Lithium-ion battery electro-thermal modelling and internal states co-estimation for electric vehicles

Saqli, Khadija; Bouchareb, Houda; M'Sirdi, Kouider; Bentaie, M. Oudghiri

doi:10.1016/j.est.2023.107072

Cited by 26 publications

(1 citation statement)

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“…A detailed battery model is required for model-based SOC estimation. This model can improve the simulation efficiency in addition to the SOC estimation [43]. The literature might separate equivalent circuit and electrochemical models [44].…”

Section: Literature Reviewmentioning

confidence: 99%

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

et al. 2023

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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.

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Section: Literature Reviewmentioning

confidence: 99%