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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[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]
In the citing paper
“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]
Citations
Cites
Hochreiter, S., & Schmidhuber, J. (1997) · Neural Computation · doi:10.1162/neco.1997.9.8.1735 · Cited by 102,318
Adam: A Method for Stochastic Optimization
Kingma, D. P., & Ba, J. (2014) · arXiv · doi:10.48550/arxiv.1412.6980 · Cited by 98,946
LeCun, Y., Bengio, Y., & Hinton, G. (2015) · Nature · doi:10.1038/nature14539 · Cited by 84,126
Hinton, G., Deng, L., Yu, D., Dahl, G., Mohamed, A.-R., Jaitly, N., Senior, A., Vanhoucke, V., Nguyen, P., Sainath, T., et al. (2012) · IEEE Signal Processing Magazine · doi:10.1109/msp.2012.2205597 · Cited by 10,260
Speech recognition with deep recurrent neural networks
Graves, A., Mohamed, A.-R., & Hinton, G. (2013) · 2013 IEEE International Conference on Acoustics, Speech and Signal Processing · doi:10.1109/icassp.2013.6638947 · Cited by 8,893
On the difficulty of training recurrent neural networks
Pascanu, R., Mikolov, T., & Bengio, Y. (2012) · arXiv · doi:10.48550/arxiv.1211.5063 · Cited by 4,310
Recurrent Neural Network RegularizationPreprint
Zaremba, W., Sutskever, I., & Vinyals, O. (2014) · arXiv (Cornell University) · doi:10.48550/arxiv.1409.2329 · Cited by 3,174
Context-dependent pre-trained deep neural networks for large-vocabulary speech recognition
Dahl, G. E., Yu, D., Deng, L., & Acero, A. (2012) · IEEE Transactions on Audio Speech and Language Processing · doi:10.1109/tasl.2011.2134090 · Cited by 3,011
The Vanishing Gradient Problem During Learning Recurrent Neural Nets and Problem Solutions
Hochreiter, S. (1998) · International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems · doi:10.1142/s0218488598000094 · Cited by 2,939
Deep Speech 2: End-to-End Speech Recognition in English and MandarinPreprint
Amodei, D., Anubhai, R., Battenberg, E., Case, C. T., Casper, J., Catanzaro, B., Chen, J., Chrzanowski, M., Coates, A., Diamos, G., et al. (2015) · arXiv (Cornell University) · doi:10.48550/arxiv.1512.02595 · Cited by 2,265
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Ning, Z., Deng, Z., Li, J., Liu, H., & Guo, W. (2022) · Journal of Energy Storage · doi:10.1016/j.est.2022.106052 · Cited by 52
Nguyen, C. D., & Bae, S. J. (2023) · Journal of Energy Storage · doi:10.1016/j.est.2023.108042 · Cited by 53
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Liu, G., Deng, Z., Xu, Y., Lai, L., Gong, G., Tong, L., Zhang, H., Li, Y., Gong, M., Yan, M., et al. (2025) · Applied Sciences · doi:10.3390/app15137555 · Cited by 53
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Li, H., Fu, L., Long, X., Liu, L., & Zeng, Z. (2024) · Energy · doi:10.1016/j.energy.2024.130834 · Cited by 47
Oloyede, M., Akpakwu, G., Myburgh, H., De Freitas, A., & Kunatsa, T. (2024) · World Electric Vehicle Journal · doi:10.3390/wevj15090381 · Cited by 47
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Surya, S., Samanta, A., Marcis, V. A., & Williamson, S. S. (2022) · IEEE Transactions on Transportation Electrification · doi:10.1109/tte.2022.3170359 · Cited by 47
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Guirguis, J., & Ahmed, R. (2024) · Energies · doi:10.3390/en17143502 · Cited by 50
Self-citationVidal, C., Hausmann, M., Barroso, D., Shamsabadi, P. M., Biswas, A., Chemali, E., Ahmed, R., & Emadi, A. (2018) · IEEE Transportation Electrification Conference and Expo · doi:10.1109/itec.2018.8450251 · Cited by 45
Self-citationBockrath, S., Roßkopf, A., Koffel, S., Waldhör, S., Srivastava, K., & Lorentz, V. (2019) · Annual Conference of the IEEE Industrial Electronics Society (IECON) · doi:10.1109/iecon.2019.8926815 · Cited by 45
Electrical lithium-ion battery models based on recurrent neural networks: A holistic approach
