SKM 2023 – wissenschaftliches Programm

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DY: Fachverband Dynamik und Statistische Physik

DY 46: Poster: Machine Learning and Data Analytics

DY 46.2: Poster

Donnerstag, 30. März 2023, 13:00–16:00, P1

Battery modeling: Fusing equivalent circuit models with data-driven surrogate modelling — •Limei Jin1,2, Franz P. Bereck2, Josef Granwehr2, Rüdiger-A. Eichel2, Karsten Reuter1, and Christoph Scheurer11Fritz-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. Experimentally determined ECM parameters vary depending on operating conditions and the lifetime history of battery usage. Here we focus on State of Health (SOH) and State of Charge (SOC) as a basis for operando diagnosis and functionality optimization in the setting of fast-charging. Within pure ECM approaches, aging effects can only be represented to a limited extent, as aging is related to a variety of different factors whose impact on cell impedance are not sufficiently understood, yet. The highly complex interplay of factors motivates the development of data-driven Machine Learning (ML) models as a basis for future battery management systems. We present ML enabled ECMs based on experimental impedance analyses and a data-driven ML approach that computationally samples an abstract target space for classification and recognition of cells at vastly different SOC/SOH combinations.

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