Parameter identification of lithium-ion batteries: a comparative study of various models and optimization techniques for battery modeling

Abstract

This work presents a comparative study of optimization techniques for parameter identification in equivalent electrical models of lithium-ion batteries. The 2RC model is applied to a set of twelve batteries using four publicly available datasets obtained from well-established research institutions. The methodology is structured in four stages: first, the 2RC model is selected due to its balance between physical interpretability and computational simplicity; second, experimental charge-discharge cycle data are collected; third, various optimization techniques are applied with the aim of minimizing the error between the experimental data and the response estimated by the model; and finally, accuracy is evaluated using the mean squared error, while computational efficiency is assessed through execution time. Traditional, metaheuristic, and bio-inspired optimization methods are considered, including least squares optimization, particle swarm optimization, simulated annealing, and several nature-inspired variants. It is demonstrated that bio-inspired techniques achieve greater accuracy than traditional methods, without a significant increase in computational cost. In particular, particle swarm optimization shows superior performance in terms of precision and robustness against local minima. It is concluded that the integration of advanced optimization strategies significantly enhances the fidelity of equivalent electrical models, which is essential for accurate estimation of internal states such as state of charge, aging, and service life in lithium-ion batteries used in electric vehicles and aerospace systems.