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Flux-weakening fuzzy adaptive ST-SMO sensorless control algorithm for PMSM in EV

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Abstract

Aiming at the sensorless control system of Permanent Magnet Synchronous Motor (PMSM) for the pure Electric Vehicle (EV), the issues of slow convergence, phase delay, and chattering caused by Sliding Mode Observer (SMO), a single current flux weakening fuzzy adaptive Super-Twisting (ST) SMO algorithm is proposed in this paper. The fuzzy adaptive algorithm is used to estimate the uncertain boundary and adaptively adjust sliding mode gain, which is used in the ST algorithm to accelerate the convergence speed of sliding mode gain, and eliminate the system delay caused by phase-locked loop and phase compensation, and improve the estimation accuracy of speed and rotor position. The single current flux weakening is used to improve the issues of d-q axis current coupling and enhance the load capacity of the field weakening area of the PMSM. The algorithm is verified by MATLAB/Simulink. The simulation results show that the single current flux-weakening fuzzy ST-SMO algorithm has faster convergence speed in the variable speed and the variable load of PMSM sensorless control system, which has significantly reduced chattering, obtained more accurate speed and rotor position, and has better dynamic response and robustness, and has widened the speed region of PMSM.

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Acknowledgements

This work was supported by the sub-project of the National Key Research and Development Program (2016YFD0700103-02) and the National Natural Science Foundation of China (51675163).

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Authors and Affiliations

  1. Vehicle and Transportation Engineering Institute, Henan University of Science and Technology, Luoyang, 471000, China

    Yongwei Wang, Jingbo Wu, Zhijun Guo, Chengwei Xie, Junjie Liu & Xin Jin

  2. Henan Key Laboratory of Automobile Energy Saving and New Energy, Luoyang, 471000, China

    Jingbo Wu

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  1. Yongwei Wang
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Correspondence to Yongwei Wang.

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Wang, Y., Wu, J., Guo, Z. et al. Flux-weakening fuzzy adaptive ST-SMO sensorless control algorithm for PMSM in EV. J Supercomput 78, 10930–10949 (2022). https://doi.org/10.1007/s11227-021-04264-8

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  • DOI: https://doi.org/10.1007/s11227-021-04264-8

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