Parameter Identification of Lithium-Ion Battery Model Based on African Vultures Optimization Algorithm

Hend M. Fahmy, Rania A. Sweif, Hany M. Hasanien (), Marcos Tostado-Véliz, Mohammed Alharbi and Francisco Jurado
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Hend M. Fahmy: Electrical Power and Machines Department, Faculty of Engineering, Ain Shams University, Cairo 11517, Egypt
Rania A. Sweif: Electrical Power and Machines Department, Faculty of Engineering, Ain Shams University, Cairo 11517, Egypt
Hany M. Hasanien: Electrical Power and Machines Department, Faculty of Engineering, Ain Shams University, Cairo 11517, Egypt
Marcos Tostado-Véliz: Department of Electrical Engineering, Superior Polytechnic School of Linares, University of Jaén, 23700 Linares, Spain
Mohammed Alharbi: Electrical Engineering Department, College of Engineering, King Saud University, Riyadh 11421, Saudi Arabia
Francisco Jurado: Department of Electrical Engineering, Superior Polytechnic School of Linares, University of Jaén, 23700 Linares, Spain

Mathematics, 2023, vol. 11, issue 9, 1-31

Abstract: This paper establishes a study for an accurate parameter modeling method for lithium-ion batteries. A precise state space model generated from an equivalent electric circuit is used to carry out the proposed identification process, where parameter identification is a nonlinear optimization process problem. The African vultures optimization algorithm (AVOA) is utilized to solve this problem by simulating African vultures’ foraging and navigating habits. The AVOA is used to implement this strategy and improve the quality of the solutions. Four scenarios are considered to take the effect of loading, fading, and dynamic analyses. The fitness function is selected as the integral square error between the estimated and measured voltage in these scenarios. Numerical simulations were executed on a 2600 mAhr Panasonic Li-ion battery to demonstrate the effectiveness of the suggested parameter identification technique. The proposed AVOA was fulfilled with high accuracy, the least error, and high closeness with the experimental data compared with different optimization algorithms, such as the Nelder–Mead simplex algorithm, the quasi-Newton algorithm, the Runge Kutta optimizer, the genetic algorithm, the grey wolf optimizer, and the gorilla troops optimizer. The proposed AVOA achieves the lowest fitness function level of the scenarios studied compared with relative optimization algorithms.

Keywords: lithium-ion battery; battery management system; integral square error; state of charge; battery modeling; parameter estimation; African vultures optimizer (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2023
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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