The DOI matches its Crossref record; 101 of the 102 references it cites were found in a registry or an index.
Data-driven state of charge estimation of lithium-ion batteries: Algorithms, implementation factors, limitations and future trends
Abstract
Abstract Global carbon emissions caused by fossil fuels and diesel-based vehicles have urged the necessity to move toward the development of electric vehicles and related battery storage systems. Lithium-ion batteries are the ideal candidate for electric vehicle due to their superior performance with regard to high energy density and long lifespan. The state of charge of lithium-ion batteries is one of the crucial evaluation indicators of the battery management system that confirms the extended battery life, better charging-discharging profiles, and safe driving of electric vehicles. However, the accuracy of the state of charge is influenced by several issues such as battery aging cycles, noise effects, and temperature impacts. Therefore, this review presents a detailed classification of the recent data-driven state of charge estimation highlighting algorithm, input features, configuration, execution process, strength, weakness and estimation error. This review critically investigates the various key implementation factors of the data-driven algorithms in terms of data preprocessing, hyperparameter adjustment, activation function, evaluation criteria, computational cost and robustness validation under uncertainties. In addition, the review explores the deficiencies of existing data-driven state of charge estimation algorithms to identify the gaps for future research. Finally, the review provides some effective future directions that would be beneficial to the automobile researchers and industrialists to design an accurate and robust state of charge estimation technique toward future sustainable electric vehicle applications.
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“Recent advances in data-driven machine learning (ML) methods have enhanced SOC estimation[8,9], offering notable advantages.”
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 [9]
Citations
Cites
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Self-citationFrom model-based control to data-driven control: Survey, classification and perspective
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Data-driven health estimation and lifetime prediction of lithium-ion batteries: A review
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Critical Review on the Battery State of Charge Estimation Methods for Electric Vehicles
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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
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1 of the 102 references couldn’t be matched to a known paper.
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- Guzman, 2017, The use of NARX neural networks to forecast daily groundwater levels. Water resour, OR Manag., 31, 1591
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Hannan, M. A., Wali, S. B., Ker, P. J., Rahman, M. S. A., Mansor, M., Ramachandaramurthy, V. K., Muttaqi, K. M., Mahlia, T. M. I., & Dong, Z. Y. (2021) · Journal of Energy Storage · doi:10.1016/j.est.2021.103023 · Cited by 749
Self-citationSolar photovoltaic energy optimization methods, challenges and issues: A comprehensive review
Al-Shahri, O. A., Ismail, F. B., Hannan, M. A., Lipu, M. S. H., Al-Shetwi, A. Q., Begum, R. A., Al-Muhsen, N. F. O., & Soujeri, E. (2020) · Journal of Cleaner Production · doi:10.1016/j.jclepro.2020.125465 · Cited by 539
Self-citationLipu, M. S. H., Hannan, M. A., Karim, T. F., Hussain, A., Saad, M. H. M., Ayob, A., Miah, M. S., & Mahlia, T. M. I. (2021) · Journal of Cleaner Production · doi:10.1016/j.jclepro.2021.126044 · Cited by 478
Self-citationWang, Z., Feng, G., Zhen, D., Gu, F., & Ball, A. (2021) · Energy Reports · doi:10.1016/j.egyr.2021.08.113 · Cited by 441
Cui, Z., Wang, L., Li, Q., & Wang, K. (2021) · International Journal of Energy Research · doi:10.1002/er.7545 · Cited by 392
Metadata
- Authors
- Hossain Lipu, M. S., Hannan, M. A., Hussain, A., Ayob, A., Saad, M. H. M., Karim, T. F., & How, D. N. T.
- Affiliations
- Universiti Tenaga Nasional (MY)
- National University of Malaysia (MY)
- American International University-Bangladesh (BD)
- Corresponding author
- Hussain, Aini
- Venue
- Journal of Cleaner Production
- Publisher
- Elsevier BV
- Published
- 2020-12
- Volume
- 277
- Pages
- 124110
- ISSN
- 0959-6526 (ISSN-L)
- OpenAlex
- W3084393668
- Semantic Scholar
- 9a766a9683c379eb728e014b816371801b64f50e
- Cited by
- 378
- References
- 102
- Citations by year
- 2026: 25 · 2025: 70 · 2024: 82 · 2023: 89 · 2022: 62 · 2021: 48 · 2020: 2
- FWCI
- 18.59
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- Journal of Cleaner Production (publishedVersion)
- Funders
- Universiti Tenaga Nasional
- Ministry of Higher Education, Malaysia
- Universiti Kebangsaan Malaysia (DIP-2018-020, 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
- lithium-ion batteries, state of charge estimation, data-driven algorithms, electric vehicles, battery management systems, battery degradation, temperature effects, data preprocessing, hyperparameter optimization, activation functions, robustness under uncertainty, computational cost
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