Bayesian Parameterization of Continuum Battery Models from Featurized Electrochemical Measurements Considering Noise **

Abstract: Physico‐chemical continuum battery models are typically parameterized by manual fits, relying on the individual expertise of researchers. In this article, we introduce a computer algorithm that directly utilizes the experience of battery researchers to extract information from experimental data reproducibly. We extend Bayesian Optimization (BOLFI) with Expectation Propagation (EP) to create a black‐box optimizer suited for modular continuum battery models. Standard approaches compare the experimental data in its raw entirety to the model simulations. By dividing the data into physics‐based features, our data‐driven approach uses orders of magnitude less simulations. For validation, we process full‐cell GITT measurements to characterize the diffusivities of both electrodes non‐destructively. Our algorithm enables experimentators and theoreticians to investigate, verify, and record their insights. We intend this algorithm to be a tool for the accessible evaluation of experimental databases.

Location
Deutsche Nationalbibliothek Frankfurt am Main
Extent
Online-Ressource
Language
Englisch

Bibliographic citation
Bayesian Parameterization of Continuum Battery Models from Featurized Electrochemical Measurements Considering Noise ** ; day:21 ; month:11 ; year:2022 ; extent:1
Batteries & supercaps ; (21.11.2022) (gesamt 1)

Creator
Kuhn, Yannick
Wolf, Hannes
Latz, Arnulf
Horstmann, Birger

DOI
10.1002/batt.202200374
URN
urn:nbn:de:101:1-2022112114282738748346
Rights
Open Access; Der Zugriff auf das Objekt ist unbeschränkt möglich.
Last update
15.08.2025, 7:34 AM CEST

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Associated

  • Kuhn, Yannick
  • Wolf, Hannes
  • Latz, Arnulf
  • Horstmann, Birger

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