Date of Award


Publication Type


Degree Name



Electrical and Computer Engineering


Battery EIS;Equivalent Circuit Model;Exhaustive Search;Li-ion battery;Nonlinear Least Squares;Parameters Estimation


Balakumar Balasingam



Creative Commons License

Creative Commons Attribution 4.0 International License
This work is licensed under a Creative Commons Attribution 4.0 International License.


This thesis compares three methods for estimating battery parameters of the electrical equivalent circuit model (ECM) based on electrochemical impedance spectroscopy (EIS). These methods are referred to as least squares (LS), exhaustive search (ES), and nonlinear least squares (NLS). The ES approach utilizes the LS method to roughly determine the lower and upper bounds of the ECM parameters, while the NLS approach incorporates a Monte Carlo run, allowing for different initial guesses to enhance the accuracy of EIS fitting. The proposed approaches are validated using both simulated and real-world EIS data. When the signal-to-noise ratio (SNR) is high, both the ES and NLS approaches exhibit better fitting accuracy compared to the LS approach. Furthermore, in the validation using simulated EIS data as well as actual EIS data obtained from LG 18650 and Molicel 21700 batteries, the NLS approach consistently outperforms the LS and ES approaches in terms of fitting accuracy. Additionally, the computational time required for the NLS approach is significantly shorter than that of the ES approach, and the NLS approach demonstrates only a minimal difference in computational time compared to the LS approach while providing significantly better fitting performance.

Available for download on Friday, August 16, 2024