IEEE Transactions on Industrial Electronics, February 2020, DOI:10.1109/TIE.2020.2972468

Capacity-fading Behavior Analysis for Early Detection of Unhealthy Li-ion Batteries

Changyong Lee 1, Sugyeong Jo 1, Daeil Kwon 2, and Michael G. Pecht 3
1 Ulsan National Institute of Science and Technology, 131639 Ulsan, Ulsan Korea (the Republic of)
2 Sungkyunkwan University, 35017 Suwon, Gyunggi-do Korea (the Republic of)
3 CALCE, Center for Advanced Life Cycle Engineering, Department of Mechanical Engineering, University of Maryland, College Park, Maryland 20740, USA


Reliability testing on Li-ion batteries is critical to designing operational back-end strategies for developing portable electronics. This paper develops a capacity-fading behavior analysis for the early detection of unhealthy Li-ion batteries during reliability tests by comparing against the capacity-fading behaviors of healthy batteries from qualification. The developed approach uses a local outlier factor for measuring the anomaly scores of the capacity-fading behaviors of test batteries at a certain cycle, kernel density estimation for normalizing the range of anomaly scores over cycles, and a hidden Markov model for estimating the probability that the test batteries are at a certain state (i.e., healthy or unhealthy). Experimental results on Li-ion batteries used for portable consumer electronics confirm that the developed method outperforms previous approaches, reducing the required number of reliability tests for unhealthy batteries to 100 cycles, less than a month in practice.

This article is available online here and to CALCE Consortium Members for personal review.

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