A Parameter Identification Method for Second-Order RC Equivalent Circuits Based on an Improved Snake Algorithm

As the core component of new energy systems, the performance of lithium-ion batteries directly impacts the efficiency and safety of the entire system. Battery parameter identification serves as the foundation for state estimation and remaining useful life prediction, while also playing a pivotal role in health management and fault early warning. Addressing the issues of parameter identification algorithms being susceptible to parameter settings and experiencing premature convergence during iterations, which hinder their ability to effectively escape local optimal solutions, this article introduces a method for identifying parameters of the second-order RC equivalent circuit of lithium batteries based on an improved snake optimization algorithm. This method aims to enhance the efficiency and accuracy of parameter identification for battery equivalent circuits.