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: 15 April 2025 | Viewed by 743

Special Issue Editors


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Guest Editor
Department of Electrical and Computer Engineering, University of Connecticut, Storrs, CT, USA
Interests: power electronics; energy conversion; renewable energy; smart-grid applications

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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
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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

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Keywords

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

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Published Papers (1 paper)

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Research

25 pages, 1385 KiB  
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
Viewed by 518
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
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