Thermally degraded speed estimation of traction machine drive in electric vehicle

Ali, SMN; Hossain, MJ; Wang, D; Mahmud, MAP; Sharma, V; Kashif, M; Kouzani, AZ

Thermally degraded speed estimation of traction machine drive in electric vehicle

Ali, SMN Hossain, MJ Wang, D Mahmud, MAP Sharma, V Kashif, M Kouzani, AZ

Permalink

Publisher:: Institution of Engineering and Technology (IET)
Publication Type:: Journal Article
Citation:: IET Electric Power Applications, 2022, 16, (12), pp. 1464-1475
Issue Date:: 2022-12-01

Open Access

Copyright Clearance Process

Recently Added
In Progress
Open Access

This item is open access.

Adobe PDF

Download full textAdobe PDF (1.79 MB)

View on publisher's site

View statistics

Full metadata record

Field	Value	Language
dc.contributor.author	Ali, SMN
dc.contributor.author	Hossain, MJ
dc.contributor.author	Wang, D
dc.contributor.author	Mahmud, MAP
dc.contributor.author	Sharma, V
dc.contributor.author	Kashif, M
dc.contributor.author	Kouzani, AZ
dc.date.accessioned	2023-05-01T04:05:23Z
dc.date.available	2023-05-01T04:05:23Z
dc.date.issued	2022-12-01
dc.identifier.citation	IET Electric Power Applications, 2022, 16, (12), pp. 1464-1475
dc.identifier.issn	1751-8660
dc.identifier.issn	1751-8679
dc.identifier.uri	http://hdl.handle.net/10453/170159
dc.description.abstract	The speed of an induction machine drive (IMD) in the electrified powertrain of an electric vehicle (EV) suffers from thermal degradation caused by EV loading, driving cycle schedules, EV operating conditions, traffic state and temperature. It is necessary to estimate this thermal degradation in order to design appropriate control methodologies to address this significant issue that directly affects the EV performance. This study proposes a robust linear parameter varying (LPV) observer to estimate this degradation in IMD as well as EV speed under various thermal and loading conditions in steady state and during large transients. The stability and robustness of LPV methodology is ensured by optimal gains of (Formula presented.) control and linear matrix inequalities using convex optimisation techniques. The weighting functions in LPV design are optimised by genetic algorithms. The proposed observer performance is compared with that of conventional sensorless field-oriented control and sliding mode observer. An improved speed performance during EV operation is also presented to validate the robustness of the proposed LPV observer against New European Driving Cycle. The performance analysis is conducted through NI myRIO 1900 controller-based electrical drive set-up.
dc.language	en
dc.publisher	Institution of Engineering and Technology (IET)
dc.relation.ispartof	IET Electric Power Applications
dc.relation.isbasedon	10.1049/elp2.12152
dc.rights	info:eu-repo/semantics/openAccess
dc.subject	0906 Electrical and Electronic Engineering
dc.subject.classification	Electrical & Electronic Engineering
dc.title	Thermally degraded speed estimation of traction machine drive in electric vehicle
dc.type	Journal Article
utslib.citation.volume	16
utslib.for	0906 Electrical and Electronic Engineering
pubs.organisational-group	/University of Technology Sydney
pubs.organisational-group	/University of Technology Sydney/Faculty of Science
utslib.copyright.status	open_access	*
dc.date.updated	2023-05-01T04:05:22Z
pubs.issue	12
pubs.publication-status	Published
pubs.volume	16
utslib.citation.issue	12

Abstract:

The speed of an induction machine drive (IMD) in the electrified powertrain of an electric vehicle (EV) suffers from thermal degradation caused by EV loading, driving cycle schedules, EV operating conditions, traffic state and temperature. It is necessary to estimate this thermal degradation in order to design appropriate control methodologies to address this significant issue that directly affects the EV performance. This study proposes a robust linear parameter varying (LPV) observer to estimate this degradation in IMD as well as EV speed under various thermal and loading conditions in steady state and during large transients. The stability and robustness of LPV methodology is ensured by optimal gains of (Formula presented.) control and linear matrix inequalities using convex optimisation techniques. The weighting functions in LPV design are optimised by genetic algorithms. The proposed observer performance is compared with that of conventional sensorless field-oriented control and sliding mode observer. An improved speed performance during EV operation is also presented to validate the robustness of the proposed LPV observer against New European Driving Cycle. The performance analysis is conducted through NI myRIO 1900 controller-based electrical drive set-up.

Please use this identifier to cite or link to this item:

http://hdl.handle.net/10453/170159