An Efficient Approach to Precise Parameter Identification on Lithium Batteries Using EIS with Goertzel Algorithm and Adaptive Sampling

This work presents an efficient approach for identifying electrical parameters in lithium-ion battery cells using Electrochemical Impedance Spectroscopy (EIS) with adaptive sampling and the Goertzel algorithm. A first-order Thevenin model is identified from frequency-domain data; parameters R0, R1, and C are estimated by nonlinear least squares. The proposed pipeline preserves accuracy while drastically reducing computing time and memory, making it suitable for embedded and low-power systems.