A Bayesian view on the Hilbert transform and the Kramers-Kronig transform of electrochemical impedance data: Probabilistic estimates and quality scores
By Jiapeng Liu, T. Wan, F. Ciucci. Electrochimica Acta, 2020
123 citations recorded by the source corpus.
Electrochemical impedance spectroscopy (EIS) is one of the most widely used experimental tools in electrochemistry and has applications ranging from energy storage and power generation to medicine. Considering the broad applicability of the EIS technique, it is critical to validate the EIS data against the Hilbert transform (HT) or, equivalently, the Kramers–Kronig relations. These mathematical relations allow one to assess the self-consistency of obtained spectra. However, the use of validation tests is still uncommon. In the present article, we aim at bridging this gap by reformulating the HT under a Bayesian framework. In particular, we developed the Bayesian Hilbert transform (BHT) method that interprets the HT probabilistic. Leveraging the BHT, we proposed several scores that provide quick metrics for the evaluation of the EIS data quality.
Subfields: Control and Systems Engineering
View Paper: https://www.semanticscholar.org/paper/3b588d985988ebded8b71faba04886fbb9eac7d8
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In Control and Systems Engineering
Citations in the corpus
- Battery digital twins: Perspectives on the fusion of models, data and artificial intelligence for smart battery management systems (2020, background): “Towards this end, Liu et al. [60] reformulated some of the core EIS criteria under a Bayesian framework and subsequently create metrics to assess how well the data fits to the constraints of its derivation, namely linearity, time-invariance and causality.”