Version 1
: Received: 9 September 2024 / Approved: 10 September 2024 / Online: 10 September 2024 (10:59:01 CEST)
How to cite:
Wu, Y.; Balasingam, B. A Comparison of Battery Equivalent Circuit Model Parameter Extraction Approaches Based on Electrochemical Impedance Spectroscopy. Preprints2024, 2024090793. https://doi.org/10.20944/preprints202409.0793.v1
Wu, Y.; Balasingam, B. A Comparison of Battery Equivalent Circuit Model Parameter Extraction Approaches Based on Electrochemical Impedance Spectroscopy. Preprints 2024, 2024090793. https://doi.org/10.20944/preprints202409.0793.v1
Wu, Y.; Balasingam, B. A Comparison of Battery Equivalent Circuit Model Parameter Extraction Approaches Based on Electrochemical Impedance Spectroscopy. Preprints2024, 2024090793. https://doi.org/10.20944/preprints202409.0793.v1
APA Style
Wu, Y., & Balasingam, B. (2024). A Comparison of Battery Equivalent Circuit Model Parameter Extraction Approaches Based on Electrochemical Impedance Spectroscopy. Preprints. https://doi.org/10.20944/preprints202409.0793.v1
Chicago/Turabian Style
Wu, Y. and Balakumar Balasingam. 2024 "A Comparison of Battery Equivalent Circuit Model Parameter Extraction Approaches Based on Electrochemical Impedance Spectroscopy" Preprints. https://doi.org/10.20944/preprints202409.0793.v1
Abstract
This paper presents three approaches to estimating 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 with 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 LS approach, the ES and NLS approaches show better fitting accuracy at various noise levels; whereas both in 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.
Engineering, Electrical and Electronic Engineering
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
