The DOI matches its Crossref record; 37 of the 40 references it cites were found in a registry or an index.
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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In the citing paper
“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
Waag, W., Fleischer, C., & Sauer, D. U. (2014) · Journal of Power Sources · doi:10.1016/j.jpowsour.2014.02.064 · Cited by 1,059
State-of-Charge Estimation for Lithium-Ion Batteries Using Neural Networks and EKF
Charkhgard, M., & Farrokhi, M. (2010) · IEEE Transactions on Industrial Electronics · doi:10.1109/tie.2010.2043035 · Cited by 779
Sun, F., Hu, X., Zou, Y., & Li, S. (2011) · Energy · doi:10.1016/j.energy.2011.03.059 · Cited by 614
Hu, C. K., Youn, B. D., & Chung, J. (2011) · Applied Energy · doi:10.1016/j.apenergy.2011.08.002 · Cited by 561
Support Vector Machines Used to Estimate the Battery State of Charge
Álvarez Antón, J. C., García Nieto, P. J., Blanco Viejo, C., & Vilán Vilán, J. A. (2013) · IEEE Transactions on Power Electronics · doi:10.1109/tpel.2013.2243918 · Cited by 512
Training recurrent neural networks
Hinton, G. E., & Sutskever, I. (2013) · Cited by 380
Roscher, M. A., & Sauer, D. U. (2010) · Journal of Power Sources · doi:10.1016/j.jpowsour.2010.06.098 · Cited by 365
Meng, J., Luo, G., & Gao, F. (2015) · IEEE Transactions on Power Electronics · doi:10.1109/tpel.2015.2439578 · Cited by 350
Support vector based battery state of charge estimator
Hansen, T., & Wang, C.-J. (2005) · Journal of Power Sources · doi:10.1016/j.jpowsour.2004.09.020 · Cited by 347
Chemali, E., Preindl, M., Malysz, P., & Emadi, A. (2016) · IEEE Journal of Emerging and Selected Topics in Power Electronics · doi:10.1109/jestpe.2016.2566583 · Cited by 300
Self-citationGholizadeh, M., & Salmasi, F. R. (2013) · IEEE Transactions on Industrial Electronics · doi:10.1109/tie.2013.2259779 · Cited by 298
Li, J., Barillas, J. K., Guenther, C., & Danzer, M. A. (2012) · Journal of Power Sources · doi:10.1016/j.jpowsour.2012.12.057 · Cited by 253
Chen, X., Shen, W., Cao, Z., & Kapoor, A. (2013) · Journal of Power Sources · doi:10.1016/j.jpowsour.2013.08.039 · Cited by 214
Battery state-of-charge estimator using the SVM technique
Álvarez Antón, J. C., García Nieto, P. J., de Cos Juez, F. J., Sánchez Lasheras, F., González Vega, M., & Roqueñí Gutiérrez, M. N. (2013) · Applied Mathematical Modelling · doi:10.1016/j.apm.2013.01.024 · Cited by 211
Deng, Z., Yang, L., Cai, Y., Deng, H., & Sun, L. (2016) · Energy · doi:10.1016/j.energy.2016.06.130 · Cited by 168
Advanced Electric Drive Vehicles
Emadi, A. (2014) · Energy, power electronics, and machines series · doi:10.1201/b17506 · Cited by 164
Self-citationFast Model Predictive Control for Redistributive Lithium-Ion Battery Balancing
McCurlie, L., Preindl, M., & Emadi, A. (2016) · IEEE Transactions on Industrial Electronics · doi:10.1109/tie.2016.2611488 · Cited by 138
Self-citationState of charge estimation for Li-ion battery based on model from extreme learning machine
Du, J., Liu, Z., & Wang, Y. (2014) · Control Engineering Practice · doi:10.1016/j.conengprac.2013.12.014 · Cited by 136
Soft Computing for Battery State-of-Charge (BSOC) Estimation in Battery String Systems
Lee, Y., Wang, W., & Kuo, T.-Y. (2008) · IEEE Transactions on Industrial Electronics · doi:10.1109/tie.2007.896496 · Cited by 136
Ahmed, R., El Sayed, M., Arasaratnam, I., Tjong, J., & Habibi, S. (2014) · IEEE Journal of Emerging and Selected Topics in Power Electronics · doi:10.1109/jestpe.2014.2331059 · Cited by 135
Self-citationIntegrated System Identification and State-of-Charge Estimation of Battery Systems
Liu, L., Wang, L. Y., Chen, Z., Wang, C., Lin, F., & Wang, H. (2012) · IEEE Transactions on Energy Conversion · doi:10.1109/tec.2012.2223700 · Cited by 122
Chang, W.-Y. (2013) · International Journal of Electrical Power & Energy Systems · doi:10.1016/j.ijepes.2013.05.038 · Cited by 111
Ahmed, R., El Sayed, M., Arasaratnam, I., Tjong, J., & Habibi, S. (2014) · IEEE Journal of Emerging and Selected Topics in Power Electronics · doi:10.1109/jestpe.2014.2331062 · Cited by 101
Self-citationTraining Recurrent Neural Networks
Sutskever, I. (2013) · Cited by 79
Malysz, P., Gu, R., Ye, J., Yang, H., & Emadi, A. (2016) · IET Electrical Systems in Transportation · doi:10.1049/iet-est.2015.0030 · Cited by 44
Self-citationKollmeyer, P. J. (2015) · Cited by 16
Self-citationKollmeyer, P. J., Lamb, W., Juang, L. W., McFarland, J. D., Jahns, T. M., & Sarlioglu, B. (2012) · IEEE Transportation Electrification Conference and Expo · doi:10.1109/itec.2012.6243511 · Cited by 13
Self-citation
Cited by
Sorted by citations
Wang, Y., Tian, J., Sun, Z., Wang, L., Xu, R., Li, M., & Chen, Z. (2020) · Renewable and Sustainable Energy Reviews · doi:10.1016/j.rser.2020.110015 · Cited by 1,394
How, D. N. T., Hannan, M. A., Lipu, M. S. H., & Ker, P. J. (2019) · IEEE Access · doi:10.1109/access.2019.2942213 · Cited by 850
Hu, X., Yuan, H. S., Zou, C., Li, Z., & Zhang, L. (2018) · IEEE Transactions on Vehicular Technology · doi:10.1109/tvt.2018.2865664 · Cited by 536
Nyamathulla, S., & Dhanamjayulu, C. (2024) · Journal of Energy Storage · doi:10.1016/j.est.2024.111179 · Cited by 518
Deep-Convolution-Based LSTM Network for Remaining Useful Life Prediction
Ma, M., & Mao, Z. (2020) · IEEE Transactions on Industrial Informatics · doi:10.1109/tii.2020.2991796 · Cited by 518
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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