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Batteries
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Battery Management Systems Based on Electrochemical Impedance Spectroscopy—2nd Edition
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Battery Management Systems Based on Electrochemical Impedance Spectroscopy—2nd Edition

A Special Issue of Batteries (ISSN 2313-0105) belonging to the section "Energy Storage System Aging, Diagnosis and Safety".

Deadline for manuscript submissions: 30 November 2026 | Viewed by 1876

Editors

Dr. Sung Yeul Park

E-Mail Website
Guest Editor
Department of Electrical and Computer Engineering, University of Connecticut, Storrs, CT, USA
Interests: power electronics; energy conversion; renewable energy; smart-grid applications
Special Issues, Collections and Topics in MDPI journals
Dr. Balakumar Balasingam

E-Mail Website
Guest Editor
Department of Electrical & Computer Engineering, University of Windsor, 401 Sunset Avenue, Windsor, ON N9B 3P4, Canada
Interests: battery management systems; human–machine systems; signal processing; machine learning; information fusion
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Electrochemical impedance spectroscopy (EIS) is emerging as a vital tool for enhancing battery management systems (BMSs), offering precise insights into a battery’s state of health, charge, and overall performance. This Special Issue aims to explore the integration of EIS within BMS, focusing on cutting-edge research which leverages impedance data for the real-time monitoring, predictive maintenance, and optimization of battery systems. We invite contributions addressing both theoretical and practical aspects, including novel EIS techniques, data interpretation methods, and their application to various battery chemistries, alongside studies that bridge the gap between laboratory-scale experiments and real-world applications, as well as those exploring the challenges of implementing EIS in commercial BMSs. As this Special Issue seeks to advance the understanding and application of EIS in BMSs, providing a platform for researchers to present innovative solutions which enhance battery longevity, safety, and efficiency, authors are encouraged to submit original research, review articles, and case studies that contribute to this rapidly evolving field. 

Dr. Sung Yeul Park
Dr. Balakumar Balasingam
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-anonymized 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

  • electrochemical impedance spectroscopy (EIS)
  • battery management systems (BMS)
  • battery performance
  • real-time monitoring
  • predictive maintenance

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Related Special Issue

Published Papers (2 papers)

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Research

20 pages, 1232 KB  
Article
Variability Analysis of Battery EIS Measurements
by Prarthana Pillai, Banuselvasaraswathy Balasubramanian, Krishna R. Pattipati and Balakumar Balasingam
Batteries 2026, 12(7), 258; https://doi.org/10.3390/batteries12070258 - 17 Jul 2026
Viewed by 547
Abstract
Electrochemical Impedance Spectroscopy (EIS) is a non-destructive technique for characterizing the battery behavior for estimating the state of health (SOH). EIS provides frequency-domain information on key parameters, including solid electrolyte interphase (SEI) resistance, charge-transfer (CT) resistance, and ohmic resistance, which are sensitive to [...] Read more.
Electrochemical Impedance Spectroscopy (EIS) is a non-destructive technique for characterizing the battery behavior for estimating the state of health (SOH). EIS provides frequency-domain information on key parameters, including solid electrolyte interphase (SEI) resistance, charge-transfer (CT) resistance, and ohmic resistance, which are sensitive to battery degradation mechanisms. In an EIS test, a sinusoidal excitation signal is applied to the battery, and the corresponding voltage response is analyzed to extract the impedance spectrum. The reliability of SOH estimation therefore depends critically on the accurate and repeatable extraction of impedance features. This paper investigates the variability in impedance spectra arising from the state of charge (SOC), temperature, rest time, and repeated measurements under nominally identical conditions. This variability is identified as drift and represents previously underexplored variations in the impedance spectrum. To quantify these variations, this work proposes a normalized resistance-based index that captures changes in the impedance spectrum using estimated equivalent circuit model (ECM) parameters. The proposed index is applicable across battery chemistries, sizes, and operating conditions. It is evaluated using published datasets spanning different chemistries, SOC levels, and temperatures, as well as laboratory data collected from repeated EIS experiments. The results show that even at fixed SOC and temperature, repeated measurements can produce measurable bias and variance in ECM parameters. These findings highlight the importance of accounting for drift in EIS analysis and motivate uncertainty-aware battery diagnostics for practical SOH monitoring systems. Full article
(This article belongs to the Special Issue Battery Management Systems Based on Electrochemical Impedance Spectroscopy—2nd Edition)
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19 pages, 7583 KB  
Article
From Operation to SOH Estimation: Analysis of Lithium-Ion Capacitors Based on Passive EIS for E-Bus Application
by Tarek Ibrahim, Muhammad Usman Tahir, Mohamed Abdel-Monem, Erik Schaltz, Vaclav Knap, Daniel Ioan Stroe and Tamas Kerekes
Batteries 2026, 12(6), 212; https://doi.org/10.3390/batteries12060212 - 10 Jun 2026
Viewed by 905
Abstract
Real-time monitoring of lithium-ion capacitors (LICs) is crucial for ensuring reliability and predictive maintenance in dynamic applications such as electric transportation. However, traditional electrochemical impedance spectroscopy (EIS) techniques are complex and costly for onboard diagnostics due to their reliance on external excitation signals [...] Read more.
Real-time monitoring of lithium-ion capacitors (LICs) is crucial for ensuring reliability and predictive maintenance in dynamic applications such as electric transportation. However, traditional electrochemical impedance spectroscopy (EIS) techniques are complex and costly for onboard diagnostics due to their reliance on external excitation signals and dedicated hardware. Therefore, this paper presents an innovative framework for online state of health (SOH) estimation that bypasses these limitations by utilizing fast Fourier transform (FFT)-based passive impedance extraction directly from operational current and voltage signals. From experimental data, the equivalent circuit model (ECM) is developed, as well as its parameters, such as ohmic resistance, charge-transfer resistance, and Warburg diffusion. These parameters are identified through the extraction of impedance points in the low frequency region through FFT and the series resistance point using ohmic measurement, then performing a periodic curve fitting to these points. These curve fittings provide extracted ECM parameters. These parameters are used with a trained model to estimate the SOH of the monitored cell and are updated online. The proposed method was experimentally validated on five LIC cells aged under various C-rates (1C, 4C, 7C) and temperatures (35 °C, 40 °C, 50 °C), showing consistent impedance evolution with capacity fade. Validation of the utilized machine learning models, such as Polynomial Regression (PR), principal components analysis (PCA), and random forest (RF) regression, achieved SOH prediction errors as low as 2.23% compared to experimental results. The developed framework is particularly suitable for applications such as flash-charged electric buses but is broadly applicable across other energy storage systems as well. This advanced method enables real-time diagnostics without hardware modification, offering significant potential for integration into existing battery management systems (BMSs). Full article
(This article belongs to the Special Issue Battery Management Systems Based on Electrochemical Impedance Spectroscopy—2nd Edition)
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