Comparison of Parameter Estimation Methods for Determining the Parameters of the Battery Electrical Equivalent Circuit Model

Lithium-ion battery chemistry has become the preferred choice in modern electrical energy storage systems. While it offers many advantages, its reactive nature can lead to hazardous situations. To prevent this, parameters such as voltage, current, temperature, and state of charge (SOC) must be monitored. In order to carry out this monitoring process, the battery's behavior needs to be mathematically modeled. For this purpose, parameters were determined for equivalent circuit models of varying degrees using different curve-fitting methods. These parameters were derived from test data of A123 Systems' ANR26650M1-B and Samsung's INR 18650 20R battery types. Models created through different curve-fitting methods were then compared with the test data. As a result of this study, a curvefitting method was identified that provides a fast, easy, and highly consistent equivalent circuit model.

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Publication Details

Published
2024-11-28
DOI
https://doi.org/10.1109/eleco64362.2024.10847219
Citations
3
Primary Topic
Advanced Battery Technologies Research
Type
article
Field-Weighted Citation Impact
0.61
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article

Comparison of Parameter Estimation Methods for Determining the Parameters of the Battery Electrical Equivalent Circuit Model

Ege Uzun, Mehmet Onur Gülbahçe, Mustafa Alparslan Zehir, İlhan Kocaarslan et al.
3 citations
Advanced Battery Technologies Research
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article

Comparison of Parameter Estimation Methods for Determining the Parameters of the Battery Electrical Equivalent Circuit Model

Ege Uzun, Mehmet Onur Gülbahçe, Mustafa Alparslan Zehir, İlhan Kocaarslan, Batı Eren Ergun, Arda Akyıldız, Mithat Can Çamur, Cavit Fatih Küçüktezcan
article en
3 citations

Abstract

Lithium-ion battery chemistry has become the preferred choice in modern electrical energy storage systems. While it offers many advantages, its reactive nature can lead to hazardous situations. To prevent this, parameters such as voltage, current, temperature, and state of charge (SOC) must be monitored. In order to carry out this monitoring process, the battery's behavior needs to be mathematically modeled. For this purpose, parameters were determined for equivalent circuit models of varying degrees using different curve-fitting methods. These parameters were derived from test data of A123 Systems' ANR26650M1-B and Samsung's INR 18650 20R battery types. Models created through different curve-fitting methods were then compared with the test data. As a result of this study, a curvefitting method was identified that provides a fast, easy, and highly consistent equivalent circuit model.

Affordable and clean energy
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Advanced Battery Technologies Research
0.61
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