Experimental analysis and modeling of temperature dependence of lithium-ion battery direct current resistance for power capability prediction

Zheng, L; Zhu, J; Wang, G; Lu, DDC; McLean, P; He, T

Experimental analysis and modeling of temperature dependence of lithium-ion battery direct current resistance for power capability prediction

Zheng, L Zhu, J

Wang, G

Lu, DDC

McLean, P

He, T

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Publication Type:: Conference Proceeding
Citation:: 2017 20th International Conference on Electrical Machines and Systems, ICEMS 2017, 2017
Issue Date:: 2017-10-02

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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	Lu, DDC https://orcid.org/0000-0003-2145-0580	en_US
dc.contributor.author	McLean, P https://orcid.org/0000-0002-7731-4927	en_US
dc.contributor.author	He, T	en_US
dc.date.available	2020-05-25T19:12:03Z
dc.date.issued	2017-10-02	en_US
dc.identifier.citation	2017 20th International Conference on Electrical Machines and Systems, ICEMS 2017, 2017	en_US
dc.identifier.isbn	9781538632468	en_US
dc.identifier.uri	http://hdl.handle.net/10453/126174
dc.description.abstract	© 2017 IEEE. Accurate lithium-ion battery power capability prediction gives an indication for managing power flows in or out of batteries within the safe operating area, which is one of the primary challenging functions of battery management systems (BMSs). The battery direct current resistance (DCR) is typically employed for power capability prediction, but its characteristic depends significantly on the ambient temperature. It is essential to investigate systematically the temperature dependence of battery DCR for achieving reliable power capability prediction. Based on a large amount of battery test data, a battery DCR model is proposed for quantitatively describing its temperature dependence. This model is then applied for battery power capability prediction, and the results are verified by experimental results.	en_US
dc.relation.ispartof	2017 20th International Conference on Electrical Machines and Systems, ICEMS 2017	en_US
dc.relation.isbasedon	10.1109/ICEMS.2017.8056426	en_US
dc.title	Experimental analysis and modeling of temperature dependence of lithium-ion battery direct current resistance for power capability prediction	en_US
dc.type	Conference Proceeding
utslib.for	0906 Electrical and Electronic Engineering	en_US
utslib.for	0912 Materials Engineering	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

Abstract:

© 2017 IEEE. Accurate lithium-ion battery power capability prediction gives an indication for managing power flows in or out of batteries within the safe operating area, which is one of the primary challenging functions of battery management systems (BMSs). The battery direct current resistance (DCR) is typically employed for power capability prediction, but its characteristic depends significantly on the ambient temperature. It is essential to investigate systematically the temperature dependence of battery DCR for achieving reliable power capability prediction. Based on a large amount of battery test data, a battery DCR model is proposed for quantitatively describing its temperature dependence. This model is then applied for battery power capability prediction, and the results are verified by experimental results.

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http://hdl.handle.net/10453/126174