Statistical Simulation of Conductor Lay in Random Windings via X-ray Computed Tomography of Electric Vehicle Stators

Joshua Hoole; Guillaume Remy; Nick Simpson; Mark Williams; Phil H Mellor

doi:10.1109/ECCE53617.2023.10362127

Statistical Simulation of Conductor Lay in Random Windings via X-ray Computed Tomography of Electric Vehicle Stators

Joshua Hoole, Guillaume Remy, Nick Simpson, Mark Williams, Phil H Mellor

Research output: Chapter in Book/Report/Conference proceeding › Conference Contribution (Conference Proceeding)

1 Citation (Scopus)

27 Downloads (Pure)

Abstract

Traditional random windings, with multiple parallel strands-in-hand, provide a potential winding technology to support the mass-electrification of the automotive sector. However, there are challenges when representing the conductor lay achieved within as-manufactured stator windings during AC loss estimations. This paper presents the exploitation of X-ray Computed Tomography of an electric vehicle stator in the development of a conductor lay simulation methodology. Through comparing the AC loss estimated using assumed rigid conductor lays and the conductor lay simulation, it has been found that simplified fixed conductor lays are suitable for estimating winding AC loss variability. However, comparison with the experimentally characterised AC loss variability of the coil groups within an electric vehicle stator has shown future efforts must be focused on enhanced simulation of the intra-turn and inter-turn mixing of parallel strands within stator slots.

Original language	English
Title of host publication	2023 IEEE Energy Conversion Congress and Exposition (ECCE)
Publisher	Institute of Electrical and Electronics Engineers (IEEE)
Pages	3837-3844
Number of pages	8
ISBN (Electronic)	9798350316445
ISBN (Print)	9798350316452
DOIs	https://doi.org/10.1109/ECCE53617.2023.10362127
Publication status	Published - 29 Dec 2023
Event	2023 IEEE Energy Conversion Congress and Exposition (ECCE) - Nashville, United States Duration: 29 Oct 2023 → 2 Nov 2023 https://www.ieee-ecce.org/2023/

Publication series

Name	IEEE Energy Conversion Congress and Exposition, ECCE
Publisher	IEEE
Volume	2023
ISSN (Print)	2329-3721
ISSN (Electronic)	2329-3748

Conference

Conference	2023 IEEE Energy Conversion Congress and Exposition (ECCE)
Country/Territory	United States
City	Nashville
Period	29/10/23 → 2/11/23
Internet address	https://www.ieee-ecce.org/2023/

Bibliographical note

Funding Information:
The X-ray Computed Tomography data presented in this article was acquired through CiMAT at the University of Warwick under EPSRC Project Number (EP/T02593X/1).

Publisher Copyright:
© 2023 IEEE.

Keywords

Computed Tomography
Conductor Lay
AC Loss

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This is the accepted author manuscript (AAM). The final published version (version of record) is available online via IEEE at https://doi.org/10.1109/ECCE53617.2023.10362127. Please refer to any applicable terms of use of the publisher.
Accepted author manuscript, 4.16 MBLicence: CC BY

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Hoole, J., Remy, G., Simpson, N., Williams, M., & Mellor, P. H. (2023). Statistical Simulation of Conductor Lay in Random Windings via X-ray Computed Tomography of Electric Vehicle Stators. In 2023 IEEE Energy Conversion Congress and Exposition (ECCE) (pp. 3837-3844). (IEEE Energy Conversion Congress and Exposition, ECCE; Vol. 2023). Institute of Electrical and Electronics Engineers (IEEE). https://doi.org/10.1109/ECCE53617.2023.10362127

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title = "Statistical Simulation of Conductor Lay in Random Windings via X-ray Computed Tomography of Electric Vehicle Stators",

abstract = "Traditional random windings, with multiple parallel strands-in-hand, provide a potential winding technology to support the mass-electrification of the automotive sector. However, there are challenges when representing the conductor lay achieved within as-manufactured stator windings during AC loss estimations. This paper presents the exploitation of X-ray Computed Tomography of an electric vehicle stator in the development of a conductor lay simulation methodology. Through comparing the AC loss estimated using assumed rigid conductor lays and the conductor lay simulation, it has been found that simplified fixed conductor lays are suitable for estimating winding AC loss variability. However, comparison with the experimentally characterised AC loss variability of the coil groups within an electric vehicle stator has shown future efforts must be focused on enhanced simulation of the intra-turn and inter-turn mixing of parallel strands within stator slots.",

keywords = "Computed Tomography, Conductor Lay, AC Loss",

author = "Joshua Hoole and Guillaume Remy and Nick Simpson and Mark Williams and Mellor, {Phil H}",

note = "Funding Information: The X-ray Computed Tomography data presented in this article was acquired through CiMAT at the University of Warwick under EPSRC Project Number (EP/T02593X/1). Publisher Copyright: {\textcopyright} 2023 IEEE.; 2023 IEEE Energy Conversion Congress and Exposition (ECCE) ; Conference date: 29-10-2023 Through 02-11-2023",

