Optimized Virtual Vector Technique With Loss Minimization of Six Phase IM Under Low-Speed Operation for Electric Vehicles
The model-based predictive control (MPC) has gained popularity in the field of multiphase drives because of its high degree of flexibility and its robust dynamic response. However, the performance of asymmetrical six-phase machine (A6M) with low secondary subspace impedance has been reported to suffer from poor current quality under conventional MPC. Although the virtual voltage vectors (VVVs) concept has been proposed to address this issue, the current waveforms still exhibit high ripple content in the low-speed operating region. Although existing solutions in the literature manage to optimize the VVV amplitude based on loading conditions, they fall short in addressing the low-speed region with light/high loading conditions. The proposed technique in this article aims to improve the machine performance under low speeds over the full loading range by synthesizing the so-called optimized VVVs (OVVVs). These OVVV are synthesized with variable amplitude based on the constant flux scenario (CFS). The same concept is also extended to enhance the drive efficiency by adopting the variable flux scenario (VFS) in low-speed light-loading region. The proposed OVVV approaches show a significant enhancement in terms of current quality, current tracking, losses, and torque ripple reduction as well as increased loading capability. The effectiveness of the proposed OVVV approach for different operating conditions is experimentally validated using a 1.5 hp prototype A6M.
Authors
- Wessam E. Abdel-Azim (ORCID: https://orcid.org/0000-0001-5607-0313)
- Shehab Ahmed (ORCID: https://orcid.org/0000-0003-0073-8745)
- Ayman Samy Abdel-Khalik (ORCID: https://orcid.org/0000-0001-5162-4954)
- Mohamed G. Abdel-Moneim (ORCID: https://orcid.org/0000-0001-7615-352X)
- Shady Gadoue (ORCID: https://orcid.org/0000-0001-6786-3933)
- Abdullah Shawier (ORCID: https://orcid.org/0000-0003-1910-1242)
Institutions
- Queen Mary University of London (GB)
- King Abdullah University of Science and Technology (SA)
- Alexandria University (EG)
Publication Details
- Journal
- IEEE Transactions on Transportation Electrification
- Published
- 2024-11-04
- DOI
- https://doi.org/10.1109/tte.2024.3489954
- Citations
- 4
- Primary Topic
- Engineering Applied Research
- Type
- article
- Field-Weighted Citation Impact
- 1.04