Electrochemical Impedance Spectroscopy (EIS) and Machine Learning based Battery State of Health (SoH) Estimation

Tonima, Masuda Akter; DeHart, Austin; Tabakci, Deniz; Tisapramotkul, Piramon; Munro-West, Andrew; Mehra, Aarushi; Shoa, Tina

doi:10.1109/icphm57936.2023.10194065

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2024

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Cited by 2 publications

References 38 publications

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Lithium Ion Batteries Impedance Approximation and Model Parameterization Using Artificial Neural Networks

Pohlmann,

Mashayekh,

Karnehm

et al. 2024

2024 IEEE International Conference on Industrial Technology (ICIT)

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Lithium Ion Batteries Impedance Approximation and Model Parameterization Using Artificial Neural Networks

Pohlmann,

Mashayekh,

Karnehm

et al. 2024

2024 IEEE International Conference on Industrial Technology (ICIT)

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Impact of Electrochemical Impedance Spectroscopy Dataset Curation on Solid Oxide Cell Degradation Assessment

Mänken,

Uecker,

Schäfer

et al. 2024

J. Electrochem. Soc.

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Electrochemical impedance spectroscopy (EIS) has become a standard measurement technique for detecting degradation in single cells and stacks of solid oxide cells (SOCs). Depending on the experimental setup and test equipment, instabilities and unexpected results can be observed in EIS measurements. For example, in the low-frequency range, instabilities can be induced by feed gas flow fluctuations. Another phenomenon are parasitic, inductive impedances that degrade the high-frequency range. To compensate for such influences in large EIS data sets, we propose a specially developed EIS data curation pipeline. Based on the results of its application, we demonstrate the impact on the quantitative and qualitative attribution of electrochemical processes from EIS using equivalent circuit modeling and distribution of relaxation times. Furthermore, the substantial differences in the temporal evolution of the latter during long-term experiments are highlighted for EIS measurements obtained at the SOC stack and single cell level. In addition, the significant misestimation of aging rates, especially with respect to the fuel electrode and the high-frequency series resistance, is shown when comparing EIS measurements, few of which exhibit a parasitic inductive impedance.

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Electrochemical Impedance Spectroscopy (EIS) and Machine Learning based Battery State of Health (SoH) Estimation

Cited by 2 publications

References 38 publications

Lithium Ion Batteries Impedance Approximation and Model Parameterization Using Artificial Neural Networks

Lithium Ion Batteries Impedance Approximation and Model Parameterization Using Artificial Neural Networks

Impact of Electrochemical Impedance Spectroscopy Dataset Curation on Solid Oxide Cell Degradation Assessment

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