Cited by 378Battery Modeling and ManagementAdvancements in Battery MaterialsElectric and Hybrid Vehicle Technologies

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Data-driven state of charge estimation of lithium-ion batteries: Algorithms, implementation factors, limitations and future trends

Hossain Lipu, M.S., Hannan, M.A., Hussain, Aini, Ayob, Afida, Saad, Mohamad H.M., Karim, Tahia F., How, Dickson N.T.·2020·Journal of Cleaner Production

DOI 10.1016/j.jclepro.2020.124110

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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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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
ORCID: Hannan, M.A.
Hussain, Aini
Ayob, Afida
Saad, Mohamad H.M.
How, Dickson N.T.
Venue: Journal of Cleaner Production
Publisher: Elsevier BV
Published: 2020-12
Volume: 277
Pages: 124110
ISSN: 0959-6526 (ISSN-L)
DOI: 10.1016/j.jclepro.2020.124110
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
Licence: www.elsevier.com/tdm/userlicense/1.0/ (tdm)
www.elsevier.com/legal/tdmrep-license (tdm)
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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Data-driven state of charge estimation of lithium-ion batteries: Algorithms, implementation factors, limitations and future trends

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