Research on Parameter Identification of Lithiumion Batteries Based on Improved SCE Algorithm

Abstract

Evaluating the charging status of power batteries is very important in battery management systems, and the accuracy and parameter identification of battery models are crucial for it. Using DST and FUDS lithium-ion battery dynamic mode datasets for simulation verification, and comparing with particle swarm optimization algorithm, grey wolf algorithm, and genetic algorithm. The simulation results show that this method has advantages in recognition accuracy, with an average quadratic error of 0.0166V for parameter recognition. Compared with other optimization algorithms, it decreased by 7.8%, 8.3%, and 14.9% respectively.