Long Short-Term Memory Networks for Accurate State-of-Charge Estimation of Li-ion Batteries

State of charge (SOC) estimation is critical to the safe and reliable operation of Li-ion battery packs, which nowadays are becoming increasingly used in electric vehicles (EVs), Hybrid EVs, unmanned aerial vehicles, and smart grid systems. We introduce a new method to perform accurate SOC estimatio...

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Veröffentlicht in:	IEEE transactions on industrial electronics (1982) Jg. 65; H. 8; S. 6730 - 6739
Hauptverfasser:	Chemali, Ephrem, Kollmeyer, Phillip J., Preindl, Matthias, Ahmed, Ryan, Emadi, Ali
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	New York IEEE 01.08.2018 The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Schlagworte:	Ambient temperature Batteries Battery management systems Computer architecture Datasets Electric filters Electric vehicles Estimation Hybrid vehicles Kalman filters Li-ion batteries Lithium-ion batteries Logic gates long short-term memory (LSTM) machine learning Microprocessors Neural networks Rechargeable batteries Recurrent neural networks recurrent neural networks (RNNs) Short term Smart grid State of charge state-of-charge (SOC) estimation Temperature Training Unmanned aerial vehicles
ISSN:	0278-0046, 1557-9948
Online-Zugang:	Volltext
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