A Novel Method of Parameter Identification for Lithium-Ion Batteries Based on Elite Opposition-Based Learning Snake Optimization

This paper shows that lithium-ion battery model parameters are vital for state-of-health assessment and performance optimization. Traditional evolutionary algorithms often fail to balance global and local search. To address these challenges, this study proposes the Elite Opposition-Based Learning Snake Optimization (EOLSO) algorithm, which uses an elite opposition-based learning mechanism to enhance diversity and a non-monotonic temperature factor to balance exploration and exploitation. The algorithm is applied to the parameter identification of the second-order RC equivalent circuit model. EOLSO outperforms some traditional optimization methods, including the Gray Wolf Optimizer (GWO), Honey Badger Algorithm (HBA), Golden Jackal Optimizer (GJO), Enhanced Snake Optimizer (ESO), and Snake Optimizer (SO), in both standard functions and HPPC experiments. The experimental results demonstrate that EOLSO significantly outperforms the SO, achieving reductions of 43.83% in the Sum of Squares Error (SSE), 30.73% in the Mean Absolute Error (MAE), and 25.05% in the Root Mean Square Error (RMSE). These findings position EOLSO as a promising tool for lithium-ion battery modeling and state estimation. It also shows potential applications in battery management systems, electric vehicle energy management, and other complex optimization problems. The code of EOLSO is available on GitHub.