Loss Minimization Control for High-Speed SPMSM Based on Magnetizing Current Observation and Iron Loss Resistance Online Estimation
Conventional Loss Minimization Control (LMC) strategies can reduce electrical losses of high-speed Surface-mounted Permanent Magnet Synchronous Motors (SPMSMs) by regulating the d-axis current to its optimal value. However, their performance is limited by the accuracy of the optimal current calculated based on fixed model parameters. Considering that some model parameters are prone to change during the operation of high-speed SPMSMs, this paper proposes an improved LMC strategy based on online parameter estimation. Firstly, the impact of model parameter variations on electrical losses and optimal current is analyzed. Secondly, lookup tables for stator resistance and synchronous inductance, along with a super-twisting sliding mode observer, are designed for the estimation of dq-axis magnetizing currents, which are used to estimate the iron loss resistance. Subsequently, addressing the issue of increased system complexity due to the introduction of electromagnetic torque in traditional LMC strategies, this paper directly adopts the optimal magnetizing current as the reference and designs an improved LMC strategy using the observed magnetizing current as feedback. Finally, the effectiveness of the proposed method is validated through experiments on a high-speed magnetic levitation air spinning machine, demonstrating significant reduction in electrical losses and improved efficiency.
Authors
- Di Wang (ORCID: https://orcid.org/0000-0001-5713-3064)
- Kun Mao (ORCID: https://orcid.org/0000-0001-5620-959X)
- Shiqiang Zheng (ORCID: https://orcid.org/0000-0002-2842-9699)
- Zhuang Liu (ORCID: https://orcid.org/0000-0002-7635-8063)
- Chong Zhou (ORCID: https://orcid.org/0000-0002-3697-1701)
Institutions
- Beihang University (CN)
Publication Details
- Journal
- IEEE Transactions on Power Electronics
- Published
- 2025-06-09
- DOI
- https://doi.org/10.1109/tpel.2025.3577729
- Citations
- 1
- Primary Topic
- Sensorless Control of Electric Motors
- Type
- article
- Field-Weighted Citation Impact
- 0.73
Funders
- National Natural Science Foundation of China