State-of-Health Estimation of Lithium-ion Batteries Based on WOA-CNN-LSTM-Attention
Author:
Affiliation:
1. University of Chinese Academy of Sciences,Institute of Electrical Engineering Chinese Academy of Sciences,Beijing,China
2. University of Science and Technology,Shunde Innovation School,Beijing,China
Funder
National Natural Science Foundation of China
Publisher
IEEE
Link
http://xplorestaging.ieee.org/ielx7/10154619/10154582/10154659.pdf?arnumber=10154659
Reference29 articles.
1. Uncertainty Management for Diagnostics and Prognostics of Batteries using Bayesian Techniques
2. Online Capacity Estimation of Lithium-Ion Batteries Based on Novel Feature Extraction and Adaptive Multi-Kernel Relevance Vector Machine
3. Lithium-Ion Battery Prognostics based on Support Vector Regression and Time-Series Analysis
4. Battery Health Prognosis for Electric Vehicles Using Sample Entropy and Sparse Bayesian Predictive Modeling
5. Intelligent prognostics for battery health monitoring based on sample entropy
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