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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 \(R_0\) , \(R_1\) , 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.

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

  • 会议论文

  • 发表日期：

    2026年

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  • Jesús Gabriel Arroyo Pecchi,
  • Julio César Tafur-Sotelo,
  • Damián Eleazar Sal y Rosas Celi

摘要

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 \(R_0\) , \(R_1\) , 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.