Online battery state-of-charge estimation based on sparse gaussian process regression-Reference-Cited by-同舟云学术

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Online battery state-of-charge estimation based on sparse gaussian process regression

Published:2016-07 Issue: Volume: Page:
ISSN:
Container-title:2016 IEEE Power and Energy Society General Meeting (PESGM)
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Author:

Ozcan Gozde,Pajovic Milutin,Sahinoglu Zafer,Wang Yebin,Orlik Philip V.,Wada Toshihiro

Publisher

IEEE

Link

http://xplorestaging.ieee.org/ielx7/7593872/7741056/07741980.pdf?arnumber=7741980

Cited by 7 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. Research Progress on Data-Driven Methods for Battery States Estimation of Electric Buses;World Electric Vehicle Journal;2023-06-02

2. A Novel Neural Network With Gaussian Process Feedback for Modeling the State-of-Charge of Battery Cells;IEEE Transactions on Industry Applications;2022-07

3. Critical Review of Intelligent Battery Systems: Challenges, Implementation, and Potential for Electric Vehicles;Energies;2021-09-21

4. Online voltage prediction using gaussian process regression for fault-tolerant photovoltaic standalone applications;Neural Computing and Applications;2021-06-28

5. State of charge estimation for lithium-ion battery based on Gaussian process regression with deep recurrent kernel;International Journal of Electrical Power & Energy Systems;2021-01

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