iTransformer Network Based Approach for Accurate Remaining Useful Life Prediction in Lithium-Ion Batteries
Author:
Affiliation:
1. McMaster University,McMaster Automotive Resource Centre (MARC),Hamilton,ON,Canada,L8P0A6
Publisher
IEEE
Link
http://xplorestaging.ieee.org/ielx8/10598816/10598836/10598898.pdf?arnumber=10598898
Reference22 articles.
1. Dynamic battery remaining useful life estimation: An on-line data-driven approach
2. The state of understanding of the lithium-ion-battery graphite solid electrolyte interphase (SEI) and its relationship to formation cycling
3. Battery Lifetime Prognostics
4. An online dual filters RUL prediction method of lithium-ion battery based on unscented particle filter and least squares support vector machine
5. State-of-Health Monitoring and Prediction of Lithium-Ion Battery Using Probabilistic Indication and State-Space Model
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