Lithium-ion Batteries State of Charge Comparison between Extended Kalman Filter and Machine Learning-Reference-Cited by-同舟云学术

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Lithium-ion Batteries State of Charge Comparison between Extended Kalman Filter and Machine Learning

Published:2024-05-20 Issue: Volume:113 Page:1-6
ISSN:
Container-title:2024 IEEE International Instrumentation and Measurement Technology Conference (I2MTC)
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Author:

Guedes Walter Barbosa¹,Da Silva Jaidilson Jó²,Perkusich Angelo²,Gomes de Sousa Alves Caio Luiz³

Affiliation:

1. Federal University of Campina Grande,Dept. of Electrical Engineering,Campina Grande,Brazil

2. Virtus Research, Development and Innovation Center Federal University of Campina Grande,Campina Grande,Brazil

3. Federal University of Paraíba,Dept. of Electrical Engineering,João Pessoa,Brazil

Publisher

IEEE

Link

http://xplorestaging.ieee.org/ielx8/10560479/10560519/10561115.pdf?arnumber=10561115

Reference19 articles.

1. State of Charge Estimation of Lithium-Ion Batteries in Electric Drive Vehicles Using Extended Kalman Filtering

2. Overview of model-based online state-of-charge estimation using Kalman filter family for lithium-ion batteries;Shrivastava;Renewable and Sustainable 31 Energy Reviews,2019

3. Generalised diagnostic framework for rapid battery degradation quantification with deep learning

4. Extended kalman filter for accurate state of charge estimation of lithium-based batteries: a comparative analysis

5. State of Charge and State of Health Estimation for Lithium Batteries Using Recurrent Neural Networks

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