Cited by 907Battery Modeling and ManagementAdvancements in Battery MaterialsControl Systems and Identification

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

Chemali, Ephrem, Kollmeyer, Phillip J., Preindl, Matthias, Ahmed, Ryan, Emadi, Ali·2018·IEEE Transactions on Industrial Electronics

DOI 10.1109/tie.2017.2787586

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Abstract

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 °C.

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Long Short-Term Memory
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Authors: Chemali, E., Kollmeyer, P. J., Preindl, M., Ahmed, R., & Emadi, A.
Affiliations: McMaster University (CA)
Columbia University (US)
ORCID: Chemali, Ephrem
Kollmeyer, Phillip J.
Preindl, Matthias
Ahmed, Ryan
Emadi, Ali
Venue: IEEE Transactions on Industrial Electronics
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Published: 2018-08
Volume: 65
Issue: 8
Pages: 6730-6739
ISSN: 0278-0046 (ISSN-L)
1557-9948
DOI: 10.1109/tie.2017.2787586
OpenAlex: W2776458183
Cited by: 906
References: 40
Citations by year: 2026: 125 · 2025: 142 · 2024: 141 · 2023: 135 · 2022: 121 · 2021: 98 · 2020: 78 · 2019: 50 · 2018: 16
FWCI: 24.97
Type: journal article
Language: en
Open access: closed
Licence: ieeexplore.ieee.org/Xplorehelp/downloads/license-information/IEEE.html (vor)
Locations: IEEE Transactions on Industrial Electronics (publishedVersion)
Funders: Canada Excellence Research Chair in Hybrid Powertrain Program
Topics: Physical Sciences › Engineering › Automotive Engineering › Battery Modeling and Management
Physical Sciences › Engineering › Electrical and Electronic Engineering › Advancements in Battery Materials
Physical Sciences › Engineering › Control and Systems Engineering › Control Systems and Identification
Keywords: state of charge estimation, lithium-ion batteries, long short-term memory, recurrent neural networks, ambient temperature, model-free estimation, electric vehicles

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

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