How the distribution of relaxation times enhances complex equivalent circuit models for fuel cells

• Published in: Electrochimica acta Vol. 355; p. 136764
• Main Authors: Dierickx, Sebastian, Weber, André, Ivers-Tiffée, Ellen
• Format: Journal Article
• Language: English
• Published: Oxford Elsevier Ltd 20-09-2020; Elsevier BV
• Subjects: Charge transfer; Complexity; Distribution of relaxation times; Electrochemical impedance spectroscopy; Electrodes; Equivalent circuit modelling; Equivalent circuits; Fuel cells; PEMFC; Regularization; SOFC; Spectrum analysis; Equivalent circuit modelling; PEMFC; Distribution of relaxation times; Electrochemical impedance spectroscopy; SOFC
• ISSN: 0013-4686; 1873-3859
• DOI: 10.1016/j.electacta.2020.136764

Distribution of relaxation times (DRT) is a well-established method for deconvoluting electrochemical impedance spectroscopy (EIS) data from fuel cells. DRT-analysis provides a deeper insight into electrode reactions and supports identification of the most accurate equivalent circuit models. This established method has undergone much change in recent decades and has been applied to many sub-fields. Contemporary studies are highly specialized and produce specialist literature: to further a comprehensive view, this paper provides an overview and retrospective. EIS measurement and subsequent interpretation by DRT are challenging because (i) high-performance electrodes have very low impedances (10–100 mΩ), (ii) the concept of one rate-limiting step is usually not applicable, and (iii) the charge transfer and transport processes in anode and cathode are often coupled; overlapping in the frequency domain. In this paper selected results from advanced EIS and DRT analyses are discussed. We demonstrate the importance of EIS data quality, introduce the use of the Kramers-Kronig transformation, explain the impacts of statistically distributed noise and single errors in EIS spectra, and the selection of the regularization parameter lambda for improved interpretation of DRT curves. Finally, well-selected examples of DRT approaches lead to adequate models with different complexity. As a result, this paper deepens the understanding of how to assess electrochemical measurements of fuel cells based on the DRT. It, therefore, represents a vital guide for DRT analysis.
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