Transfer Learning Techniques for the Lithium-Ion Battery State of Charge Estimation
Open accessAbstract
State of Charge (SOC) estimation is vital for battery management systems (BMS), impacting battery efficiency and lifespan. Accurate SOC estimation is challenging due to battery complexity and limited data for training Machine Learning based models. Transfer learning (TL) leverages pre-trained models, reducing training time and improving generalization in SOC estimation. In this paper, 8 different transfer learning techniques are examined, which were applied in four different models (LSTM, GRU, BiLSTM, and BiGRU) for SOC estimation. These transfer learning techniques have been applied to three datasets for re-training the models and results have been compared with the same models defined by Bayesian Hyperparameter Optimization. The TL4 and TL5 techniques consistently stood out as among the most efficient in both accuracy and computational time.
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Comparative results with trained models optimized via Bayesian hyperparameter tuning demonstrated that hybrid architectures and adaptive hidden state techniques are always one of the most effective technologies in both accuracy and computation time.
“Comparative results with trained models optimized via Bayesian hyperparameter tuning demonstrated that hybrid architectures and adaptive hidden state techniques are always one of the most effective technologies in both accuracy and computation time.”
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 [93]
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Experimental — accuracy not yet measuredEleftheriadis et al. analyzed the impact of eight different transfer learning techniques on four different methods for SOC estimation, such as LSTM, GRU, BiLSTM, and BiGRU.
“Eleftheriadis et al. analyzed the impact of eight different transfer learning techniques on four different methods for SOC estimation, such as LSTM, GRU, BiLSTM, and BiGRU[93].”
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 [93]
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Cites
Adam: A Method for Stochastic Optimization
Kingma, D. P., & Ba, J. (2014) · arXiv · doi:10.48550/arxiv.1412.6980 · Cited by 98,946 · see citing works
Scikit-learn: Machine Learning in Python
Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Müller, A., Nothman, J., Louppe, G., et al. (2012) · arXiv · doi:10.48550/arxiv.1201.0490 · Cited by 71,016 · see citing works
Pan, S. J., & Yang, Q. (2010) · IEEE Transactions on Knowledge and Data Engineering · doi:10.1109/tkde.2009.191 · Cited by 23,387 · see citing works
Weiss, K., Khoshgoftaar, T. M., & Wang, D. (2016) · Journal of Big Data · doi:10.1186/s40537-016-0043-6 · Cited by 6,260 · see citing works
Hannan, M. A., Lipu, M. S. H., Hussain, A., & Mohamed, A. (2017) · Renewable and Sustainable Energy Reviews · doi:10.1016/j.rser.2017.05.001 · Cited by 2,005 · see citing works
Robust Estimation of a Location Parameter
Huber, P. J. (1992) · Springer series in statistics · doi:10.1007/978-1-4612-4380-9_35 · Cited by 1,458 · see citing works
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 823 · see citing works
Ali, M. U., Zafar, A., Nengroo, S. H., Hussain, S., Alvi, M. J., & Kim, H. (2019) · Energies · doi:10.3390/en12030446 · Cited by 455 · see citing works
Xiong, R., Zhang, Y., Wang, J., He, H., Peng, S., & PECHT, M. G. (2018) · IEEE Transactions on Vehicular Technology · doi:10.1109/tvt.2018.2864688 · Cited by 450 · see citing works
Hossain Lipu, M. S., Hannan, M. A., Hussain, A., Ayob, A., Saad, M. H. M., Karim, T. F., & How, D. N. T. (2020) · Journal of Cleaner Production · doi:10.1016/j.jclepro.2020.124110 · Cited by 378 · see citing works
Adaikkappan, M., & Nageswari, S. (2021) · International Journal of Energy Research · doi:10.1002/er.7339 · Cited by 326 · see citing works
A Review of Battery State of Health Estimation Methods: Hybrid Electric Vehicle Challenges
Noura, N., Boulon, L., & Jemeï, S. (2020) · World Electric Vehicle Journal · doi:10.3390/wevj11040066 · Cited by 266 · see citing works
Lithium-ion battery state of charge estimation with a Kalman Filter based on a electrochemical model
Domenico, D. D., Fiengo, G., & Stefanopoulou, A. G. (2008) · The proceedings of the IEEE Conference on Control Applications/The proceedings of the ... IEEE Conference on Control Applications · doi:10.1109/cca.2008.4629639 · Cited by 238 · see citing works
Schwendemann, S., Amjad, Z., & Sikora, A. (2021) · Engineering Applications of Artificial Intelligence · doi:10.1016/j.engappai.2021.104415 · Cited by 156 · see citing works
