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1
State of Health Estimation of Lithium-Ion Batteries for Dyn..:
, In:
IECON 2022 – 48th Annual Conference of the IEEE Industrial Electronics Society
,
Ghosh, Nitika
;
Garg, Akhil
;
Warnecke, Alexander
. - p. 1-6 , 2022
Link:
https://doi.org/10.1109/IECON49645.2022.9968789
RT T1 <cTitle>IECON 2022 – 48th Annual Conference of the IEEE Industrial Electronics Society</cTitle>: T1 <Title>State of Health Estimation of Lithium-Ion Batteries for Dynamic Driving Profiles Based on Feature Extraction from Battery Relaxation Time Using Machine Learning</Title> UL https://suche.suub.uni-bremen.de/peid=ieee-9968789&Exemplar=1&LAN=DE A1 Ghosh, Nitika A1 Garg, Akhil A1 Warnecke, Alexander A1 Panigrahi, B. K. YR 2022 SN 2577-1647 K1 Lithium-ion batteries K1 Training K1 NASA K1 Estimation K1 Feature extraction K1 Mathematical models K1 Real-time systems K1 State of Health (SOH) K1 machine learning K1 feature extraction K1 capacity fade K1 voltage relaxation SP 1 OP 6 LK http://dx.doi.org/https://doi.org/10.1109/IECON49645.2022.9968789 DO https://doi.org/10.1109/IECON49645.2022.9968789 SF ELIB - SuUB Bremen
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