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1 Ergebnisse
1
Lithium-Ion Health Prediction for Battery Systems Using Dee..:
, In:
2023 International Conference on Self Sustainable Artificial Intelligence Systems (ICSSAS)
,
Kumar, Jaidev
;
Murugan, A.S.S.
;
Kumar, R. Vinoth
... - p. 1481-1487 , 2023
Link:
https://doi.org/10.1109/ICSSAS57918.2023.10331780
RT T1 <cTitle>2023 International Conference on Self Sustainable Artificial Intelligence Systems (ICSSAS)</cTitle>: T1 <Title>Lithium-Ion Health Prediction for Battery Systems Using Deep A-LSTM-CNN Approach</Title> UL https://suche.suub.uni-bremen.de/peid=ieee-10331780&Exemplar=1&LAN=DE A1 Kumar, Jaidev A1 Murugan, A.S.S. A1 Kumar, R. Vinoth A1 Ghamande, Manasi Vyankatesh A1 Kumar, NMG A1 Radhika YR 2023 K1 Lithium-ion batteries K1 Voltage measurement K1 Transportation K1 Aging K1 Predictive models K1 Feature extraction K1 Electric vehicles K1 Convolutional Neural Network (CNN) K1 Principal Component Analysis (PCA) K1 Attention K1 Preprocessing SP 1481 OP 1487 LK http://dx.doi.org/https://doi.org/10.1109/ICSSAS57918.2023.10331780 DO https://doi.org/10.1109/ICSSAS57918.2023.10331780 SF ELIB - SuUB Bremen
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