Detecting Abnormality of Battery Lifetime from First-Cycle Data Using Few-Shot Learning.

ABSTRACT: The service life of large battery packs can be significantly influenced by only one or two abnormal cells with faster aging rates. However, the early-stage identification of lifetime abnormality is challenging due to the low abnormal rate and imperceptible initial performance deviations. This work proposes a lifetime abnormality detection method for batteries based on few-shot learning and using only the first-cycle aging data. Verified with the largest known dataset with 215 commercial lithium-ion batteries, the method can identify all abnormal batteries, with a false alarm rate of only 3.8%. It is also found that any capacity and resistance-based approach can easily fail to screen out a large proportion of the abnormal batteries, which should be given enough attention. This work highligh

SUBMITTER: Tang X 

PROVIDER: S-EPMC10853708 | biostudies-literature | 2024 Feb

REPOSITORIES: biostudies-literature