A theory of distribution functions of relaxation times for the deconvolution of immittance data

Developing an Automated Tool for Quantitative Analysis of the Deconvoluted Electrochemical Impedance Response of a Solid Oxide Fuel Cell

Despite being commercially available, solid oxide fuel cell (SOFC) technology requires further study to understand its physicochemical processes for diagnostics, prognostics, and quality assurance purposes. Electrochemical impedance spectroscopy (EIS), a widely used characterization technique for SOFCs, is often accompanied by the distribution of relaxation times (DRT) as a method for deconvoluting the contribution of each physicochemical process from the aggregated impedance response spectra. While EIS yields valuable information for the operation of SOFCs, the quantitative analysis of the DRT and its shifts remains cumbersome. To address this issue, and to create a replicable benchmark for the assessment of DRT results, a custom tool was developed in MATLAB to numerically analyze the DRT spectra, identify the DRT peaks, and assess their deviation in terms of peak frequency and DRT amplitude from nominal operating conditions. The preliminary validation of the tool was carried out by applying the tool to an extensive experimental campaign on 23 SOFC button-sized samples from three production batches in which EIS measurements were performed in parametric operating conditions. It was concluded that the results of the automated analysis via the developed tool were in accordance with the qualitative analysis of previous studies. It is capable of providing adequate additional quantitative results in terms of DRT shifts for further analysis and provides the basis for better interoperability of DRT analyses between laboratories.

Exploring Transport Behavior in Hybrid Perovskites Solar Cells via Machine Learning Analysis of Environmental-Dependent Impedance Spectroscopy

Hybrid organic-inorganic perovskites are one of the promising candidates for the next-generation semiconductors due to their superlative optoelectronic properties. However, one of the limiting factors for potential applications is their chemical and structural instability in different environments. Herein, the stability of (FAPbI3 )0.85 (MAPbBr3 )0.15 perovskite solar cell is explored in different atmospheres using impedance spectroscopy. An equivalent circuit model and distribution of relaxation times (DRTs) are used to effectively analyze impedance spectra. DRT is further analyzed via machine learning workflow based on the non-negative matrix factorization of reconstructed relaxation time spectra. This exploration provides the interplay of charge transport dynamics and recombination processes under environment stimuli and illumination. The results reveal that in the dark, oxygen atmosphere induces an increased hole concentration with less ionic character while ionic motion is dominant under ambient air. Under 1 Sun illumination, the environment-dependent impedance responses show a more striking effect compared with dark conditions. In this case, the increased transport resistance observed under oxygen atmosphere in equivalent circuit analysis arises due to interruption of photogenerated hole carriers. The results not only shed light on elucidating transport mechanisms of perovskite solar cells in different environments but also offer an effective interpretation of impedance responses.

Determination of the Differential Capacity of Lithium-Ion Batteries by the Deconvolution of Electrochemical Impedance Spectra

Electrochemical impedance spectroscopy (EIS) is a powerful tool for investigating electrochemical systems, such as lithium-ion batteries or fuel cells, given its high frequency resolution. The distribution of relaxation times (DRT) method offers a model-free approach for a deeper understanding of EIS data. However, in lithium-ion batteries, the differential capacity caused by diffusion processes is non-negligible and cannot be decomposed by the DRT method, which limits the applicability of the DRT method to lithium-ion batteries. In this study, a joint estimation method with Tikhonov regularization is proposed to estimate the differential capacity and the DRT simultaneously. Moreover, the equivalence of the differential capacity and the incremental capacity is proven. Different types of commercial lithium-ion batteries are tested to validate the joint estimation method and to verify the equivalence. The differential capacity is shown to be a promising approach to the evaluation of the state-of-health (SOH) of lithium-ion batteries based on its equivalence with the incremental capacity.