CALCE – Knee Point Prediction and Anomaly Detection in the Capacity Fade of Lithium ion Batteries

When:
May 19, 2020 @ 8:00 am – 9:00 am
2020-05-19T08:00:00-07:00
2020-05-19T09:00:00-07:00
Where:
webinar

https://umd.webex.com/mw3300/mywebex/default.do?nomenu=true&siteurl=umd&service=6&rnd=0.16642910159686097&main_url=https%3A%2F%2Fumd.webex.com%2Fec3300%2Feventcenter%2Fevent%2FeventAction.do%3FtheAction%3Ddetail%26%26%26EMK%3D4832534b00000004a30082c1281de630a92b32b387446da7af6de4b72cd149eb80472bfb4610c373%26siteurl%3Dumd%26confViewID%3D157787840885825714%26encryptTicket%3DSDJTSwAAAASu8C7IDH2Spdaw9Q3tKNT917_LPrmc5bnbyyEwtfGejw2%26

By Weiping Diao

Lithium-ion batteries often exhibit a transition to a more rapid capacity fade trend (knee point) before hitting the 80% end-of-life threshold, which indicates the onset of a rapid deterioration of performance. Understanding the mechanisms that drive this phenomenon is valuable to battery manufacturers and device companies to mitigate or avoid the occurrence of knee points by improving battery design, manufacturing process, and management. This presentation will talk about the experimental plan and results from ongoing experiments. Modeling and predicting the two-stage nonlinear degradation trend under normal operating condition can shorten qualification testing time, provide guidance when scheduling battery replacements, and evaluate and plan secondary uses of batteries. An accelerated testing and modeling approach to predict the nonlinear degradation behavior will be discussed. The identification of the knee point is valuable to identify the more severe degradation trend, and can serve as metric to evaluate the reliability of lithium-ion batteries. This presentation will introduce the developed algorithm to identify the knee point.

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