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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Bibliographic Details
Published in:IEEE transactions on industrial electronics (1982) Vol. 65; no. 8; pp. 6730 - 6739
Main Authors: Chemali, Ephrem, Kollmeyer, Phillip J., Preindl, Matthias, Ahmed, Ryan, Emadi, Ali
Format: Journal Article
Language:English
Published: New York IEEE 01.08.2018
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subjects:
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 Access:Get full text
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Summary: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.
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ISSN:0278-0046
1557-9948
DOI:10.1109/TIE.2017.2787586