This article presents a low-measurement effort and less storage space but an effective method to reduce the torque ripple. First, a torque-balanced measurement method is presented to obtain the flux-linkage characteristics at four torque-balanced positions. Then, a four-order Fourier series model is proposed to describe the entire flux-linkage and torque characteristics. To reduce the storage space, a polynomial-Fourier series model is proposed to describe the torque and current model from the rotor position and flux linkage. Based on the proposed polynomial-Fourier series model, a novel model predictive torque control (MPTC) is implemented to minimize the torque ripple with flux-linkage-based torque estimation. Experimental results show that the proposed method can effectively reduce torque ripple with lower measurement effort and less storage space compared with the traditional MPTC method. The proposed method provides a low effort and convenient way to implement the advanced control of SRMs in the industry application.

This article presents a novel model predictive method for torque ripple and source current ripple suppression in switched reluctance machines (SRMs) when the bus voltage fluctuates. Based on the measured rotor position, phase current, and phase voltage, the states, including bus voltage, capacitor current, and phase flux-linkage, are calculated for the preparation of the prediction. On this basis, the machine torque and inverter current for all the possible switching states are predicted to construct the cost function. The operating state with the minimum value of cost function is considered as the optimal switch signal and applied to the control power converter, thus achieving the effect of simultaneous reduction of torque ripple and source current ripple. Experimental results show the proposed method has a significant effect on the suppression of both torque ripple and source current ripple even with bus voltage fluctuation, which verifies the effectiveness of the proposed method. The influence of the key factors, such prediction horizon, parametric variation, and weight coefficient, on the machine performance are further investigated. The proposed method is easy to be implemented in the control logic as well as robust to bus voltage fluctuations, enabling to promote the competitiveness of SRMs in electric vehicle application.

For the switched reluctance machine (SRM) with a heavy load, the inductance of the conduction phase is nonlinear, which makes the sensorless control method based on the linear region of the phase inductance no longer applicable. Meanwhile, in the case of light or no load, the estimated rotor position will abruptly change at the connection points between neighboring segments, which has an impact on the estimation of the speed. In this article, a low-speed sensorless control method for the SRM with a heavy load is proposed. The idle-phase inductance is modeled over a wide rotor position range for position estimation, and a proportional gain decreasing type second-order generalized integrator based on a frequency-locked loop is constructed to estimate the speed while performing rotor position filtering. The effectiveness of the proposed method is experimentally verified with a 3-phase 12/8-pole SRM prototype, and the results show that the estimated rotor position and speed are accurate and smooth, which is beneficial to performance improvement of the sensorless control.

For synchronous reluctance motor (SynRM), parameters tends to change when the magnetic is saturated, and the characteristics are significantly nonlinear. The conventional maximum torque per ampere (MTPA) control of SynRM, which ignores the deviation terms, is subject to significant errors about current angle and leads to torque degradation. For the saturation of magnetic, relying only on finite element simulation, the accuracy of motor parameters is still not guaranteed in practical applications. In this study, an online approach of inductance parameters determination that can account for magnetic self-saturation and cross-saturation was proposed. To achieve high-precision maximum torque per ampere (MTPA) control of SynRM concerning torque degradation, this article first analyzes the factors contributing to the MTPA control error of SynRM. It then combines the identification of SynRM inductance parameters to compensate for the deviation terms online and applies the virtual signal injection method. Simulation and experimental results verify the effectiveness of the proposed parameter identification approach and high-precision MTPA control strategy considering deviation terms.

In this paper, a position sensorless method for 12/8 switched reluctance machines (SRM) is proposed. To inject high-frequency pulses into the inductance linear region of the idle phase, a pulse injection strategy based on the inductance threshold of the active phase is proposed in the paper. A simple linear model is established to describe the relationship between the unsaturated inductance and the position, and the rotor position is further reconstructed. The simulation results verify that the proposed method can effectively estimate the rotor position. The method in this paper requires low measurement and is suitable for low-speed operation.