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

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...

Full description

Saved in:

Bibliographic Details
Main Authors:	Hossain Lipu M.S., Hannan M.A., Hussain A., Ayob A., Saad M.H.M., Karim T.F., How D.N.T.
Other Authors:	36518949700
Format:	Review
Published:	Elsevier Ltd 2023
Tags:	Add Tag No Tags, Be the first to tag this record!

id	my.uniten.dspace-25063
record_format	dspace
spelling	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
institution	Universiti Tenaga Nasional
building	UNITEN Library
collection	Institutional Repository
continent	Asia
country	Malaysia
content_provider	Universiti Tenaga Nasional
content_source	UNITEN Institutional Repository
url_provider	http://dspace.uniten.edu.my/
description	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
author2	36518949700
author_facet	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
author	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	13.211869

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

Similar Items