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To improve the torque accuracy for open-end winding permanent magnet synchronous motor (OEW-PMSM) with common dc bus, it is necessary to eliminate the zero-sequence torque (ZST) caused by zero-sequence current (ZSC) and the third back electromotive force. But the third flux linkage mismatch can enlarge ZSC which causes torque ripple. To deal with the above problem, firstly, a q-axis current injection method combined with deadbeat predictive current control (DPCC) is adopted to inject an extra current into the reference q-axis current. The extra torque generated by this injected current can compensate for ZST. Secondly, to ensure the accuracy of the q-axis injected current, a parameter identification method based on recursive least square (RLS) algorithm with forgetting factor is presented to identify the third flux linkage accurately. The identification value can reduce ZSC and torque ripple under different working conditions. And the accurate q-axis injected current can be obtained to counteract ZST and output accurate torque. Finally, three methods are comparatively analyzed in this paper, namely the traditional DPCC method, the DPCC method with q-axis current injection and the proposed DPCC method based on RLS with forgetting factor. The simulation and experimental results verify the effectiveness of the proposed DPCC method. [ABSTRACT FROM AUTHOR]

Deadbeat predictive current control (DPCC) can predict motor behavior based on SPMSM model. However, during the operation of motor system, motor parameters (such as stator inductance and flux linkage) vary frequently according to different working conditions, which may lead to controller parameter mismatch, causing current harmonic content to increase and efficiency to decrease. In order to solve these problems caused by parameter variation, first, this paper proposes a current prediction error model by considering uncertainties of model parameters. Second, stator inductance and flux linkage are decoupled based on current prediction error model, which can reduce the interaction between parameters. Finally, the Kalman Filter (KF) algorithm is presented to filter the decoupled parameters. It is shown that the stator inductance and flux linkage can be identified accurately and the complexity of computation can be simplified. The traditional DPCC method, Extended Kalman Filter (EKF) based DPCC method and the proposed DPCC method are comparatively analyzed in this paper. Simulation and experiment indicate that the proposed parameter decoupling identification method can effectively reduce current harmonic content, current fluctuation and current tracking errors caused by parameter mismatch. [ABSTRACT FROM AUTHOR]

Finite-set model predictive current control (FS-MPCC) has been employed for machine drives owing to the good dynamic performance. Sensitivity to parameter variation is one of the main barriers to its widespread application. To overcome this barrier, this article proposes an improved FS-MPCC for surfaced permanent magnet synchronous machines (SPMSMs). The contribution of this article is developing a novel current update mechanism, where the variation of resistance, rotor flux linkage, and inductance is considered, in which a modified coordinate system that contains a stationary and rotary axis frame is introduced. In this case, the predicted parameter can be obtained more accurately based on this mechanism, which can suppress the disturbances caused by model parameter mismatch. In addition, SPMSM parameter mismatch effect on FS-MPCC performance is analyzed in this article, which testifies that resistance mismatch influence on current prediction error can be neglected in practical system. The proposed FS-MPCC is validated and compared with different state-of-the-art methods. Improvement of the proposed control strategy is validated by means of experimental results on a 2-kW test rig. [ABSTRACT FROM AUTHOR]

In order to suppress the torque ripple of an open-end winding permanent-magnet synchronous machine (OEW-PMSM), a q-axis current injection method is typically employed but the disturbances caused by the third flux linkage parameter mismatch can augment the torque ripple. To solve this problem, in this paper, first, a zero-sequence current (ZSC) and zero-sequence back-EMF observer (ZCBO) are proposed based on an adaptive sliding mode control, which is able to simultaneously estimate ZSC and zero-sequence back EMF under varying conditions. Based on this ZCBO, the estimated ZSC and zero-sequence back EMF could compensate for the one-step control delay and suppress the aforementioned disturbances. Second, although central hexagon modulation is employed in the predictive current control (PCC) scheme, zero-sequence voltage (ZSV) still exists in the zero-sequence path due to the dead time of the inverter, affecting the predictive accuracy of the ZCBO. To accurately establish the ZCBO, the ZSV caused by the dead time of the inverter is considered in the ZCBO. Finally, a comparative study of two types of methods is presented, and simulations established by MATLAB software and experimental confirmations are carried out to verify the effectiveness of the proposed PCC scheme in this paper. [ABSTRACT FROM AUTHOR]

The recent trend in the design of the high-power density power converter generally reduces the rate of the device cooling process. As a result, increased thermal coupling among devices exists. Based on measurements, a thermal coupling resistances network (TCRN) model is proposed in this article. Considering different spacings and current values at a fixed value of case temperature (${T} _{\text {c}}$), the relationships between the case-to-ambient thermal resistance (${R} _{\text {ca}}$) of individual power devices and their thermal coupling resistance (${R} _{\text {cp}}$) to the adjacent device are established. The close correspondence of ${T} _{\text {c}}$ from the calculation of the different spacing and experimental results obtained from a thermal coupling measurement platform confirms the established TCRN model and the relationships. Traditional thermal models do not consider the changes of ${R} _{\text {ca}}$ and also ignore the effect of thermal coupling among the adjacent devices. Compared with these models, the proposed thermal resistances modeling approach provides a better understanding of the thermal behavior of power devices. [ABSTRACT FROM AUTHOR]

The efficiency performance of multi-motor-driven system highly depends on the power management. Three aspects of contribution have been made in this study. 1) A predictive power management for a DMPS is developed. To improve the performance of the predictive power management, an adaptive velocity predictor is proposed and the coefficients of proposed predictor can update its parameters according to identified driving patterns. Simulation results show that the new velocity predictor have best prediction performance compared with traditional predictors. 2) A neural network based power management is proposed. According to the optimization results of dynamic programming, radial-basis-function neural network is trained. The input dimensions and the number of hidden layer neurons of the neural network are optimized. 3) The performance of proposed control strategies are compared with three different drive cycles including MANHATTAN cycle, Japanese 1015 cycle, and UDDSHDV cycle. Simulation results indicate that compared with original control strategy, the predictive control strategy and neural network based control strategy show better efficiency performance. The neural network based strategy is verified by hardware-in-loop experiment and experiment results indicate that the control performance in real hardware shows similar property with simulation results. [ABSTRACT FROM AUTHOR]