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Batteries
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Battery Management Systems Based on Electrochemical Impedance Spectroscopy
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Battery Management Systems Based on Electrochemical Impedance Spectroscopy

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: closed (31 January 2026) | Viewed by 17136

Special Issue 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-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

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

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Related Special Issue

Published Papers (5 papers)

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Research

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22 pages, 10243 KB  
Article
A Novel Empirical Degradation-Guided Transformer–GRU Network for Predicting Battery Capacity Degradation
by Xiandao Lei, Chenyu Liu, Zeping Chen, Jin Fang, Shanshan Guo and Caiping Zhang
Batteries 2026, 12(3), 85; https://doi.org/10.3390/batteries12030085 - 2 Mar 2026
Viewed by 928
Abstract
Battery ageing is inevitable during operation, leading not only to performance degradation but to potential safety concerns. Consequently, accurate prediction of the state of health (SOH) of lithium-ion batteries is crucial for ensuring their safety and reliability. This study proposed a novel hybrid [...] Read more.
Battery ageing is inevitable during operation, leading not only to performance degradation but to potential safety concerns. Consequently, accurate prediction of the state of health (SOH) of lithium-ion batteries is crucial for ensuring their safety and reliability. This study proposed a novel hybrid neural network architecture that integrates a transformer module, an empirical degradation (ED) model, and a gated recurrent unit (GRU). The transformer module enhances the global representation of the feature sequence, while the ED model comprehensively considers the impact of temperature on the rate of battery capacity degradation, compensating the un-interpretability of the transformer architecture in predicting SOH. In addition, pseudo-incremental capacity curves have been obtained using charging fragments from multi-stage constant current fast charging, which solves the issue of extracting mechanism features under fast charging conditions. Experimental results demonstrate that, across a wide temperature range, the model maintains a low average RMSE between 0.43% and 0.59% for prediction horizons of 4 to 128 cycles. Specifically, the average RMSE is 0.87% at −5 °C and 0.37% between 25 °C and 55 °C. Compared to standalone data-driven models, the proposed hybrid architecture reduces prediction error by approximately 50% at 25 °C, exhibiting superior predictive performance and robustness. Full article
(This article belongs to the Special Issue Battery Management Systems Based on Electrochemical Impedance Spectroscopy)
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15 pages, 1353 KB  
Article
Battery State-of-Health Estimation with Embedded Impedance Spectrum Features Under Multiple Battery Chemistry and Temperature Conditions
by Yue Xiang, Dikshit Chauhan and Dipti Srinivasan
Batteries 2026, 12(2), 77; https://doi.org/10.3390/batteries12020077 - 20 Feb 2026
Viewed by 902
Abstract
The transition to clean energy and electrification of transportation requires accurate, real-time monitoring of the state of health (SoH) of lithium-ion batteries, which serve as critical components for energy storage. Conventional SoH estimation methods typically rely on fixed statistical feature extraction, have poor [...] Read more.
The transition to clean energy and electrification of transportation requires accurate, real-time monitoring of the state of health (SoH) of lithium-ion batteries, which serve as critical components for energy storage. Conventional SoH estimation methods typically rely on fixed statistical feature extraction, have poor generalization ability, and are unsuitable for multiple battery chemistry and temperature conditions. In this work, we propose a deep learning framework based on a transformer encoder and XGBoost to extract ageing-related electrochemical impedance spectroscopy (EIS) features, capturing low-, mid-, and high-frequency ageing characteristics, directly from daily operation profiles for capacity estimation. The approach requires only current, voltage, and temperature time-series data, making it suitable for edge deployment without the need for explicit EIS measurements. Validation on a dataset with two battery chemistries and three temperature conditions yields a root-mean-square error of 0.16% to 0.20% in capacity estimation. These results establish the feasibility of accurate SoH estimation during multiple operation of battery energy storage systems and electric vehicles. Full article
(This article belongs to the Special Issue Battery Management Systems Based on Electrochemical Impedance Spectroscopy)
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16 pages, 5211 KB  
Article
Measurement of Battery Aging Using Impedance Spectroscopy with an Embedded Multisine Coherent Measurement System
by Jorge Lourenço, Luis S. Rosado, Pedro M. Ramos and Fernando M. Janeiro
Batteries 2025, 11(6), 227; https://doi.org/10.3390/batteries11060227 - 10 Jun 2025
Cited by 1 | Viewed by 3428
Abstract
This work describes the development of an embedded standalone measurement system that monitors the aging of batteries using impedance spectroscopy. The system generates a multisine stimulus that contains the frequency components at which the battery impedance is measured. Coherent generation and sampling is [...] Read more.
This work describes the development of an embedded standalone measurement system that monitors the aging of batteries using impedance spectroscopy. The system generates a multisine stimulus that contains the frequency components at which the battery impedance is measured. Coherent generation and sampling is assured, and Goertzel filters, one for each measurement frequency, are updated with each new sample. This architecture reduces memory requirements because the current and voltage of the measured samples are discarded after processing. Aging is monitored, as the system is able to automatically perform complete or partial charge/discharge cycles as well as measurement cycles without requiring user interaction. Full article
(This article belongs to the Special Issue Battery Management Systems Based on Electrochemical Impedance Spectroscopy)
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25 pages, 1385 KB  
Article
A Comparison of Battery Equivalent Circuit Model Parameter Extraction Approaches Based on Electrochemical Impedance Spectroscopy
by Yuchao Wu and Balakumar Balasingam
Batteries 2024, 10(11), 400; https://doi.org/10.3390/batteries10110400 - 10 Nov 2024
Cited by 13 | Viewed by 6205
Abstract
This paper presents three approaches to estimating the battery parameters of the electrical equivalent circuit model (ECM) based on electrochemical impedance spectroscopy (EIS); these approaches are referred to as (a) least squares (LS), (b) exhaustive search (ES), and (c) nonlinear least squares (NLS). [...] Read more.
This paper presents three approaches to estimating the battery parameters of the electrical equivalent circuit model (ECM) based on electrochemical impedance spectroscopy (EIS); these approaches are referred to as (a) least squares (LS), (b) exhaustive search (ES), and (c) nonlinear least squares (NLS). The ES approach is assisted by the LS method for the rough determination of the lower and upper bound of the ECM parameters, and the NLS approach is incorporated with the Monte Carlo run such that different initial guesses can be assigned to improve the goodness of EIS fitting. The proposed approaches are validated using both simulated and real EIS data. Compared to the LS approach, the ES and NLS approaches show better fitting accuracy at various noise levels, whereas in both the validation using simulated EIS data and actual EIS data collected from LG 18650 and Molicel 21700 batteries, the NLS approach shows better fitting accuracy than that of LS and ES approaches. In all cases, compared with the ES approach, the computational time of the NLS approach is significantly faster, and compared with the LS approach, the NLS approach shows a minimal difference in computational time and considerably better fitting performance. Full article
(This article belongs to the Special Issue Battery Management Systems Based on Electrochemical Impedance Spectroscopy)
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Review

