Regensburg 2022 – wissenschaftliches Programm
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DY: Fachverband Dynamik und Statistische Physik
DY 8: Data Analytics for Complex Systems (joint session DY/SOE)
DY 8.2: Vortrag
Montag, 5. September 2022, 15:15–15:30, H18
Extending the limits of Electrochemical Impedance Spectroscopy with Machine Learning and Digital Twins — •Limei Jin1,2, Franz P. Bereck2, Christian H. Bartsch2, Josef Granwehr2, Rüdiger-A. Eichel2, Karsten Reuter1, and Christoph Scheurer1 — 1Fritz-Haber-Institut der MPG, Berlin, Germany — 2IEK-9, Forschungszentrum Jülich, Jülich, Germany
Electrochemical impedance spectroscopy (EIS) is widely used to characterize electrochemical energy conversion systems. The traditional analysis with equivalent circuit models (ECM) has recently been augmented by a transform based distribution of relaxation times (DRT) analysis which allows one to reduce the ambiguity in the construction of ECMs and thus overfitting. Yet, DRT, just like most traditional analyses, is firmly based in the linear response regime as well as based on frequency sweeps on a logarithmic scale. The latter makes these approaches time-consuming, the first limits their scope severely. To develop novel experimental spectroscopic excitation schemes that address these limitations, a model space of sufficiently realistic systems is required that substitutes for time-consuming measurements in terms of a digital twin. We present a joint experimental and theoretical approach for the construction of such a target space for the case of battery cell performance and ageing behaviour.