Loss Minimization Control of SPMSM Based on Virtual Signal Injection

The key to optimizing the efficiency of Permanent Magnet Synchronous Motors (PMSMs) lies in how to obtain accurate and stable loss minimization criteria without solving complex loss equations. This paper first compensates for inverter nonlinear voltage error and estimates motor parameters including iron loss resistance, <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">d-q</i>-axis inductance, and permanent magnet flux linkage, thereby reducing the impact of parameter variations on loss power estimation. Then takes the id as the signal injection target, uses the difference in motor loss before and after signal injection as the loss minimization control (LMC) criterion, and proposes an LMC method for Surface Permanent Magnet Synchronous Motor (SPMSM). The LMC criterion of the proposed method is accurate and stable, which avoids solving complex loss models and reduces the computational burden. Experimental results show that the proposed method can improve motor efficiency under different operating conditions by up to 1.32% compared to the Maximum Torque Per Ampere (MTPA) control method.

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Publication Details

Journal
IEEE Transactions on Transportation Electrification
Published
2025-09-01
DOI
https://doi.org/10.1109/tte.2025.3604484
Primary Topic
Advanced Algorithms and Applications
Type
article
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article

Loss Minimization Control of SPMSM Based on Virtual Signal Injection

Yongpeng Shen, Yakai Ge, Bo Zhou, Zhiwei Chen
IEEE Transactions on Transportation Electrification
Advanced Algorithms and Applications
article

Loss Minimization Control of SPMSM Based on Virtual Signal Injection

Yongpeng Shen, Yakai Ge, Bo Zhou, Zhiwei Chen
article en

Abstract

The key to optimizing the efficiency of Permanent Magnet Synchronous Motors (PMSMs) lies in how to obtain accurate and stable loss minimization criteria without solving complex loss equations. This paper first compensates for inverter nonlinear voltage error and estimates motor parameters including iron loss resistance, <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">d-q</i>-axis inductance, and permanent magnet flux linkage, thereby reducing the impact of parameter variations on loss power estimation. Then takes the id as the signal injection target, uses the difference in motor loss before and after signal injection as the loss minimization control (LMC) criterion, and proposes an LMC method for Surface Permanent Magnet Synchronous Motor (SPMSM). The LMC criterion of the proposed method is accurate and stable, which avoids solving complex loss models and reduces the computational burden. Experimental results show that the proposed method can improve motor efficiency under different operating conditions by up to 1.32% compared to the Maximum Torque Per Ampere (MTPA) control method.

IEEE Transactions on Transportation ElectrificationVol. 11(6)
Zhengzhou University of Light Industry (CN)
Openalex Percentile: Top 72%
Advanced Algorithms and Applications
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Loss Minimization Control of SPMSM Based on Virtual Signal Injection — Yongpeng Shen, Yakai Ge, et al. · IEEE Transactions on Transportation Electrification (2025) | TGRS Research Map | TGRS