Optimal parameter identification strategy applied to lithium-ion battery model for electric vehicles using drive cycle data

The optimal parameter identification of lithium-ion (Li-ion) battery models is essential for accurately capturing battery behavior and performance in electric vehicle (EV) applications. Traditional methods for parameter identification often rely on manual tuning or trial-and-error approaches, which...

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Bibliographic Details
Published in:Energy reports Vol. 11; pp. 2049 - 2058
Main Authors: Ghadbane, Houssam Eddine, Rezk, Hegazy, Ferahtia, Seydali, Barkat, Said, Al-Dhaifallah, Mujahed
Format: Journal Article
Language:English
Published: Elsevier Ltd 01.06.2024
Elsevier
Subjects:
Electric vehicles
Engineering Sciences
Li-ion battery
Metaheuristic optimization algorithms
Parameters identification
Metaheuristic optimization algorithms
Electric vehicles
Li-ion battery
Parameters identification
Electric vehicles, Li-ion battery, Metaheuristic optimization algorithms, Parameters identification
ISSN:2352-4847, 2352-4847
Online Access:Get full text
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Summary:The optimal parameter identification of lithium-ion (Li-ion) battery models is essential for accurately capturing battery behavior and performance in electric vehicle (EV) applications. Traditional methods for parameter identification often rely on manual tuning or trial-and-error approaches, which can be time-consuming and yield suboptimal results. In recent years, metaheuristic optimization algorithms have emerged as powerful tools for efficiently searching and identifying optimal parameter values. This paper proposes an optimal parameter identification strategy using a metaheuristic optimization algorithm applied to a Shepherd model for EV applications. The identification technique that was based on the Self-adaptive Bonobo Optimizer (SaBO) performed extremely well when it came to the process of identifying the battery's unidentified properties. Because of this, the overall voltage error of the suggested identification technique has been lowered to 4.2377 × 10−3, and the root mean square error (RMSE) between the model and the data has been calculated to be 8.64 × 10−3. In addition, compared to the other optimization methods, the optimization efficiency was able to attain 96.6%, which validated its efficiency.
ISSN:2352-4847
2352-4847
DOI:10.1016/j.egyr.2024.01.073