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

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.

Automated summary

This paper presents a study on an accurate parameter modeling method for lithium-ion batteries. A precise state space model generated from an equivalent electric circuit is used for parameter identification, which is a nonlinear optimization problem. The African vultures optimization algorithm (AVOA) is employed to solve this problem by mimicking the foraging and navigating habits of African vultures. Four scenarios are considered to investigate the effect of loading, fading, and dynamic analyses. Numerical simulations on a 2600 mAhr Panasonic Li-ion battery demonstrate the effectiveness of the proposed parameter identification technique. The AVOA outperforms other optimization algorithms in terms of accuracy, error minimization, and similarity with experimental data.