Spatial-temporal Attention-Based Time Series Prediction Network for Lithium Battery Remaining Useful Life Estimation
Author:
Affiliation:
1. Hangzhou Dianzi University,School of Electronic and Information,Hangzhou,China,310018
2. Zhejiang Leapmotor Technology Co., Ltd,Hangzhou,China,310053
Funder
National Natural Science Foundation of China
Publisher
IEEE
Link
http://xplorestaging.ieee.org/ielx7/10498130/10498125/10498716.pdf?arnumber=10498716
Reference17 articles.
1. An integrated multi-head dual sparse self-attention network for remaining useful life prediction
2. Prediction of power network planning demand coefficient using eXtreme Gradient Boosting algorithm
3. A hybrid framework for predicting the remaining useful life of battery using Gaussian process regression
4. FAML-RT: Feature alignment-based multi-level similarity metric learning network for a two-stage robust tracker
5. A novel method of discharge capacity prediction based on simplified electrochemical model-aging mechanism for lithium-ion batteries
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