Remaining Useful Life Prediction of Li-ion Batteries Using an Improved CNN-LSTM Model-Reference-Cited by-同舟云学术

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Remaining Useful Life Prediction of Li-ion Batteries Using an Improved CNN-LSTM Model

Published:2023-11-10 Issue: Volume: Page:1164-1169
ISSN:
Container-title:2023 IEEE 2nd International Power Electronics and Application Symposium (PEAS)
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Author:

Yu Annuo¹,Wang Qingsong¹,Dong Xiaofeng²,Buja Giuseppe³

Affiliation:

1. School of Electrical Engineering, Southeast University,Nanjing,China,210096

2. State Grid Jiangsu Electric Power Co., Ltd.,Suzhou Power Supply Branch,Suzhou,China,215000

3. University of Padova,Department of Industrial Engineering,Padova,Italy,35131

Funder

State Grid Corporation of China

Publisher

IEEE

Link

http://xplorestaging.ieee.org/ielx7/10394673/10394683/10395235.pdf?arnumber=10395235

Reference14 articles.

1. Predicting the Remaining Life of Lithium-ion Batteries Using a CNN-LSTM Model

2. Battery Health Prognosis Using Brownian Motion Modeling and Particle Filtering

3. State-of-Health Estimation and Remaining-Useful-Life Prediction for Lithium-Ion Battery Using a Hybrid Data-Driven Method

4. A LSTM-RNN method for the lithuim-ion battery remaining useful life prediction

5. Evaluation on State of Charge Estimation of Batteries With Adaptive Extended Kalman Filter by Experiment Approach

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