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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Vydáno v:	IEEE transactions on industrial electronics (1982) Ročník 65; číslo 8; s. 6730 - 6739
Hlavní autoři:	Chemali, Ephrem, Kollmeyer, Phillip J., Preindl, Matthias, Ahmed, Ryan, Emadi, Ali
Médium:	Journal Article
Jazyk:	angličtina
Vydáno:	New York IEEE 01.08.2018 The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Témata:	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
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Popis
Shrnutí:	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 estimation for Li-ion batteries using a recurrent neural network (RNN) with long short-term memory (LSTM). We showcase the LSTM-RNN's ability to encode dependencies in time and accurately estimate SOC without using any battery models, filters, or inference systems like Kalman filters. In addition, this machine-learning technique, like all others, is capable of generalizing the abstractions it learns during training to other datasets taken under different conditions. Therefore, we exploit this feature by training an LSTM-RNN model over datasets recorded at various ambient temperatures, leading to a single network that can properly estimate SOC at different ambient temperature conditions. The LSTM-RNN achieves a low mean absolute error (MAE) of 0.573% at a fixed ambient temperature and an MAE of 1.606% on a dataset with ambient temperature increasing from 10 to 25 <inline-formula><tex-math notation="LaTeX">^{\circ }</tex-math> </inline-formula>C.
Bibliografie:	ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 14
ISSN:	0278-0046 1557-9948
DOI:	10.1109/TIE.2017.2787586