4.8 Article

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

Journal

ADVANCED SCIENCE
Volume -, Issue -, Pages -

Publisher

WILEY
DOI: 10.1002/advs.202305315

Keywords

big data; early-stage detection; few-shot learning; lifetime abnormality; lithium-ion battery

Ask authors/readers for more resources

This study proposes a few-shot learning-based method for detecting battery lifetime abnormalities, which can identify all abnormal batteries using only the first-cycle aging data with a low false alarm rate.
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 highlights the opportunities to diagnose lifetime abnormalities via big data analysis, without requiring additional experimental effort or battery sensors, thereby leading to extended battery life, increased cost-benefit, and improved environmental friendliness.

Authors

I am an author on this paper
Click your name to claim this paper and add it to your profile.

Reviews

Primary Rating

4.8
Not enough ratings

Secondary Ratings

Novelty
-
Significance
-
Scientific rigor
-
Rate this paper

Recommended

No Data Available
No Data Available