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38 pages, 3819 KB  
Review
Battery Passport and Online Diagnostics for Lithium-Ion Batteries: A Technical Review of Materials–Diagnostics Interactions and Online EIS
by Muhammad Usman Tahir, Tarek Ibrahim and Tamas Kerekes
Batteries 2025, 11(12), 442; https://doi.org/10.3390/batteries11120442 - 1 Dec 2025
Cited by 1 | Viewed by 3822
Abstract
Digital battery passports are being adopted to provide traceable records of lithium-ion batteries across their lifecycle, credible performance, and durability. However, it requires continuous diagnostics rather than lab-based tests and conditions. This review establishes a materials-informed system that links (i) battery-passport frameworks, (ii) [...] Read more.
Digital battery passports are being adopted to provide traceable records of lithium-ion batteries across their lifecycle, credible performance, and durability. However, it requires continuous diagnostics rather than lab-based tests and conditions. This review establishes a materials-informed system that links (i) battery-passport frameworks, (ii) cell-level design, and (iii) online electrochemical impedance spectroscopy (EIS) observables. Therefore, a chemistry-aware indicator set is proposed for passport reporting that relies on capacity and impedance indices, each accompanied by explicit tests. A review of the common and commercial LIBs (LCO, NCA, NMC, LMO, LFP) explains differences and characteristics. In addition, online EIS is reviewed, and different techniques for battery online diagnostics and state estimation are described, with details on how this online analysis is incorporated into the battery passport framework. This review covers the battery passport framework, the materials used in commercial batteries that must be documented and traced, and how these materials evolve throughout the degradation process. It concludes with the state of the art in online battery cell inspection, which enables comparable health reporting, conformity assessment, and second-life grading. Finally, it outlines key implementation priorities related to the reliability and accuracy of battery passport deployment and online battery diagnostics. Full article
(This article belongs to the Special Issue Battery Management Systems Based on Electrochemical Impedance Spectroscopy)
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