Multiobjective Optimal Sizing of Hybrid Energy Storage System for Electric Vehicles

Zhang, L; Hu, X; Wang, Z; Sun, F; Deng, J; Dorrell, DG

Multiobjective Optimal Sizing of Hybrid Energy Storage System for Electric Vehicles

Zhang, L

Hu, X Wang, Z Sun, F Deng, J Dorrell, DG

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Publication Type:: Journal Article
Citation:: IEEE Transactions on Vehicular Technology, 2018, 67 (2), pp. 1027 - 1035
Issue Date:: 2018-02-01

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Field	Value	Language
dc.contributor.author	Zhang, L https://orcid.org/0000-0002-1763-0397	en_US
dc.contributor.author	Hu, X	en_US
dc.contributor.author	Wang, Z	en_US
dc.contributor.author	Sun, F	en_US
dc.contributor.author	Deng, J	en_US
dc.contributor.author	Dorrell, DG https://orcid.org/0000-0001-8060-3386	en_US
dc.date.issued	2018-02-01	en_US
dc.identifier.citation	IEEE Transactions on Vehicular Technology, 2018, 67 (2), pp. 1027 - 1035	en_US
dc.identifier.issn	0018-9545	en_US
dc.identifier.uri	http://hdl.handle.net/10453/132249
dc.description.abstract	© 2017 IEEE. Energy storage system (ESS) is an essential component of electric vehicles, which largely affects their driving performance and manufacturing cost. A hybrid energy storage system (HESS) composed of rechargeable batteries and ultracapacitors shows a significant potential for maximally exploiting their complementary characteristics. This study focuses on optimal HESS sizing of an example electric vehicle using a multi-objective optimization algorithm, with the overarching goal of reducing the ESS cost while prolonging battery life. To this end, a battery state-of-health model is incorporated to quantitatively investigate the impact of component sizing on battery life. The wavelet-transform-based power management algorithm is adopted to realize the power coordination between the batteries and ultracapacitors, in which the ultracapacitors are responsible for handling high-frequency power transients, whereas the batteries deal with average power leveling. The Urban Dynamometer Driving Schedule is used to represent real power demands.	en_US
dc.relation.ispartof	IEEE Transactions on Vehicular Technology	en_US
dc.relation.isbasedon	10.1109/TVT.2017.2762368	en_US
dc.rights	info:eu-repo/semantics/closedAccess
dc.subject.classification	Automobile Design & Engineering	en_US
dc.title	Multiobjective Optimal Sizing of Hybrid Energy Storage System for Electric Vehicles	en_US
dc.type	Journal Article
utslib.citation.volume	2	en_US
utslib.citation.volume	67	en_US
utslib.for	0902 Automotive Engineering	en_US
utslib.for	0906 Electrical and Electronic Engineering	en_US
utslib.for	08 Information and Computing Sciences	en_US
utslib.for	09 Engineering	en_US
utslib.for	10 Technology	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
utslib.copyright.status	closed_access	*
pubs.issue	2	en_US
pubs.publication-status	Published	en_US
pubs.volume	67	en_US

Abstract:

© 2017 IEEE. Energy storage system (ESS) is an essential component of electric vehicles, which largely affects their driving performance and manufacturing cost. A hybrid energy storage system (HESS) composed of rechargeable batteries and ultracapacitors shows a significant potential for maximally exploiting their complementary characteristics. This study focuses on optimal HESS sizing of an example electric vehicle using a multi-objective optimization algorithm, with the overarching goal of reducing the ESS cost while prolonging battery life. To this end, a battery state-of-health model is incorporated to quantitatively investigate the impact of component sizing on battery life. The wavelet-transform-based power management algorithm is adopted to realize the power coordination between the batteries and ultracapacitors, in which the ultracapacitors are responsible for handling high-frequency power transients, whereas the batteries deal with average power leveling. The Urban Dynamometer Driving Schedule is used to represent real power demands.

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http://hdl.handle.net/10453/132249