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Yao, J., & Kowal, J. (2024) · Journal of Energy Storage · doi:10.1016/j.est.2024.114888 · Cited by 44
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Ding, X., Cui, Z., Yuan, H., & Cui, N. (2022) · Journal of Energy Storage · doi:10.1016/j.est.2022.105552 · Cited by 44
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Oh, S., Kim, J., & Moon, I. (2024) · Journal of Energy Storage · doi:10.1016/j.est.2024.112887 · Cited by 43
Dini, P., & Paolini, D. (2025) · Batteries · doi:10.3390/batteries11030107 · Cited by 40
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Wang, X., Sun, Q., Kou, X., Ma, W., Zhang, H., & Liu, R. (2021) · Energy · doi:10.1016/j.energy.2021.122406 · Cited by 40
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Lai, X., Qin, C., Gao, W., Zheng, Y., & Yi, W. (2018) · Applied Sciences · doi:10.3390/app8091592 · Cited by 39
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Javid, G., Abdeslam, D. O., & Basset, M. (2021) · Energies · doi:10.3390/en14030758 · Cited by 39
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Vellingiri, M. T., Mehedi, I. M., & Palaniswamy, T. (2022) · Mathematics · doi:10.3390/math10020260 · Cited by 37
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Ma, B., Yu, H., Wang, W., Yang, X., Zhang, L., Xie, H., Zhang, C., Chen, S., & Liu, X. (2022) · Rare Metals · doi:10.1007/s12598-022-02156-1 · Cited by 35
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Mirzaee, H., & Kamrava, S. (2023) · Journal of Energy Storage · doi:10.1016/j.est.2023.109522 · Cited by 34
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Yang, Z.-X., Yu, G., Zhao, J. B., Wong, P. K., & Wang, X. (2021) · IEEE Transactions on Vehicular Technology · doi:10.1109/tvt.2021.3087004 · Cited by 31
Kannan, M., Sundareswaran, K., Nayak, P. S. R., & Simon, S. P. (2023) · IEEE Transactions on Vehicular Technology · doi:10.1109/tvt.2023.3242269 · Cited by 31
Zine, B., Bia, H., Benmouna, A., Becherif, M., & Iqbal, M. (2022) · Energies · doi:10.3390/en15218172 · Cited by 30
Zhang, K., Bai, D., Li, Y., Song, K., Zheng, B., & Yang, F. (2024) · Applied Energy · doi:10.1016/j.apenergy.2024.122666 · Cited by 30
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Zhang, Y., Tan, X. M., & Wang, Z. (2025) · Journal of Energy Storage · doi:10.1016/j.est.2025.115575 · Cited by 29
Saneep, K., Sundareswaran, K., Nayak, P. S. R., & Puthusserry, G. V. (2025) · Journal of Energy Storage · doi:10.1016/j.est.2025.115879 · Cited by 30
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Tao, S., Jiang, B., Wei, X., & Dai, H. (2023) · Energies · doi:10.3390/en16042008 · Cited by 31
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Mei, P., Karimi, H. R., Xie, J., Chen, F., Ou, L., Yang, S., & Huang, C. (2024) · Renewable and Sustainable Energy Reviews · doi:10.1016/j.rser.2024.114857 · Cited by 29
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Naseem, H., Hadi, A., Kang, M., & Seok, J. (2025) · Energy Technology · doi:10.1002/ente.202500100 · Cited by 32
Duan, W., Song, C., Chen, Y., Xiao, F., Peng, S., Shao, Y., & Song, S. (2020) · Mathematical Problems in Engineering · doi:10.1155/2020/9502605 · Cited by 29
Yao, Q., Lu, D. D., & Lei, G. (2020) · IEEE Journal of Emerging and Selected Topics in Industrial Electronics · doi:10.1109/jestie.2020.3041711 · Cited by 29
Wadi, A., Abdel–Hafez, M. F., Hussein, A. A.-H., & Alkhawaja, F. (2021) · IEEE Transactions on Vehicular Technology · doi:10.1109/tvt.2021.3085006 · Cited by 29
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Cao, R., Zhang, Z., Lin, J., Lu, J., Zhang, L., Xiao, L., Liu, X., & Yang, S. (2022) · Batteries · doi:10.3390/batteries8110224 · Cited by 29
Metadata
- 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 › 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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