Reliability of AI in Predicting the State of Health of Li-Ion Batteries*
Author:
Affiliation:
1. DEI, University of Bologna,Bologna,Italy
2. Beghelli Group,Bologna,Italy
Publisher
IEEE
Link
http://xplorestaging.ieee.org/ielx8/10616000/10616049/10616089.pdf?arnumber=10616089
Reference26 articles.
1. Prognostics in Battery Health Management
2. End-of-discharge and End-of-life Prediction in Lithium-ion Batteries with Electrochemistry-based Aging Models
3. Extended Kalman filtering for battery management systems of LiPB-based HEV battery packs
4. Battery remaining useful life prediction at different discharge rates
5. An On-Line State of Health Estimation of Lithium-Ion Battery Using Unscented Particle Filter
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