Deconvolution of electrochemical impedance spectroscopy data using the deep-neural-network-enhanced distribution of relaxation times

Electrochemical impedance spectroscopy (EIS) is widely used to characterize electrochemical systems. The distribution of relaxation times (DRT) has emerged as a powerful, non-parametric alternative to circumvent the inherent challenges of EIS analysis through equivalent circuits or physical models. Recently, deep neural net-works have been developed to estimate the DRT. However, this line of research is still in its infancy, and several issues remain unresolved, including the long training time and unknown accuracy of this method. Furthermore, deep neural networks have not been used for deconvolving DRTs with negative peaks. This work addresses these challenges. A pretraining step is included to decrease the computation time; error analysis allows error esti-mation and the development of error reduction strategies. Furthermore, the training loss function is modified to handle DRTs with negative peaks. For most cases tested, this new framework outperforms ridge regression. Moreover, these advances are validated with an array of synthetic and real EIS spectra from various applications, including lithium-metal batteries, solid oxide fuel cells, and proton exchange membrane fuel cells. Overall, this research opens new avenues for the development and application of the deep-neural-network-based analysis of EIS data.

Automated summary

Electrochemical impedance spectroscopy (EIS) is widely used to analyze electrochemical systems. The distribution of relaxation times (DRT) has emerged as a powerful alternative to traditional EIS analysis methods. This study focuses on developing a deep neural network-based approach to estimate the DRT accurately and efficiently. The proposed framework outperforms ridge regression and is validated with various EIS spectra, showing great potential for the analysis of EIS data.