A Novel Sensorless Loss Minimization Control Method for High-Speed SPMSM
This paper investigates the efficiency optimization of high-speed surface-mounted permanent magnet synchronous motors (SPMSMs) based on sensorless control. Compared with the Maximum Torque per Ampere control strategy, which minimizes copper losses, the Loss Minimization Control (LMC) method considers both copper and iron losses, achieving global optimization of efficiency. However, its effect is limited by the accuracy of the optimal current model. Given both reliability and efficiency, an improved sensorless LMC method is proposed. Firstly, an improved optimal current model is derived. Unlike traditional models, the improved model excludes electromagnetic torque and does not require additional modules for measurement or estimation, thereby reducing system complexity. Meanwhile, acknowledging the crucial role of estimated rotor position and speed accuracy in the effect of LMC, this study integrates an improved fundamental frequency extractor with a phase-locked loop to process the observed back electromotive force, thus obtaining accurate rotor position and speed. Furthermore, considering the variability in the iron loss resistance of SPMSMs, an iron loss resistance lookup table is devised. Finally, the proposed method is validated on a high-speed SPMSM (15000 r/min) by comparative simulations and experiments.
Authors
- Xusheng Lei (ORCID: https://orcid.org/0000-0001-8186-0245)
- Kun Mao (ORCID: https://orcid.org/0000-0001-5620-959X)
- Shiqiang Zheng (ORCID: https://orcid.org/0000-0002-2842-9699)
- Gao Fei (ORCID: https://orcid.org/0000-0003-0144-2815)
- Zhuang Liu (ORCID: https://orcid.org/0000-0002-7635-8063)
- Jianhua Qu (ORCID: https://orcid.org/0000-0001-8171-9137)
Institutions
- University of Nottingham Ningbo China (CN)
- Beihang University (CN)
Publication Details
- Journal
- IEEE Transactions on Industry Applications
- Published
- 2025-02-20
- DOI
- https://doi.org/10.1109/tia.2025.3543785
- Citations
- 2
- Primary Topic
- Advanced Algorithms and Applications
- Type
- article
- Field-Weighted Citation Impact
- 2.43
Funders
- National Natural Science Foundation of China