Implementation of an Improved Motor Control for Electric Vehicles

Men, X; Guo, Y; Wu, G; Chen, S; Shi, C

Implementation of an Improved Motor Control for Electric Vehicles

Men, X Guo, Y

Wu, G Chen, S Shi, C

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Publisher:: MDPI
Publication Type:: Journal Article
Citation:: Energies, 2022, 15, (13)
Issue Date:: 2022-07-01

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Field	Value	Language
dc.contributor.author	Men, X
dc.contributor.author	Guo, Y https://orcid.org/0000-0001-6182-0684
dc.contributor.author	Wu, G
dc.contributor.author	Chen, S
dc.contributor.author	Shi, C
dc.date.accessioned	2023-03-09T04:53:20Z
dc.date.available	2023-03-09T04:53:20Z
dc.date.issued	2022-07-01
dc.identifier.citation	Energies, 2022, 15, (13)
dc.identifier.issn	1996-1073
dc.identifier.issn	1996-1073
dc.identifier.uri	http://hdl.handle.net/10453/166791
dc.description.abstract	Electric vehicles are regarded as a significant way to mitigate the global energy crisis and the environmental pollution problem. Motor control is a very important part for electric vehicles. As for hardware, a motor controller usually has components such as a power module, microprocessor unit, IGBT driver, sensors, and resolver-to-digital convertor. As for software, a field-oriented control (FOC) with space vector pulse width modulation (SVPWM) is a popular method, while model predictive control (MPC) has recently shown great potential in motor drives. In this paper, both FOC and MPC are discussed and the performances are compared based on experiments. As the implementation is on a digital processor, the discretization and normalization are addressed, and the flux observer and speed estimation are discussed. Some practical issues for implementation are also talked about, such as field weakening control, overmodulation, etc. This paper focuses on how to implement the improved motor control for electric vehicles as industrial applications. The steady-state torque performances of this motor controller are verified by motor test-bench experiments. MPC shows as good performance as FOC in these experiments.
dc.language	English
dc.publisher	MDPI
dc.relation.ispartof	Energies
dc.relation.isbasedon	10.3390/en15134833
dc.rights	info:eu-repo/semantics/openAccess
dc.subject	02 Physical Sciences, 09 Engineering
dc.title	Implementation of an Improved Motor Control for Electric Vehicles
dc.type	Journal Article
utslib.citation.volume	15
utslib.for	02 Physical Sciences
utslib.for	09 Engineering
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	open_access	*
dc.date.updated	2023-03-09T04:53:16Z
pubs.issue	13
pubs.publication-status	Published
pubs.volume	15
utslib.citation.issue	13

Abstract:

Electric vehicles are regarded as a significant way to mitigate the global energy crisis and the environmental pollution problem. Motor control is a very important part for electric vehicles. As for hardware, a motor controller usually has components such as a power module, microprocessor unit, IGBT driver, sensors, and resolver-to-digital convertor. As for software, a field-oriented control (FOC) with space vector pulse width modulation (SVPWM) is a popular method, while model predictive control (MPC) has recently shown great potential in motor drives. In this paper, both FOC and MPC are discussed and the performances are compared based on experiments. As the implementation is on a digital processor, the discretization and normalization are addressed, and the flux observer and speed estimation are discussed. Some practical issues for implementation are also talked about, such as field weakening control, overmodulation, etc. This paper focuses on how to implement the improved motor control for electric vehicles as industrial applications. The steady-state torque performances of this motor controller are verified by motor test-bench experiments. MPC shows as good performance as FOC in these experiments.

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http://hdl.handle.net/10453/166791