Optimized Data-Driven Network for Remaining Useful Life Prediction of Lithium-ion Batteries-Reference-Cited by-同舟云学术

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Optimized Data-Driven Network for Remaining Useful Life Prediction of Lithium-ion Batteries

Published:2023-12-03 Issue: Volume: Page:1-6
ISSN:
Container-title:2023 IEEE International Conference on Energy Technologies for Future Grids (ETFG)
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Author:

Ansari Shaheer¹,Ayob Afida¹,Hussain Aini¹,Lipu M S Hussain²,Saad Mohamad Hanif Md³,Hannan M A⁴

Affiliation:

1. Universiti Kebangsaan Malaysia,Department of Electrical Electronic and Systems Engineering,Bangi,Malaysia

2. Green University of Bangladesh,Department of Electrical and Electronic Engineering,Dhaka,Malaysia

3. Universiti Kebangsaan Malaysia,Department of Mechanical and Manufacturing Engineering,Bangi,Malaysia

4. Sunway University,School of Engineering and Technology,Petaling Jaya,Malaysia

Publisher

IEEE

Link

http://xplorestaging.ieee.org/ielx7/10406302/10407105/10407420.pdf?arnumber=10407420

Reference16 articles.

1. Technological Developments in Batteries;Hu;IEEE Power and Energy Magazine,2017

2. A data-driven bias correction method based lithiumion battery modeling approach for electric vehicle applications

3. Destruction mechanism of the internal structure in Lithium-ion batteries used in aviation industry

4. A review on prognostics and health monitoring of Li-ion battery

5. Prognostics of lithium-ion batteries based on Dempster–Shafer theory and the Bayesian Monte Carlo method

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