Online state‐of‐health prediction of lithium‐ion batteries with limited labeled data-Reference-Cited by-同舟云学术

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Online state‐of‐health prediction of lithium‐ion batteries with limited labeled data

Published:2020-08-18 Issue:14 Volume:44 Page:11345-11360
ISSN:0363-907X
Container-title:International Journal of Energy Research
language:en
Short-container-title:Int J Energy Res

Author:

Yu Jinsong¹^ORCID,Yang Jie¹^ORCID,Wu Yao¹,Tang Diyin¹^ORCID,Dai Jing²

Affiliation:

1. School of Automation Science and Electrical Engineering Beihang University Beijing China

2. China Academy of Launch Vehicle Technology R&D Center Beijing China

Funder

National Basic Research Program of China

National Natural Science Foundation of China

Publisher

Wiley

Subject

Energy Engineering and Power Technology,Fuel Technology,Nuclear Energy and Engineering,Renewable Energy, Sustainability and the Environment

Link

https://onlinelibrary.wiley.com/doi/pdf/10.1002/er.5750

Reference50 articles.

1. Model-based State-of-charge Estimation Approach of the Lithium-ion Battery Using an Improved Adaptive Particle Filter

2. Discussion on the Li-Ion Battery Health Monitoring and Remaining-Useful-Life Prediction

3. Indirect State-of-Health Estimation for Lithium-Ion Batteries under Randomized Use

4. Battery-Management System (BMS) and SOC Development for Electrical Vehicles

5. A naive Bayes model for robust remaining useful life prediction of lithium-ion battery

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