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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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Khan, M. A., & Khan, A. S. (2025) · SSRN Electronic Journal · doi:10.2139/ssrn.5593026
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State estimation of Lithium batteries based on adaptive outlier robust filter
Yun, Z., Qin, W., & Wu, C. (2026) · Journal of Energy Storage · doi:10.1016/j.est.2025.120241
Yüce, H., & Arabacı, H. (2025) · National Conference on Electrical and Electronics Engineering (ELECO) · doi:10.1109/eleco69582.2025.11329352
State of Charge Estimation of Batteries Using Physics Guided Neural Network and Sensor Data
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Wu, J., Zhou, J., Tan, S.-C., & Hui, S. Y. R. (2026) · IEEE Transactions on Power Electronics · doi:10.1109/tpel.2026.3665875
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Bhardwaj, T., Kale, V., Ballal, M. S., & Khond, S. (2026) · Ionics · doi:10.1007/s11581-026-06974-6
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Faryar, D., Berta, R., Fresta, M., Saad, A., Lazzaroni, L., Ballout, H., Srour, O., & Bellotti, F. (2026) · Lecture notes in electrical engineering · doi:10.1007/978-3-032-17174-0_4
Machine learning-driven evolution of intelligent unmanned aerial vehicles
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State of charge estimation of energy storage battery based on long short-term memory neural network
Wang, S., Xie, Y., Liu, G., Huang, Q., Wang, Y., Zhang, G., & Fernández, C. (2026) · Elsevier eBooks · doi:10.1016/b978-0-443-27578-4.00013-6
Aswini, M., & Dr. Muthurajan, S. (2026) · SSRN Electronic Journal · doi:10.2139/ssrn.6412562
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Tripathi, S. K., Shekhar, Y., Singh, A., Yadav, A. K., & Kumar, D. (2025) · International Conference Communication, Networks & Computing · doi:10.1109/cnc68716.2025.11484725
Wu, C., Wang, Y., & Xu, Y. (2025) · China Automation Congress (CAC) · doi:10.1109/cac67268.2025.11487556
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Wu, Z., Xu, W., Jiang, M., Sun, C., & Qiang, H. (2026) · Journal of The Electrochemical Society · doi:10.1149/1945-7111/ae6e9b
Lee, S.-E., Lee, J. B., Yoon, J., An, H., Suh, H., Kim, Y., & Kim, J. (2026) · IEEE Transactions on Transportation Electrification · doi:10.1109/tte.2026.3696642
Ma, L., Tian, J., Zhang, T., Guo, Q., Guo, R., Shen, W., Lu, Y., & Chung, C. Y. (2026) · Energy storage materials · doi:10.1016/j.ensm.2026.105277
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Wang, Z., Zhang, J., & Zhang, Q. (2026) · Chinese Control and Decision Conference (CCDC) · doi:10.1109/ccdc69976.2026.11560298
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Li, S., Yang, S., Yang, Y., Wu, M., Zhu, Z., Ya, M., & Hu, J. (2026) · Journal of Energy Storage · doi:10.1016/j.est.2026.124251 · Cited by 1
Orellana-Iñiguez, J. P., Gallo, W. L. R., & Almeida, M. (2026) · Eletrônica de Potência · doi:10.18618/rep.e202626
Xia, J., Zeng, H., Zhang, Q., Ma, L., Ye, G., & liu, P. (2026) · SSRN Electronic Journal · doi:10.2139/ssrn.7353385
Jiang, D., Guo, Z., Xu, X., Bao, H., & Chen, K. (2026) · SSRN Electronic Journal · doi:10.2139/ssrn.7353384
Wang, J., Wei, W., Ma, B., Pan, H., & Liu, G. (2026) · Batteries · doi:10.3390/batteries12090328
Xie, X., Zhou, W., Liu, D., Xia, T., & He, X. (2026) · Energies · doi:10.3390/en19174050
Chen, H., Ren, B., Luo, A., Yan, Y., Meng, K., Zhang, G., Guo, Y., & Wang, Y. (2026) · Journal of Energy Storage · doi:10.1016/j.est.2026.124457
Xu, T., Li, P., Zhang, H., & Wang, K. (2026) · SSRN Electronic Journal · doi:10.2139/ssrn.7386834
Ali, S., Bianchi, V., & Munari, I. D. (2026) · Batteries · doi:10.3390/batteries12090338
Wang, Z., Li, Y., Sun, J., Zhang, Y., Liang, F., Wu, D., Yu, P., & Wang, W. (2026) · Journal of Energy Storage · doi:10.1016/j.est.2026.124453
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Li, Y., Zeng, H., Xia, J., Shi, Y., & Liu, P. (2026) · Research Square · doi:10.21203/rs.3.rs-10706443/v1
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Benbouhenni, H., & Bizon, N. (2026) · Batteries · doi:10.3390/batteries12090353 · Cited by 1
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Liu, Y., Xinyu, W. A. N. G., Feng, S., & Liang, X. (2026) · Journal of Energy Storage · doi:10.1016/j.est.2026.124614
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Amenzouy, R., Latif, R., Laaboubi, M., Bakhouya, M., Abouzahir, M., & Jenkal, W. (2026) · International Conference on Connected Innovation and Technology (ICCITX) · doi:10.1109/iccitx70146.2026.11679918
Zhu, M., Qiu, J., & Zhu, X. (2026) · Journal of Energy Storage · doi:10.1016/j.est.2026.124716
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Gobinet, R., Oudani, M., Charki, A., Makhoukh, A., & Redouane, A. (2026) · Lecture notes in networks and systems · doi:10.1007/978-3-032-29677-1_6
Sanikal, V., Suryadevara, G., Narayanam, V. R., & Supramanian, A. V. (2026) · Technology and Innovation Conference (TIC) · doi:10.1109/tic68483.2026.11702988
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Tsuji, K., Maruta, I., Kawaguchi, T., & Osamura, K. (2026) · IEEE Access · doi:10.1109/access.2026.3741614
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Jiang, D., Guo, Z., Xu, X., Bao, H., Shan, H., & Chen, K. (2026) · SSRN Electronic Journal · doi:10.2139/ssrn.7597208
Zhang, X., Gao, S., Wei, Y., & Haorui, W. (2026) · London Journal Of Engineering Research · doi:10.34257/ljer235744uk
Liu, B., Liu, G., & Zhu, L. (2026) · Journal of Energy Storage · doi:10.1016/j.est.2026.125058
Aşıkkaya, Ö. F., Yalman, Y., & Olcay, K. (2026) · Applied Sciences · doi:10.3390/app162010216
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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Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
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