A Multi-Stage Parameter Identification Method for Lithium-Ion Battery Electrochemical Models Based on Sensitivity Analysis and Physical Properties

Electrochemical models are widely used in battery design and optimization due to their capability to systematically characterize the internal physicochemical evolution of batteries. However, such models typically involve a large number of parameters with high-dimensional coupling and multi-timescale characteristics, which makes conventional parameter identification methods prone to ill-posed optimization or convergence to local optima. To address these challenges, this paper proposes a multi-stage parameter identification method for the pseudo-two-dimensional (P2D) model. First, a sensitivity analysis and parameter sensitivity ranking were conducted for the 31 uncertain parameters in the model. By incorporating physical properties and sensitivity intervals, parameter classification and dimensionality reduction are achieved. Subsequently, a staged identification framework is constructed based on the classification results, where tailored excitation conditions and optimization algorithms are assigned to different parameter subsets. This approach enhances parameter identifiability while preserving physical consistency, enabling high-precision identification of 19 key electrochemical parameters. Finally, the proposed method is validated through experiments and simulations under real dynamic grid operating conditions. The results demonstrate that the proposed method achieves relatively high accuracy and applicability, with the root mean square error of terminal voltage maintained within 12 mV under dynamic conditions.