Cardiff University | Prifysgol Caerdydd ORCA
Online Research @ Cardiff 
WelshClear Cookie - decide language by browser settings

High-resolution rotor-position detection for green vehicle drives at halt condition with statistical view

Al Ibraheemi, Mazen M. A., Anayi, Fatih J., Radhy, Zainb Hassan and Al Ibraheemi, Hayder 2021. High-resolution rotor-position detection for green vehicle drives at halt condition with statistical view. TELKOMNIKA (Telecommunication, Computing, Electronics and Control) 19 (3) , pp. 983-990. 10.12928/telkomnika.v19i3.18794

[thumbnail of 18794-53816-1-PB.pdf] PDF - Published Version
Available under License Creative Commons Attribution Share Alike.

Download (877kB)

Abstract

Considerations around environmental pollution and green energy usage have led to environmentally-friendly machines being used in many industrial applications. Permanent magnet (PM) machines are the best solution to substitute the pollutant diesel-powered machines. In such machines, rotor position detection is crucial for safe startup operating. Meanwhile, encoderless controllers have become more reliable, over the years, in supporting the operation of PM machines. The key point, presented by this paper, is to introduce an improved positioning model to detect the rotor-position of interior permanent magnet synchronous machine at halt condition. To verify this objective, only two short duration pulses were injected into the stator windings. Then, the corresponding terminal voltage and current responses were measured and employed to create two memory address lines. Thereby, the memory cells, which contain the rotor position information, could be accessed. This detection model makes a significant improvement in rotor positioning detection of high resolution (1 degree) which represents lower value than most verified results in literature. The model was simulated and tested in a MATLAB/Simulink environment and shows an approximate accuracy 95%. Additionally, the statistical analysis was also employed to support the work outcomes.

Item Type: Article
Date Type: Publication
Status: Published
Schools: Engineering
Publisher: Universitas Ahmad Dahlan / Institute of Advanced Engineering and Science / Ahmad Dahlan University
ISSN: 1693-6930
Date of First Compliant Deposit: 7 May 2021
Date of Acceptance: 25 November 2020
Last Modified: 07 May 2021 12:45
URI: https://orca.cardiff.ac.uk/id/eprint/140966

Actions (repository staff only)

Edit Item Edit Item

Downloads

Downloads per month over past year

View more statistics