Data-driven state of charge estimation of lithium-ion batteries: Algorithms, implementation factors, limitations and future trends
Automotive batteries; Charging (batteries); Digital storage; Electric automobiles; Fossil fuels; Fuel storage; Ions; Lithium-ion batteries; Battery storage system; Data-driven algorithm; Detailed classification; Evaluation indicators; Global carbon emission; High energy densities; State-of-charge es...
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2023
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my.uniten.dspace-250632023-05-29T16:06:37Z 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 A. Ayob A. Saad M.H.M. Karim T.F. How D.N.T. 36518949700 7103014445 57208481391 26666566900 7202075525 36518950900 57212923888 Automotive batteries; Charging (batteries); Digital storage; Electric automobiles; Fossil fuels; Fuel storage; Ions; Lithium-ion batteries; Battery storage system; Data-driven algorithm; Detailed classification; Evaluation indicators; Global carbon emission; High energy densities; State-of-charge estimation; Vehicle applications; Battery management systems 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. � 2020 Elsevier Ltd Final 2023-05-29T08:06:37Z 2023-05-29T08:06:37Z 2020 Review 10.1016/j.jclepro.2020.124110 2-s2.0-85091229921 https://www.scopus.com/inward/record.uri?eid=2-s2.0-85091229921&doi=10.1016%2fj.jclepro.2020.124110&partnerID=40&md5=7a8872cfddb2c7ef86d5c0611963d9c1 https://irepository.uniten.edu.my/handle/123456789/25063 277 124110 Elsevier Ltd Scopus |
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Automotive batteries; Charging (batteries); Digital storage; Electric automobiles; Fossil fuels; Fuel storage; Ions; Lithium-ion batteries; Battery storage system; Data-driven algorithm; Detailed classification; Evaluation indicators; Global carbon emission; High energy densities; State-of-charge estimation; Vehicle applications; Battery management systems |
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36518949700 |
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36518949700 Hossain Lipu M.S. Hannan M.A. Hussain A. Ayob A. Saad M.H.M. Karim T.F. How D.N.T. |
| format |
Review |
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Hossain Lipu M.S. Hannan M.A. Hussain A. Ayob A. Saad M.H.M. Karim T.F. How D.N.T. |
| spellingShingle |
Hossain Lipu M.S. Hannan M.A. Hussain A. Ayob A. Saad M.H.M. Karim T.F. How D.N.T. Data-driven state of charge estimation of lithium-ion batteries: Algorithms, implementation factors, limitations and future trends |
| author_sort |
Hossain Lipu M.S. |
| title |
Data-driven state of charge estimation of lithium-ion batteries: Algorithms, implementation factors, limitations and future trends |
| title_short |
Data-driven state of charge estimation of lithium-ion batteries: Algorithms, implementation factors, limitations and future trends |
| title_full |
Data-driven state of charge estimation of lithium-ion batteries: Algorithms, implementation factors, limitations and future trends |
| title_fullStr |
Data-driven state of charge estimation of lithium-ion batteries: Algorithms, implementation factors, limitations and future trends |
| title_full_unstemmed |
Data-driven state of charge estimation of lithium-ion batteries: Algorithms, implementation factors, limitations and future trends |
| title_sort |
data-driven state of charge estimation of lithium-ion batteries: algorithms, implementation factors, limitations and future trends |
| publisher |
Elsevier Ltd |
| publishDate |
2023 |
| _version_ |
1806425658357710848 |
| score |
13.222552 |