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| Home > Publications database > Uncertainty weighted distribution of relaxation time analysis of battery impedance spectra using Gaussian process regression for noise estimation |
| Home > Publications database > Uncertainty weighted distribution of relaxation time analysis of battery impedance spectra using Gaussian process regression for noise estimation |
| Poster (Other) | FZJ-2024-00525 |
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2023
Abstract: A common technique for characterizing electrochemical systems is the combination of electrochemical impedance spectroscopy (EIS) and equivalent circuit modeling (ECM). However, choosing a suitable electrical circuit usually requires a-priori knowledge of the investigated system. By combining ECM with a distribution of relaxation times (DRT) analysis, relevant features found in the impedance data can be distinguished more clearly and, ideally, their number can be determined.Since the data acquired by EIS shows heteroscedastic noise behavior, using uniform weighting in the DRT can result in either under- or overregularization. To account for that, two methods for noise estimation are compared: statistical noise characterization by obtaining multiple EIS spectra of a commercial Lithium-ion coin cell at the same State of Charge (SoC) and Gaussian process regression (GPR) using only a single data set.
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