Predicting the heat release variability of Li-ion cells under thermal runaway with few or no calorimetry data

Karina Masalkovaitė, Paul Gasper () and Donal P. Finegan ()
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Karina Masalkovaitė: Stanford University
Paul Gasper: National Renewable Energy Laboratory (NREL)
Donal P. Finegan: National Renewable Energy Laboratory (NREL)

Nature Communications, 2024, vol. 15, issue 1, 1-11

Abstract: Abstract Accurate measurement of the variability of thermal runaway behavior of lithium-ion cells is critical for designing safe battery systems. However, experimentally determining such variability is challenging, expensive, and time-consuming. Here, we utilize a transfer learning approach to accurately estimate the variability of heat output during thermal runaway using only ejected mass measurements and cell metadata, leveraging 139 calorimetry measurements on commercial lithium-ion cells available from the open-access Battery Failure Databank. We show that the distribution of heat output, including outliers, can be predicted accurately and with high confidence for new cell types using just 0 to 5 calorimetry measurements by leveraging behaviors learned from the Battery Failure Databank. Fractional heat ejection from the positive vent, cell body, and negative vent are also accurately predicted. We demonstrate that by using low cost and fast measurements, we can predict the variability in thermal behaviors of cells, thus accelerating critical safety characterization efforts.

Date: 2024
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DOI: 10.1038/s41467-024-52653-3

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