Date of Award

Fall 2021

Publication Type


Degree Name



Electrical and Computer Engineering

First Advisor

B. Balasingam

Second Advisor

B. Shahrrava

Third Advisor

G. Rankin


Curve fitting, Electrochemical impedance spectroscopy (EIS), Equivalent circuit model, Least squares estimation, Li-ion batteries




Battery internal impedance measurement is of great importance for a battery management system, which is tasked with ensuring the safety, efficiency, and reliability of a battery pack. Electrochemical impedance spectroscopy (EIS) is an active method of estimating battery impedance parameters, where an excitation signal spanning a wide frequency spectrum is applied to the battery to measure its response. Even though the EIS approach is accurate and reliable, it is limited to laboratory experiments due to its dependence on high-precision measurement systems. In order to benefit from the EIS approach in real-time applications, e.g., the battery management system of an electric vehicle, the impedance parameter estimation approach needs to be robust enough to perform with low-cost (and hence low-precision) sensors that are prone to measurement uncertainties. This thesis presents an approach to estimate the impedance parameters of a Li-Ion battery pack in the presence of high levels of noise. The proposed algorithm consists of fast Fourier transform, feature extraction, curve fitting, and least-squares estimation. The proposed approach is demonstrated using a swept sine wave that had frequency in the range of 0.01 Hz to 10 kHz as excitation signals. The results of the proposed parameter estimation algorithm are compared to that of recent work for objective performance comparison. Results show that the proposed algorithm significantly outperforms the previous method under high measurement noise scenarios without requiring any significant increase in computational resources.