Long Short-Term Memory Networks for Accurate State-of-Charge Estimation of Li-ion Batteries
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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Chemali et al. and Li et al. developed single-step non-recursive SOC estimators using LSTM, demonstrating excellent accuracy of LSTM in SOC estimation compared to SRNN.
“Chemali et al. and Li et al. developed single-step non-recursive SOC estimators using LSTM, demonstrating excellent accuracy of LSTM in SOC estimation compared to SRNN[79,96].”
From Advancements in the estimation of the state of charge of lithium-ion battery: a comprehensive review of traditional and deep learning approaches (2025) · cites this paper as [79]
Increasing the scale of the LSTM hidden layer enhanced estimation accuracy.
“They also found that increasing the scale of the LSTM hidden layer enhanced estimation accuracy.”
From Advancements in the estimation of the state of charge of lithium-ion battery: a comprehensive review of traditional and deep learning approaches (2025) · cites this paper as [79]
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- Authors
- Chemali, E., Kollmeyer, P. J., Preindl, M., Ahmed, R., & Emadi, A.
- Affiliations
- McMaster University (CA)
- Columbia University (US)
- Venue
- IEEE Transactions on Industrial Electronics
- Published
- 2018-08
- Volume
- 65
- Issue
- 8
- Pages
- 6730-6739
- ISSN
- 0278-0046 (ISSN-L)
- 1557-9948
- 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
- Locations
- IEEE Transactions on Industrial Electronics (publishedVersion)
- Funders
- Canada Excellence Research Chair in Hybrid Powertrain Program
- Topics
- Physical Sciences › Engineering › Automotive Engineering › Advanced Battery Technologies Research
- 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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