The DOI matches its Crossref record; 14 of the 14 references it cites were found in a registry or an index.
SOC Estimation of Li-ion Batteries With Learning Rate-Optimized Deep Fully Convolutional Network
Open accessAbstract
In this letter, we train deep learning (DL) models to estimate the state-of-charge (SOC) of lithium-ion (Li-ion) battery directly from voltage, current, and battery temperature values. The deep fully convolutional network model is proposed for its novel architecture with learning rate optimization strategies. The proposed model is capable of estimating SOC at constant and varying ambient temperature on different drive cycles without having to be retrained. The model also outperformed other commonly used DL models such as the LSTM, GRU, and CNN on an open source Li-ion battery dataset. The model achieves 0.85% root mean squared error (RMSE) and 0.7% mean absolute error (MAE) at 25 °C and 2.0% RMSE and 1.55% MAE at varying ambient temperature (-20-25 °C).
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“Hannan et al. replaced the MLP with a full CNN for the output layer, creating a SOC estimator based on a fully convolutional network (FCN)[67]. Resultantly, it surpasses LSTM, GRU, and CNN-MLP methods in estimation accuracy, generalization ability, floating point operations per second (FLOPs), and runtime speed.”
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 [67]
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Citations
Cites
Cyclical Learning Rates for Training Neural Networks
Smith, L. N. (2017) · IEEE Winter Conference on Applications of Computer Vision · doi:10.1109/wacv.2017.58 · Cited by 2,931
On the Variance of the Adaptive Learning Rate and BeyondPreprint
Liu, L., Jiang, H., He, P., Chen, W., Liu, X., Gao, J., & Han, J. (2019) · arXiv (Cornell University) · doi:10.48550/arxiv.1908.03265 · Cited by 1,837
Long Short-Term Memory Networks for Accurate State-of-Charge Estimation of Li-ion Batteries
Chemali, E., Kollmeyer, P. J., Preindl, M., Ahmed, R., & Emadi, A. (2018) · IEEE Transactions on Industrial Electronics · doi:10.1109/tie.2017.2787586 · Cited by 906
Mish: A Self Regularized Non-Monotonic Activation Function
Misra, D. (2019) · arXiv · doi:10.48550/arxiv.1908.08681 · Cited by 541
Mish: A Self Regularized Non-Monotonic Neural Activation FunctionPreprint
Misra, D. (2019) · arXiv (Cornell University) · Cited by 478
Combined CNN-LSTM Network for State-of-Charge Estimation of Lithium-Ion Batteries
Song, X., Yang, F., Wang, D., & Tsui, K. (2019) · IEEE Access · doi:10.1109/access.2019.2926517 · Cited by 394
State-of-Charge Estimation of Li-Ion Battery in Electric Vehicles: A Deep Neural Network Approach
How, D. N. T., Hannan, M. A., Lipu, M. S. H., Sahari, K. S. M., Ker, P. J., & Muttaqi, K. M. (2020) · IEEE Transactions on Industry Applications · doi:10.1109/tia.2020.3004294 · Cited by 337
Self-citationDai, H., Zhao, G., Lin, M., Wu, J., & Zheng, G. (2018) · IEEE Transactions on Industrial Electronics · doi:10.1109/tie.2018.2880703 · Cited by 325
Hannan, M. A., Lipu, M. S. H., Hussain, A., Ker, P. J., Mahlia, T. M. I., Mansor, M., Ayob, A., Saad, M. H. M., & Dong, Z. Y. (2020) · Scientific Reports · doi:10.1038/s41598-020-61464-7 · Cited by 244
Self-citationXiao, B., Liu, Y., & Xiao, B. (2019) · IEEE Access · doi:10.1109/access.2019.2913078 · Cited by 173
El Mejdoubi, A., Oukaour, A., Chaoui, H., Gualous, H., Sabor, J., & Slamani, Y. (2016) · IEEE Transactions on Industrial Electronics · doi:10.1109/tie.2015.2509916 · Cited by 170
Ouyang, Q., Chen, J., & Zheng, J. Y. (2019) · IEEE Transactions on Power Electronics · doi:10.1109/tpel.2019.2948253 · Cited by 106
Lithium–Sulfur Battery State-of-Charge Observability Analysis and Estimation
Fotouhi, A., Auger, D. J., Propp, K., & Longo, S. (2017) · IEEE Transactions on Power Electronics · doi:10.1109/tpel.2017.2740223 · Cited by 89
Shen, J.-N., Shen, J.-J., He, Y., & Ma, Z. (2018) · IEEE Transactions on Power Electronics · doi:10.1109/tpel.2018.2861730 · Cited by 82
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Sorted by citations
Cui, Z., Wang, L., Li, Q., & Wang, K. (2021) · International Journal of Energy Research · doi:10.1002/er.7545 · Cited by 392
Prognostics and health management of Lithium-ion battery using deep learning methods: A review
Zhang, Y., & Li, Y. (2022) · Renewable and Sustainable Energy Reviews · doi:10.1016/j.rser.2022.112282 · Cited by 304
Manoharan, A., Begam, M., Aparow, V. R., & Sooriamoorthy, D. (2022) · Journal of Energy Storage · doi:10.1016/j.est.2022.105384 · Cited by 304
Ren, Z. G., & Du, C. (2023) · Energy Reports · doi:10.1016/j.egyr.2023.01.108 · Cited by 292
Applications of artificial neural network based battery management systems: A literature review
Kurucan, M., Özbaltan, M., Yetgín, Z., & Alkaya, A. (2023) · Renewable and Sustainable Energy Reviews · doi:10.1016/j.rser.2023.114262 · Cited by 277
Metadata
- Authors
- Hannan, M. A., How, D. N. T., Lipu, M. S. H., Ker, P. J., Dong, Z. Y., Mansur, M., & Blaabjerg, F.
- Affiliations
- Universiti Tenaga Nasional (MY)
- National University of Malaysia (MY)
- UNSW Sydney (AU)
- Aalborg University (DK)
- Venue
- IEEE Transactions on Power Electronics
- Published
- 2021-07
- Volume
- 36
- Issue
- 7
- Pages
- 7349-7353
- ISSN
- 0885-8993 (ISSN-L)
- 1941-0107
- OpenAlex
- W3107692354
- Cited by
- 233
- References
- 14
- Citations by year
- 2026: 26 · 2025: 53 · 2024: 56 · 2023: 47 · 2022: 33 · 2021: 18
- FWCI
- 9.06
- Type
- journal article
- Language
- en
- Open access
- green
- vbn.aau.dk
- Licence
- Locations
- IEEE Transactions on Power Electronics (publishedVersion)
- VBN Forskningsportal (Aalborg Universitet) (submittedVersion, other-oa)
- Funders
- Ministry of Higher Education, Malaysia (20190101LRGS)
- Topics
- Physical Sciences › Engineering › Automotive Engineering › Battery Modeling and Management
- Physical Sciences › Engineering › Electrical and Electronic Engineering › Advancements in Battery Materials
- Physical Sciences › Engineering › Automotive Engineering › Electric and Hybrid Vehicle Technologies
- Keywords
- state of charge estimation, lithium-ion batteries, learning rate optimization, battery management systems, driving cycles, voltage and current measurement, battery temperature
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