Abstract

A novel 6/13-pole hybrid excitation axial field flux-switching permanent magnet machine (HEAFFSPMM) exhibits strong fault tolerance capability, high efficiency, and large torque density. However, merely few research on speed sensorless control in HEAFFSPMM exists. The speed sensorless control methods based on model reference adaptive system (MRAS) are studied and compared for the machine to improve the stability and reliability of the system and consequently improve the application of machine in control system. Based on the field-oriented control strategy, the MRAS observer of speed is designed and built by applying stator currents, stator flux linkages, and simplified stator currents. The three speed sensorless control algorithms of MRAS are compared and analyzed by using MATLAB/Simulink simulation and dSPACE1104 experimental platform. Results show that the speed sensorless control algorithm based on simplified stator currents has good control performance and high control accuracy.

Highlights

  • Hybrid excitation axial field flux-switching permanent magnet machine (HEAFFSPMM) exhibits large torque/power density, wide operation range, and high efficiency [1], [2]

  • In this paper, the three speed sensorless control methods of model reference adaptive system (MRAS) based on stator currents, stator flux linkages, and simplified stator currents are studied and compared

  • The simulation and experiment are conducted under HEAFFSPMM control system

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Summary

INTRODUCTION

Hybrid excitation axial field flux-switching permanent magnet machine (HEAFFSPMM) exhibits large torque/power density, wide operation range, and high efficiency [1], [2]. This machine has remarkable application prospects in the field of electric vehicles. Sun et al [15] estimated rotor speed by an artificial neural network inverse speed observer which was characterized by wide speed range, fast response speed, and strong robustness, the algorithm structure was complex and required high controller. 3 2 p id iq, and pMf if iq are the permanent magnetic, reluctance, and excitation torque, respectively

PRINCIPLE OF MRAS
Ld ud d dt iq
MRAS SIMPLIFICATION ALGORITHM FOR STATOR CURRENTS
SIMULATION ANALYSIS
COMPARISON OF THREE METHODS UNDER LOAD
Findings
CONCLUSION

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