Robust Approach to Battery Equivalent-Circuit-Model Parameter Extraction Using Electrochemical Impedance Spectroscopy

Abstract

Electrochemical impedance spectroscopy (EIS) is a well-established method of battery analysis, where the response of a battery to either a voltage or current excitation signal spanning a wide frequency spectrum is measured and analyzed. State-of-the-art EIS analysis is limited to high-precision measurement systems within laboratory environments. In order to be relevant in practical applications, EIS analysis needs to be carried out with low-cost sensors, which suffer from high levels of measurement noise. This article presents an approach to estimate the equivalent circuit model (ECM) parameters of a Li-Ion battery pack based on EIS measurements in the presence of high levels of noise. The proposed algorithm consists of a fast Fourier transform, feature extraction, curve fitting, and least-squares estimation. The results of the proposed parameter-estimation algorithm are compared to that of recent work for objective performance comparison. The error analysis of the proposed approach, in comparison to the existing approach, demonstrated significant improvement in parameter estimation accuracy in low signal-to-noise ratio (SNR) regions. Results show that the proposed algorithm significantly outperforms the previous method under high-measurement-noise scenarios without requiring a significant increase in computational resources.

Keywords

• Li-ion batteries
• battery management system
• equivalent circuit model
• impedance
• electrochemical impedance spectroscopy (EIS)
• least squares estimation