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

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. Fractio

SUBMITTER: Masalkovaite K 

PROVIDER: S-EPMC11437010 | biostudies-literature | 2024 Sep

REPOSITORIES: biostudies-literature