Novel Image-Based Rapid RUL Prediction for Li-Ion Batteries Using a Capsule Network and Transfer Learning
Author:
Affiliation:
1. Department of Automotive and Mechatronics Engineering, Ontario Tech University, ON L1G 0C5, Oshawa, Canada
Funder
Natural Sciences and Engineering Research Council of Canada (NSERC) through the NSERC Early Career Researcher Supplement and Discovery Grant Program
Ontario Tech University Startup Fund
Publisher
Institute of Electrical and Electronics Engineers (IEEE)
Subject
Electrical and Electronic Engineering,Energy Engineering and Power Technology,Transportation,Automotive Engineering
Link
http://xplorestaging.ieee.org/ielx7/6687316/10050348/09771453.pdf?arnumber=9771453
Reference22 articles.
1. A LSTM-RNN method for the lithuim-ion battery remaining useful life prediction
2. A Data-Driven Approach With Uncertainty Quantification for Predicting Future Capacities and Remaining Useful Life of Lithium-ion Battery
3. Remaining useful life prediction of lithium-ion batteries based on false nearest neighbors and a hybrid neural network
4. Remaining Useful Life Assessment for Lithium-Ion Batteries Using CNN-LSTM-DNN Hybrid Method
5. Evaluation of the End-of-Life of Electric Vehicle Batteries According to the State-of-Health
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