Hybrid battery model parameter estimation and optimization using a two-step procedure and parameter sensitivity analysis

This paper presents an effective and simple approach for parameter estimation and optimization of a simplified first principle (SFP) model of lithium-ion batteries (LIBs). Unlike existing parameter estimation and optimization methods, this proposed approach first decouples and estimates some key parameters based on theoretical analysis and experimental data. In order to alleviate the influence of the previous parameter estimation steps on the subsequent ones, and reduce parameter optimization cost, it conducts parameter sensitivity analysis with SFP model to study the parameter identifiability, and sets parameters with low identifiability to fix values. Then it utilizes particle swarm optimization (PSO) algorithm to optimize parameters with high identifiability. The proposed approach is validated with experimental data under a dynamic loading profile. Experimental results demonstrate that the proposed approach using a two-step procedure can effectively and accurately identify parameters of SFP model to simulate dynamic behaviors of LIBs, providing a reliable foundation for the development of model-based battery management systems.