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Article

Method for In-Operando Contamination of Lithium Ion Batteries for Prediction of Impurity-Induced Non-Obvious Cell Damage

Vehicle Safety Institute, Graz University of Technology, Inffeldgasse 23/1, 8010 Graz, Austria
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Author to whom correspondence should be addressed.
Academic Editor: Carlos Ziebert
Batteries 2022, 8(4), 35; https://doi.org/10.3390/batteries8040035
Received: 2 March 2022 / Revised: 7 April 2022 / Accepted: 11 April 2022 / Published: 14 April 2022
(This article belongs to the Special Issue Lithium-Ion Batteries Aging Mechanisms II)
The safety of lithium-ion batteries within electrified vehicles plays an important role. Hazards can arise from contaminated batteries resulting from non-obvious damages or insufficient production processes. A systematic examination requires experimental methods to provoke a defined contamination. Two prerequisites were required: First, the extent and type of contamination should be determinable to exclude randomness. Second, specimens should work properly before the contamination, enabling realistic behavior. In this study, two experimental methods were developed to allow for the first time a controlled and reproducible application of water or oxygen into 11 single-layer full cells (Li4Ti5O12/LiCoO2) used as specimens during electrical cycling. Electrochemical impedance spectroscopy was used to continuously monitor the specimens and to fit the parameters of an equivalent circuit model (ECM). For the first time, these parameters were used to calibrate a machine-learning algorithm which was able to predict the contamination state. A decision tree was calibrated with the ECM parameters of eight specimens (training data) and was validated by predicting the contamination state of the three remaining specimens (test data). The prediction quality proved the usability of classification algorithms to monitor for contaminations or non-obvious battery damage after manufacturing and during use. It can be an integral part of battery management systems that increases vehicle safety. View Full-Text
Keywords: lithium-ion battery; safety; in operando; contamination; electrochemical impedance spectroscopy; equivalent circuit model; machine learning; classification algorithm; decision tree lithium-ion battery; safety; in operando; contamination; electrochemical impedance spectroscopy; equivalent circuit model; machine learning; classification algorithm; decision tree
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MDPI and ACS Style

Höschele, P.; Heindl, S.F.; Schneider, B.; Sinz, W.; Ellersdorfer, C. Method for In-Operando Contamination of Lithium Ion Batteries for Prediction of Impurity-Induced Non-Obvious Cell Damage. Batteries 2022, 8, 35. https://doi.org/10.3390/batteries8040035

AMA Style

Höschele P, Heindl SF, Schneider B, Sinz W, Ellersdorfer C. Method for In-Operando Contamination of Lithium Ion Batteries for Prediction of Impurity-Induced Non-Obvious Cell Damage. Batteries. 2022; 8(4):35. https://doi.org/10.3390/batteries8040035

Chicago/Turabian Style

Höschele, Patrick, Simon Franz Heindl, Bernd Schneider, Wolfgang Sinz, and Christian Ellersdorfer. 2022. "Method for In-Operando Contamination of Lithium Ion Batteries for Prediction of Impurity-Induced Non-Obvious Cell Damage" Batteries 8, no. 4: 35. https://doi.org/10.3390/batteries8040035

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