025, 14(22), 4423; https://doi.org/10.3390/electronics14224423 - 13 Nov 2025
Viewed by 766
Abstract
Accurate state of charge (SoC) estimation is critical for the safety, performance, and longevity of lithium-ion batteries in electric vehicles and energy storage systems. This study investigates the application of Electrochemical Impedance Spectroscopy (EIS) data in conjunction with tree-based ensemble machine learning algorithms—Random [...] Read more.
Accurate state of charge (SoC) estimation is critical for the safety, performance, and longevity of lithium-ion batteries in electric vehicles and energy storage systems. This study investigates the application of Electrochemical Impedance Spectroscopy (EIS) data in conjunction with tree-based ensemble machine learning algorithms—Random Forest, Extra Trees, Gradient Boosting, XGBoost, and AdaBoost—for precise SoC prediction. A real dataset comprising multi-frequency EIS measurements was used to train and evaluate the models. The models’ performances were assessed using Mean Squared Error (MSE), Root Mean Squared Error (RMSE), and the coefficient of determination (R2). The results show that Extra Trees achieved the best accuracy (MSE = 1.76, RMSE = 1.33, R2 = 0.9977), followed closely by Random Forest, Gradient Boosting, and XGBoost, all maintaining RMSE values below 1.6% SoC. Predictions from these models closely matched the ideal 1:1 relationship, with tightly clustered error distributions indicating minimal bias. AdaBoost returned a higher RMSE (3.06% SoC) and a broader error spread. These findings demonstrate that tree-based ensemble models, particularly Extra Trees and Random Forest, offer robust, high-accuracy solutions for EIS-based SoC estimation, making them promising candidates for integration into advanced battery management systems. Full article
(This article belongs to the Special Issue Battery and Energy Storage Systems in Industrial Applications)
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