SOH Degradation Modeling of Thin Flexible Li-ion Batteries Subjected to Life Cycling with Randomized Cycling Depth, C-rates, and U-Flex-to-Install
Author:
Affiliation:
1. Auburn University,NSF-CAVE3 Electronics Research Center,Department of Mechanical Engineering,Auburn,AL
Publisher
IEEE
Link
http://xplorestaging.ieee.org/ielx7/10177286/10177494/10177643.pdf?arnumber=10177643
Reference25 articles.
1. Robust State of Health estimation of lithium-ion batteries using convolutional neural network and random forest
2. State of health prediction for lithium‐ion batteries with a novel online sequential extreme learning machine method
3. Thin flexible lithium-ion battery featuring graphite paper based current collectors with enhanced conductivity
4. Bending impact on the performance of a flexible Li4Ti5O12-based all-solid-state thin-film battery
5. State-of-Health Estimation of Li-ion Batteries in Electric Vehicle Using IndRNN under Variable Load Condition
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