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Hoole, J, Remy, G, Simpson, N, Williams, M & Mellor, PH 2023, Statistical Simulation of Conductor Lay in Random Windings via X-ray Computed Tomography of Electric Vehicle Stators. in 2023 IEEE Energy Conversion Congress and Exposition (ECCE). IEEE Energy Conversion Congress and Exposition, ECCE, vol. 2023, Institute of Electrical and Electronics Engineers (IEEE), pp. 3837-3844, 2023 IEEE Energy Conversion Congress and Exposition (ECCE), Nashville, Tennessee, United States, 29/10/23. https://doi.org/10.1109/ECCE53617.2023.10362127

Statistical Simulation of Conductor Lay in Random Windings via X-ray Computed Tomography of Electric Vehicle Stators. / Hoole, Joshua; Remy, Guillaume; Simpson, Nick et al.
2023 IEEE Energy Conversion Congress and Exposition (ECCE). Institute of Electrical and Electronics Engineers (IEEE), 2023. p. 3837-3844 (IEEE Energy Conversion Congress and Exposition, ECCE; Vol. 2023).

Research output: Chapter in Book/Report/Conference proceeding › Conference Contribution (Conference Proceeding)

TY - GEN

T1 - Statistical Simulation of Conductor Lay in Random Windings via X-ray Computed Tomography of Electric Vehicle Stators

AU - Hoole, Joshua

AU - Remy, Guillaume

AU - Simpson, Nick

AU - Williams, Mark

AU - Mellor, Phil H

N1 - Funding Information: The X-ray Computed Tomography data presented in this article was acquired through CiMAT at the University of Warwick under EPSRC Project Number (EP/T02593X/1). Publisher Copyright: © 2023 IEEE.

PY - 2023/12/29

Y1 - 2023/12/29

N2 - Traditional random windings, with multiple parallel strands-in-hand, provide a potential winding technology to support the mass-electrification of the automotive sector. However, there are challenges when representing the conductor lay achieved within as-manufactured stator windings during AC loss estimations. This paper presents the exploitation of X-ray Computed Tomography of an electric vehicle stator in the development of a conductor lay simulation methodology. Through comparing the AC loss estimated using assumed rigid conductor lays and the conductor lay simulation, it has been found that simplified fixed conductor lays are suitable for estimating winding AC loss variability. However, comparison with the experimentally characterised AC loss variability of the coil groups within an electric vehicle stator has shown future efforts must be focused on enhanced simulation of the intra-turn and inter-turn mixing of parallel strands within stator slots.

AB - Traditional random windings, with multiple parallel strands-in-hand, provide a potential winding technology to support the mass-electrification of the automotive sector. However, there are challenges when representing the conductor lay achieved within as-manufactured stator windings during AC loss estimations. This paper presents the exploitation of X-ray Computed Tomography of an electric vehicle stator in the development of a conductor lay simulation methodology. Through comparing the AC loss estimated using assumed rigid conductor lays and the conductor lay simulation, it has been found that simplified fixed conductor lays are suitable for estimating winding AC loss variability. However, comparison with the experimentally characterised AC loss variability of the coil groups within an electric vehicle stator has shown future efforts must be focused on enhanced simulation of the intra-turn and inter-turn mixing of parallel strands within stator slots.

KW - Computed Tomography

KW - Conductor Lay

KW - AC Loss

U2 - 10.1109/ECCE53617.2023.10362127

DO - 10.1109/ECCE53617.2023.10362127

M3 - Conference Contribution (Conference Proceeding)

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T3 - IEEE Energy Conversion Congress and Exposition, ECCE

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BT - 2023 IEEE Energy Conversion Congress and Exposition (ECCE)

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ER -

Hoole J, Remy G, Simpson N, Williams M, Mellor PH. Statistical Simulation of Conductor Lay in Random Windings via X-ray Computed Tomography of Electric Vehicle Stators. In 2023 IEEE Energy Conversion Congress and Exposition (ECCE). Institute of Electrical and Electronics Engineers (IEEE). 2023. p. 3837-3844. (IEEE Energy Conversion Congress and Exposition, ECCE). doi: 10.1109/ECCE53617.2023.10362127

Statistical Simulation of Conductor Lay in Random Windings via X-ray Computed Tomography of Electric Vehicle Stators

Abstract

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Bibliographical note

Keywords

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