Advancing Lithium-Ion Battery Health Prognostics With Deep Learning: A Review and Case Study
Author:
Affiliation:
1. Department of Electrical and Computer Engineering, Texas A&M University, College Station, TX, USA
2. Department of Electrical and Computer Engineering, Texas A&M University at Qatar, Doha, Qatar
Funder
Qatar National Research Fund
Publisher
Institute of Electrical and Electronics Engineers (IEEE)
Link
http://xplorestaging.ieee.org/ielx7/8782707/10402037/10400849.pdf?arnumber=10400849
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1. Influence of Control in Cycling Degradation When Batteries Perform PV Ramp-Rate Control
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3. A Novel Model-Based Voltage Construction Method for Robust State-of-Health Estimation of Lithium-Ion Batteries
4. Electrochemical modeling and parameterization towards control-oriented management of lithium-ion batteries
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