Early prediction of Li-ion cell failure from EIS derived from current-voltage time series

Wilson, Marcus T and Farrow, Vance and Dunn, Christopher J and Cowie, Logan and Cree, Michael J and Bjerkan, Juliane and Stefanovska, Aneta and Scott, Jonathan B (2025) Early prediction of Li-ion cell failure from EIS derived from current-voltage time series. JPhys Energy, 7 (2): 025001. ISSN 2515-7655

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Abstract

The ability to reliably detect the forthcoming failure of a rechargeable cell without removing it from its normal operating environment remains a significant goal in battery research. In this work we have cycled in the laboratory a previously-aged 3.2 A h, 3.6 V 18650 INR LiNixMnyCo 1−x−yO2 cell for 300 d until failure was apparent, using a current waveform representative of use in an electric vehicle application. Electrochemical impedance spectroscopy (EIS) down to 5 µHz was also performed on the cell as a ‘gold-standard’ measure, at the beginning, end and part way through the cycling. Analysis of voltage and current time series data using both parametric (equivalent circuit model) and non-parametric (wavelet-based analysis) approaches allowed us to successfully reconstruct the EIS data. As the battery aged, impedance gradually increased at frequencies between 10−4 Hz—10−1 Hz. The increase accelerated around 50 d before the battery ultimately failed. The acceleration in rate of change of impedance was detectable while the cycle efficiency remained high, indicating that a user of the cell would be unlikely to detect any change in the cell based on its performance or by common measures of state-of-health. The results imply upcoming failure may be detectable from time series analysis weeks before any noticeable drop in cell performance.

Item Type:
Journal Article
Journal or Publication Title:
JPhys Energy
ID Code:
226206
Deposited By:
Deposited On:
06 Dec 2024 11:10
Refereed?:
Yes
Published?:
Published
Last Modified:
27 Jan 2025 02:47