Novel methods for estimating lithium-ion battery state of energy and maximum available energy

Zheng, L; Zhu, J; Wang, G; He, T; Wei, Y

Novel methods for estimating lithium-ion battery state of energy and maximum available energy

Zheng, L Zhu, J

Wang, G

He, T Wei, Y

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Publication Type:: Journal Article
Citation:: Applied Energy, 2016, 178 pp. 1 - 8
Issue Date:: 2016-09-15

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Field	Value	Language
dc.contributor.author	Zheng, L	en_US
dc.contributor.author	Zhu, J https://orcid.org/0000-0002-9763-4047	en_US
dc.contributor.author	Wang, G https://orcid.org/0000-0003-4295-8578	en_US
dc.contributor.author	He, T	en_US
dc.contributor.author	Wei, Y	en_US
dc.date.issued	2016-09-15	en_US
dc.identifier.citation	Applied Energy, 2016, 178 pp. 1 - 8	en_US
dc.identifier.issn	0306-2619	en_US
dc.identifier.uri	http://hdl.handle.net/10453/117841
dc.description.abstract	© 2016 Elsevier Ltd. The battery state of energy (SOE) allows a direct determination of the ratio between the remaining and maximum available energy of a battery, which is critical for energy optimization and management in energy storage systems. In this paper, the ambient temperature, battery discharge/charge current rate and cell aging level dependencies of battery maximum available energy and SOE are comprehensively analyzed. An explicit quantitative relationship between SOE and state of charge (SOC) for LiMn2O4 battery cells is proposed for SOE estimation, and a moving-window energy-integral technique is incorporated to estimate battery maximum available energy. Experimental results show that the proposed approaches can estimate battery maximum available energy and SOE with high precision. The robustness of the proposed approaches against various operation conditions and cell aging levels is systematically evaluated.	en_US
dc.relation.ispartof	Applied Energy	en_US
dc.relation.isbasedon	10.1016/j.apenergy.2016.06.031	en_US
dc.subject.classification	Energy	en_US
dc.title	Novel methods for estimating lithium-ion battery state of energy and maximum available energy	en_US
dc.type	Journal Article
utslib.citation.volume	178	en_US
utslib.for	0906 Electrical and Electronic Engineering	en_US
utslib.for	09 Engineering	en_US
utslib.for	14 Economics	en_US
pubs.embargo.period	Not known	en_US
pubs.organisational-group	/University of Technology Sydney
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology/School of Electrical and Data Engineering
pubs.organisational-group	/University of Technology Sydney/Faculty of Science
pubs.organisational-group	/University of Technology Sydney/Faculty of Science/School of Mathematical and Physical Sciences
pubs.organisational-group	/University of Technology Sydney/Strength - CCET - Centre for Clean Energy Technology
pubs.organisational-group	/University of Technology Sydney/Students
utslib.copyright.status	open_access
pubs.publication-status	Published	en_US
pubs.volume	178	en_US

Abstract:

© 2016 Elsevier Ltd. The battery state of energy (SOE) allows a direct determination of the ratio between the remaining and maximum available energy of a battery, which is critical for energy optimization and management in energy storage systems. In this paper, the ambient temperature, battery discharge/charge current rate and cell aging level dependencies of battery maximum available energy and SOE are comprehensively analyzed. An explicit quantitative relationship between SOE and state of charge (SOC) for LiMn2O4 battery cells is proposed for SOE estimation, and a moving-window energy-integral technique is incorporated to estimate battery maximum available energy. Experimental results show that the proposed approaches can estimate battery maximum available energy and SOE with high precision. The robustness of the proposed approaches against various operation conditions and cell aging levels is systematically evaluated.

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http://hdl.handle.net/10453/117841