Prediction of remaining useful life of lithium batteries based on Decayable-LSTM
Author:
Affiliation:
1. Chongqing University of Posts and Telecommunications,Key Laboratory of Intelligent Computing for Big Data College of Automation,Chongqing,China,400065
Funder
National Natural Science Foundation of China
Publisher
IEEE
Link
http://xplorestaging.ieee.org/ielx8/10587298/10587321/10587444.pdf?arnumber=10587444
Reference20 articles.
1. Brits: Bidirectional recurrent imputation for time series[J];Cao;Advances in Neural Information Processing Systems,2018
2. LSTM-based traffic flow prediction with missing data
3. A generalized cycle life model of rechargeable Li-ion batteries
4. Parameter Estimation and Capacity Fade Analysis of Lithium-Ion Batteries Using Reformulated Models
5. Prognostics of Li(NiMnCo)O2-based lithium-ion batteries using a novel battery degradation model
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