# Parameter identification of lithium-ion batteries: a comparative study of various models and optimization techniques for battery modeling

Johan Sebastian Suarez Sepulveda, Edgar Hernando Sepúlveda-Oviedo, Bruno Jammes, Corinne Alonso

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Johan Sebastian Suarez Sepulveda, Edgar Hernando Sepúlveda-Oviedo, Bruno Jammes, Corinne Alonso. Parameter identification of lithium-ion batteries: a comparative study of various models and optimization techniques for battery modeling. XII Congreso Internacional de Ingeniería Mecánica, Mecatrónica y Automatización, May 2025, Montería, Colombia. hal-05148005

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Submitted on 8 Jul 2025

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# PARAMETER IDENTIFICATION OF LITHIUM-ION BATTERIES: A COMPARATIVE STUDY OF VARIOUS MODELS AND OPTIMIZATION TECHNIQUES FOR BATTERY MODELING

Johan Sebastian Suarez Sepulveda (1,2), Edgar Hernando Sepúlveda-Oviedo (1,2), Bruno Jammes (1), Corinne Alonso (1)

1. LAAS-CNRS, Université fédérale de Toulouse, UPS, CNRS, France;

2. Universidad Nacional de Colombia, Colombia;

## Thematic Area: Energy – Energy Conversion and Storage

### Introduction

Lithium-ion batteries are considered a key technology in sectors such as aerospace and automotive due to their high power and energy density. Accurate modeling of these batteries is considered essential for evaluating performance in terms of lifespan, state of charge, and aging. This study focuses on the analysis and comparison of optimization methods to improve the accuracy of parameter identification for a model based on an equivalent circuit. Specifically, traditional, metaheuristic, and bioinspired optimization approaches are examined and contrasted, while advanced tools in MATLAB are used to evaluate their effectiveness.

### Methods

The parameter identification of the 2RC equivalent circuit model [Peng, 2023], as shown in Figure 1, is performed on 15 batteries using 4 free datasets acquired from different research centers (NASA, CALCE, OXFORD, HNEI) existing in the state of the art [5].

<figure><img src="image_2.png" /><figcaption>Figure 1: 2RC Equivalent Circuit Model of Battery Behavior</figcaption></figure>

The methodology implemented for the 2RC equivalent circuit model shown in Figure 1 is composed of four steps: i) A bibliographic analysis is conducted to review the models used for battery behavior modeling and the optimization methods

applied for parameter identification. ii) Battery cycle datasets are acquired from [Ma, 2021]. iii) Optimization methods are utilized to minimize the discrepancy between the measured data and the equation representing the model during parameter identification. iv) The accuracy and execution time (ET) of the optimization methods are evaluated.

The optimization methods used in this study include traditional and metaheuristic methods, such as Least Squares Optimization [Pan, 2021], Particle Swarm Optimization [He, 2021], among others [Dehghani, 2021], [Peraza-Vázquez, 2021]. These methods are compared in terms of execution time (ET) in seconds, and their accuracy is measured using the mean squared error (MSE) in comparison with the actual values from the datasets [Battery Archive, 2025]. This metric is selected because MSE is a standard metric for evaluating regression and optimization models [Kumar, 2023]. The results are presented in Table 1.

<table><caption>Table 1: Computational Cost and Accuracy of the Different Optimization Methods.</caption><thead><tr><td>Method</td><td>MSE</td><td>ET (s)</td></tr></thead><tbody><tr><td>Least Squares</td><td>7.10<sup>-6</sup></td><td>0.45</td></tr><tr><td>Particle Swarm</td><td>1.10<sup>-7</sup></td><td>1.46</td></tr><tr><td>Simulated Annealing</td><td>1.10<sup>-6</sup></td><td>0.6</td></tr><tr><td>Genetic Algorithm</td><td>3.10<sup>-6</sup></td><td>1.47</td></tr><tr><td>Golf Field</td><td>6.10<sup>-5</sup></td><td>1.25</td></tr><tr><td>Australian Dingo</td><td>3.10<sup>-6</sup></td><td>2.98</td></tr><tr><td>Mexican Axolotl</td><td>5.10<sup>-4</sup></td><td>13.9</td></tr><tr><td>Spider Jumping</td><td>7.10<sup>-5</sup></td><td>29</td></tr></tbody></table>

### Results

As shown in Table 1, superior accuracy in parameter identification is achieved by the Simulated Annealing and Particle Swarm
Optimization methods when compared to the Least Squares method. In particular, a significant improvement in accuracy is obtained by Particle Swarm Optimization without a substantial increase in the computational time required, as detailed in Figure 2 and Table 1.

<figure><img src="image_3.png" /><figcaption>Figure 2: Comparative of the Accuracy of the Optimization Methods Least Squares (LS) and Particle Swarm Optimization (PS).</figcaption></figure>

## Discussion

Metaheuristic and bioinspired methods offer significant advantages in scenarios where parameter identification is challenging due to model nonlinearity. While traditional methods like Least Squares Optimization are efficient, their performance is often limited when dealing with complex, multidimensional parameter spaces. In contrast, methods such as Particle Swarm Optimization demonstrate superior accuracy by effectively exploring the solution space and avoiding local minima, without a substantial increase in computational time.

## References

[1] Battery Archive, Battery Archive Dataset, battery archive.org, 2025.

[2] Dehghani M et al, Hindawi, (2021):9107547, 2021.

[3] He M et al, Int J Electrochem Sci, (16)(5):21054, 2021.

[4] Kumar D et al, Appl Sci, (13)(14):8428, 2023.

[5] Ma X et al, Energy Storage Mater, (37):177-203, 2021.

[6] Pan H et al, Electrochim Acta, (390):138235, 2021.

[7] Peng J et al, J Energy Storage, (71):108197, 2023.

[8] Peraza-Vázquez H et al, Mathematics, (10)(1):102, 2021.