A Comprehensive Review of Lithium-Ion Batteries Modeling, and State of Health and Remaining Useful Lifetime Prediction
Author:
Affiliation:
1. Department of Electrical and Computer Engineering, Tennessee Technological University, Cookeville, TN, USA
2. Department of Electrical Engineering, Qatar University, Doha, Qatar
Funder
Center for Energy Systems Research (CESR), Tennessee Technological University, Cookeville, TN, USA
Publisher
Institute of Electrical and Electronics Engineers (IEEE)
Subject
General Engineering,General Materials Science,General Computer Science,Electrical and Electronic Engineering
Link
http://xplorestaging.ieee.org/ielx7/6287639/9668973/09944663.pdf?arnumber=9944663
Reference184 articles.
1. A Directed Acyclic Graph Network Combined With CNN and LSTM for Remaining Useful Life Prediction
2. Dynamic Bayesian Network-Based Lithium-Ion Battery Health Prognosis for Electric Vehicles
3. Complementary Cooperation Algorithm Based on DEKF Combined With Pattern Recognition for SOC/Capacity Estimation and SOH Prediction
4. A Data-Driven Approach With Uncertainty Quantification for Predicting Future Capacities and Remaining Useful Life of Lithium-ion Battery
5. Predicting battery end of life from solar off-grid system field data using machine learning
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