Prediction of Health Level of Multiform Lithium Sulfur Batteries Based on Incremental Capacity Analysis and an Improved LSTM
Author:
Affiliation:
1.
2. School of Electrical Engineering, Qingdao University,Qingdao,China,266000
Funder
Zhejiang Province Natural Science Foundation
National Natural Science Foundation of China
Publisher
Institute of Electrical and Electronics Engineers (IEEE)
Link
http://xplorestaging.ieee.org/ielx7/10352418/10462547/10462552.pdf?arnumber=10462552
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1. A combined robust approach based on auto‐regressive long short‐term memory network and moving horizon estimation for state‐of‐charge estimation of lithium‐ion batteries
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4. Facile large-scale synthesis of core–shell structured sulfur@polypyrrole composite and its application in lithium–sulfur batteries with high energy density
5. A Data-Driven Approach With Uncertainty Quantification for Predicting Future Capacities and Remaining Useful Life of Lithium-ion Battery
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