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1
Predicting the State of Health of Lithium Battery Based on ..:
, In:
2023 International Symposium on Intelligent Robotics and Systems (ISoIRS)
,
Li, Jixiang
;
Jin, Bang
;
Chang, Jingda
.. - p. 52-55 , 2023
Link:
https://doi.org/10.1109/ISoIRS59890.2023.00020
RT T1 <cTitle>2023 International Symposium on Intelligent Robotics and Systems (ISoIRS)</cTitle>: T1 <Title>Predicting the State of Health of Lithium Battery Based on Improved LSTM-RNN</Title> UL https://suche.suub.uni-bremen.de/peid=ieee-10229844&Exemplar=1&LAN=DE A1 Li, Jixiang A1 Jin, Bang A1 Chang, Jingda A1 Zhang, Tao A1 Liu, Zhaoyang YR 2023 K1 Lithium-ion batteries K1 Recurrent neural networks K1 Time series analysis K1 Estimation K1 Filtering algorithms K1 Lithium batteries K1 Prediction algorithms K1 sequential K1 state-of-health (SOH) of battery K1 LSTM-RNN K1 improved particle filtering algorithm SP 52 OP 55 LK http://dx.doi.org/https://doi.org/10.1109/ISoIRS59890.2023.00020 DO https://doi.org/10.1109/ISoIRS59890.2023.00020 SF ELIB - SuUB Bremen
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