Early prediction of Li-ion cell failure from EIS derived from current–voltage time series
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 LiNi _x Mn _y Co $ _{1-x-y}...
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| Main Authors: | , , , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
IOP Publishing
2025-01-01
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| Series: | JPhys Energy |
| Subjects: | |
| Online Access: | https://doi.org/10.1088/2515-7655/ad97df |
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| Summary: | 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 LiNi _x Mn _y Co $ _{1-x-y}$ O _2 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 µ Hz 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. |
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| ISSN: | 2515-7655 |