Moving average filter-based model predictive control for electric vehicles bidirectional chargers

He, T; Lu, D; Zheng, L; Zhu, J

Moving average filter-based model predictive control for electric vehicles bidirectional chargers

He, T Lu, D

Zheng, L Zhu, J

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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	He, T	en_US
dc.contributor.author	Lu, D https://orcid.org/0000-0003-2145-0580	en_US
dc.contributor.author	Zheng, L	en_US
dc.contributor.author	Zhu, J https://orcid.org/0000-0002-9763-4047	en_US
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/10453/126235
dc.description.abstract	© 2017 IEEE. The paper proposes a moving average filter (MAF)-based model predictive control (MPC) for the electric vehicles (EVs) bidirectional chargers. Grid virtual flux is used to estimate the grid voltage through a low pass filter (LPF). An MAF, which acts as an ideal LPF, can eliminate the effect of unbalanced/distorted grid voltage and unknown characteristic harmonics. Both the two-cascaded LPF-based and the proposed MAF-based MPC strategies can achieve bi-directional power flow for EV batteries. Compared with the system results obtained from the two-cascaded LPF based MPC algorithm, the proposed control method can improve the system performance by reducing the current total harmonic distortion under a balance/unbalance grid voltage. The reactive power performance can be improved when the active power reference varies.	en_US
dc.relation.ispartof	2017 20th International Conference on Electrical Machines and Systems, ICEMS 2017	en_US
dc.relation.isbasedon	10.1109/ICEMS.2017.8056380	en_US
dc.rights	info:eu-repo/semantics/closedAccess
dc.title	Moving average filter-based model predictive control for electric vehicles bidirectional chargers	en_US
dc.type	Conference Proceeding
utslib.for	0906 Electrical and Electronic Engineering	en_US
utslib.for	090203 Automotive Mechatronics	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/Strength - CCET - Centre for Clean Energy Technology
pubs.organisational-group	/University of Technology Sydney/Students
utslib.copyright.status	closed_access	*
pubs.publication-status	Published	en_US

Abstract:

© 2017 IEEE. The paper proposes a moving average filter (MAF)-based model predictive control (MPC) for the electric vehicles (EVs) bidirectional chargers. Grid virtual flux is used to estimate the grid voltage through a low pass filter (LPF). An MAF, which acts as an ideal LPF, can eliminate the effect of unbalanced/distorted grid voltage and unknown characteristic harmonics. Both the two-cascaded LPF-based and the proposed MAF-based MPC strategies can achieve bi-directional power flow for EV batteries. Compared with the system results obtained from the two-cascaded LPF based MPC algorithm, the proposed control method can improve the system performance by reducing the current total harmonic distortion under a balance/unbalance grid voltage. The reactive power performance can be improved when the active power reference varies.

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http://hdl.handle.net/10453/126235