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

Abstract

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 previouslyaged 3.2 A h, 3.6 V 18650 INR LiNixMnyCo1-x-yO2 cell for 300 days 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 days 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.