LG 18650HG2 Li-ion Battery Data and Example Deep Neural Network xEV SOC Estimator Script
Naguib, M. (2020) · Data Archiving and Networked Services (DANS) · doi:10.17632/cp3473x7xv · Cited by 131 · see citing works
Guo, R., & Shen, W. (2021) · Vehicles · doi:10.3390/vehicles4010001 · Cited by 121 · see citing works
State-of-Charge Estimation of Lithium-ion Batteries Using LSTM Deep Learning Method
Chung, D., Ko, J.-H., & Yoon, K.-Y. (2022) · Journal of Electrical Engineering and Technology · doi:10.1007/s42835-021-00954-8 · Cited by 85 · see citing works
Vehicles driving behavior recognition based on transfer learning
Chen, S., Yao, H., Qiao, F., et al. (2022) · Expert Systems with Applications · doi:10.1016/j.eswa.2022.119254 · Cited by 61 · see citing works
Data-Driven Methods for the State of Charge Estimation of Lithium-Ion Batteries: An Overview
Eleftheriadis, P., Giazitzis, S., Leva, S., & Ogliari, E. (2023) · Forecasting · doi:10.3390/forecast5030032 · Cited by 59 · see citing works
Self-citationEleftheriadis, P., Leva, S., & Ogliari, E. (2023) · Sustainable Energy Grids and Networks · doi:10.1016/j.segan.2023.101160 · Cited by 53 · see citing works
Self-citationAnalysis of Optimal Machine Learning Approach for Battery Life Estimation of Li-Ion Cell
Venugopal, P., Shankar, S. S., Jebakumar, C. P., Agarwal, R., Alhelou, H. H., Reka, S. S., & Golshan, M. E. H. (2021) · IEEE Access · doi:10.1109/access.2021.3130994 · Cited by 50 · see citing works
Shen, L., Li, J., Liu, J., Zhu, L., & Shen, H. T. (2022) · IEEE Transactions on Power Electronics · doi:10.1109/tpel.2022.3220760 · Cited by 43 · see citing works
Qin, P., & Zhao, L. (2023) · IEEE Transactions on Industrial Electronics · doi:10.1109/tie.2023.3250768 · Cited by 30 · see citing works
IEEE Access Special Section Editorial: Advanced Energy Storage Technologies and Their Applications
Xiong, R., Sharkh, S. M., Li, H., et al. (2020) · IEEE Access · doi:10.1109/access.2020.3036556 · Cited by 14 · see citing works
Rao, D. J. (2019) · doi:10.1002/9781119564843.ch4 · Cited by 14 · see citing works
An Overview of Data-Driven Methods for the State of Health Estimation
Eleftheriadis, P., Giazitzis, S., Ozmalatyalilar, C., Leva, S., & Zich, R. (2023) · IEEE International Conference on Environment and Electrical Engineering and IEEE Industrial and Commercial Power Systems Europe (EEEIC / I&CPS Europe) · doi:10.1109/eeeic/icpseurope57605.2023.10194864 · Cited by 11 · see citing works
Self-citationBattery Fuel Gauge: A Crucial Element in Battery Management Systems
Balasingam, B., Kumar, P., & Rankin, G. W. (2022) · IEEE Instrumentation & Measurement Magazine · doi:10.1109/mim.2022.9908256 · Cited by 6 · see citing works
State-of-Charge Estimation of Lithium Battery Based on Transfer Learning
Zhang, Z., Yang, Z., Gan, C., & Karlsen, H. (2021) · International Conference on Artificial Intelligence, Big Data and Algorithms (CAIBDA) · doi:10.1109/caibda53561.2021.00018 · Cited by 4 · see citing works
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A Review of Lithium-Ion Battery State of Charge Estimation Methods Based on Machine Learning
Zhao, F., Guo, Y., & Chen, B. (2024) · World Electric Vehicle Journal · doi:10.3390/wevj15040131 · Cited by 98 · see citing works
Yao, J., & Kowal, J. (2025) · Energy and AI · doi:10.1016/j.egyai.2025.100585 · Cited by 30 · see citing works
Wu, Y., Bai, D., Zhang, K., Li, Y., & Yang, F. (2025) · Journal of Materials Informatics · doi:10.20517/jmi.2024.84 · Cited by 20 · see citing works
- Authors
- Eleftheriadis, P., Giazitzis, S., Leva, S., & Ogliari, E.
- Affiliations
- Politecnico di Milano (IT)
- Department of Energy, Politecnico di Milano, Milan, Italy
- Corresponding author
- Eleftheriadis, Panagiotis
- Venue
- IEEE Access
- Published
- 2024
- Volume
- 12
- Pages
- 993-1004
- ISSN
- 2169-3536 (ISSN-L)
- OpenAlex
- W4389040886
- Cited by
- 31
- References
- 29
- Citations by year
- 2026: 7 · 2025: 13 · 2024: 11
- FWCI
- 2.25
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Locations
- IEEE Access (publishedVersion, cc-by)
- Virtual Community of Pathological Anatomy (University of Castilla La Mancha) (submittedVersion, cc-by)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- Funders
- MOST–Sustainable Mobility Center through the European Union Next-GenerationEU (PIANO NAZIONALE DI RIPRESA E RESILIENZA (CN00000023)
- 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, transfer learning, battery management systems, LSTM, GRU, bidirectional LSTM, bidirectional GRU, Bayesian hyperparameter optimization, limited training data
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