A Decoupled Multistage Parameter Estimation Framework for Lithium‐Ion Battery Models

ABSTRACT This paper presents a novel Decoupled Optimization Algorithm (DOA) to estimate lithium‐ion battery equivalent circuit parameters. The algorithm precisely models the dynamic behavior of LIBs during both the discharge and relaxation phases. Its main innovation lies in its ability to estimate parameters for each operating state independently, resulting in a more accurate model. The DOA is notably robust, as it naturally avoids premature convergence without requiring empirical hyperparameter tuning or training data. In addition, the model includes boundary conditions that guarantee a mathematically and physically sound solution, which streamlines the search process. Experimental validation in five different discharge rates confirmed that the DOA achieves superior accuracy in parameter estimation compared to established algorithms such as GA, PSO, CSA, and VLCS. These results underscore its potential for practical battery management applications.

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Publication Details

Journal
Energy Storage
Published
2026-06-29
DOI
https://doi.org/10.1002/est2.70454
Primary Topic
Advanced Battery Technologies Research
Type
article
Field-Weighted Citation Impact
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article

A Decoupled Multistage Parameter Estimation Framework for Lithium‐Ion Battery Models

Tasadeek Hassan Dar, Moisés Machado Santos, Paulo Sérgio Sausen, Bruno Muraro Perondi et al.
Energy Storage
Advanced Battery Technologies Research
article

A Decoupled Multistage Parameter Estimation Framework for Lithium‐Ion Battery Models

Tasadeek Hassan Dar, Moisés Machado Santos, Paulo Sérgio Sausen, Bruno Muraro Perondi, Airam Teresa Zago Romcy Sausen
article en

Abstract

ABSTRACT This paper presents a novel Decoupled Optimization Algorithm (DOA) to estimate lithium‐ion battery equivalent circuit parameters. The algorithm precisely models the dynamic behavior of LIBs during both the discharge and relaxation phases. Its main innovation lies in its ability to estimate parameters for each operating state independently, resulting in a more accurate model. The DOA is notably robust, as it naturally avoids premature convergence without requiring empirical hyperparameter tuning or training data. In addition, the model includes boundary conditions that guarantee a mathematically and physically sound solution, which streamlines the search process. Experimental validation in five different discharge rates confirmed that the DOA achieves superior accuracy in parameter estimation compared to established algorithms such as GA, PSO, CSA, and VLCS. These results underscore its potential for practical battery management applications.

Energy StorageVol. 8(5)
SRM University, Andhra Pradesh (IN), University of Northwestern (US)
Industry, innovation and infrastructure
Openalex Percentile: Top 11%
Advanced Battery Technologies Research
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