Employment of relaxation times distribution with improved elastic net regularization for advanced impedance data analysis of a lithium-ion battery

Electrochemical impedance spectroscopy (EIS) is a familiar conventional approach that has been widely applied to analyze electrochemical systems, such as batteries and fuel cells, to determine the polarization resistances of their electrodes. An improved method that can effectively interpret EIS spectra with high resolution and provide close knowledge of the time features of the electrochemical system being considered is the distribution of relaxation times (DRT). However, estimating and attaining DRT is a challenging issue that involves solutions being obtained by employing regularization techniques. This study proposed an improved elastic net (IEN) regularization with an adaptive elastic net penalty, wherein adaptive weight matrices were incorporated into the elastic net penalty. The proposed technique was first validated on standard artificial experimental elements: RC circuit, fractal-RC (FRC) circuit, ZARC element, and Gerischer element, each with a known analytical DRT. The results showed that the proposed method exhibited better estimation accuracy in obtaining the exact known DRTs and resistances, and lower mean square errors (MSEs) when compared with the conventional elastic net (EN) regularization method. Furthermore, the proposed method was applied to the EIS data of real lithium-ion batteries with different state-of-charge (SOC) and temperatures, where the obtained DRTs provided an intuitive analysis of the processes within the battery. The proposed model can accurately estimate various time characterizations and identify their processes. Besides the DRTtool results are correspondingly similar to this study's results showing the effectiveness of the proposed approach when compared. However, the limitations and weaknesses of the proposed approach were recognized and reported in this study. Moreover, the proposed approach can be further extended, improved, and employed for advanced EIS techniques and multidimensional EIS data analyses.