A Comparison of Battery Equivalent Circuit Model Parameter Extraction Approaches Based on Electrochemical Impedance Spectroscopy

Abstract

This paper presents three approaches to estimating the battery parameters of the electrical equivalent circuit model (ECM) based on electrochemical impedance spectroscopy (EIS); these approaches are referred to as (a) least squares (LS), (b) exhaustive search (ES), and (c) nonlinear least squares (NLS). The ES approach is assisted by the LS method for the rough determination of the lower and upper bound of the ECM parameters, and the NLS approach is incorporated with the Monte Carlo run such that different initial guesses can be assigned to improve the goodness of EIS fitting. The proposed approaches are validated using both simulated and real EIS data. Compared to the LS approach, the ES and NLS approaches show better fitting accuracy at various noise levels, whereas in both the validation using simulated EIS data and actual EIS data collected from LG 18650 and Molicel 21700 batteries, the NLS approach shows better fitting accuracy than that of LS and ES approaches. In all cases, compared with the ES approach, the computational time of the NLS approach is significantly faster, and compared with the LS approach, the NLS approach shows a minimal difference in computational time and considerably better fitting performance.

Keywords

• Li-ion battery (LIB)
• battery parameter estimation
• battery management system
• electrochemical impedance spectroscopy (EIS)
• electrical equivalent circuit models (ECMs)
• exhaustive search (ES)