Search

One of the main obstacles to the robust optimization for permanent magnet machines is the high computational burden, which is mainly caused by the robustness evaluation process considering the manufacturing uncertainties. In this paper, a sequential sampling Kriging model is adopted to estimate the design robustness, and the problem of high-dimensional variables for the surrogate model due to uncertainties is avoided through adopting the worst-case based approach. To further reduce the number of finite element analyses (FEA) required for constructing a metamodel, a multi-points sequential sampling process with a two-step optimization oriented surrogate model updating algorithm is proposed. Compared with the FEA directly based robust optimization in previous work, similar Pareto Fronts are achieved by adopting the meta-model proposed algorithm but the overall run time reduced by 75%. In the end, an optimization problem for a 1.2 kW motor is considered by applying the proposed algorithm, and two prototypes with deliberately designed tolerances, imitating the worst-case scenarios in the real world, are manufactured. The worst-case torque ripple is reduced from 7.6% to 4.8% after optimizing, and this verifies the efficacy of the proposed algorithm. [ABSTRACT FROM AUTHOR]

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]

Torque ripple reduction has become an active research area for permanent magnet synchronous machine (PMSM) drives. This paper presents a novel torque ripple reduction method based on harmonic current control. In the proposed method, the optimal harmonic current solution based on the torque ripple model with minimum stator resistive loss is derived in the rotor reference frame (RRF). The optimal harmonic currents are injected by the proposed harmonic current controller (HCC), wherein the magnitude of harmonic currents are defined as controller variables. To estimate the harmonic currents, the proposed method also utilizes the least mean square (LMS) based adaptive filter (AF), wherein the coefficients are defined as feedback to the proposed HCC. The proposed methodology is demonstrated using both simulations and experiments and is verified to reduce torque ripples of PMSM drives over a wide range of speeds. [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]

This article discusses a method to consider the effects of the manufacturing uncertainties on the additional cogging torque harmonics. With this method, the sensitivity analysis can be incorporated into the design stage instead of just checking it afterwards. With the authors’ previous work of the worst-uncertain-combination-analysis (WUCA) method, several design parameters and their effects on the additional cogging torque harmonics under uncertainties are discussed. It is demonstrated that a design with inherent robustness can be obtained through the proper selection of pole-arc coefficient and slot-opening width. In addition, a sensitivity index is proposed to estimate the effects of parameter uncertainties on torque harmonics. Some suggestions on improving the design robustness are consequently provided from the aspects of tolerances control and parameter design. The deductions are verified through finite element analysis (FEA) on different pole/slot configurations. [ABSTRACT FROM AUTHOR]

To improve the performance of open-winding permanent magnet synchronous motors (OW-PMSMs) with a common dc bus, a novel control method that can simultaneously eliminate parameter mismatch in the d-axis, q-axis, and zero-sequence loop (ZSL) is proposed. First, the parameter mismatches are analyzed. Second, an extended state observer (ESO), which can predict the current in the next instant and the disturbance caused by parameter mismatch, is established. By combining the ESO and the deadbeat predictive current control (DPCC) in the d-axis and q-axis, replacing the sampled current in the DPCC with the predictive current in the ESO, and considering predictive disturbance as a voltage reference feedforward compensation, one-step delay and the disturbance caused by parameter mismatch are addressed. Then, in the ZSL, the predicted zero-sequence disturbance is considered as a compensation for the reference zero-sequence voltage (ZSV). The ZSV is obtained using the zero-voltage vector redistribution strategy in alternate subhexagonal center pulsewidth modulation strategy. The proposed method enhances the robustness of OW-PMSM, against parameter mismatch in the d-axis, q-axis, or ZSL. To verify the effectiveness of the proposed method, simulation and experimental results obtained using the traditional DPCC method and the ESO+DPCC method are presented herein. [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]

Permanent-magnet (PM) motors have been widely used in industrial applications and automobiles. However, owing to the design tolerances, manufacturing uncertainties, and material inconsistency, deviations from the ideal motor inevitably occur with parasitic effects, such as additional cogging torque and vibration. One of the most significant obstacles for evaluating these uncertainties is the large computational burden caused by the countless uncertain combinations, which must be computed via the finite-element method (FEM). Herein, a worst-uncertain-combination-analyze (WUCA) method is proposed to significantly reduce the computational cost. A widely used analytical method is modified to identify the origin of additional cogging-torque harmonics caused by uncertainties. Different types of uncertainties for the surface-mounted PM machine and interior PM machine can be analyzed simultaneously. With the WUCA method, the worst-case combinations can be confirmed theoretically; thus, FEM calculations for only a few combinations of uncertainties are required, rather than thousands. Compared with the widely adopted design of experiments based uncertain combining methods, the worst-case cogging torque obtained from the WUCA is higher. FEM verification of different pole/slot configurations revealed that the WUCA method was a general and effective method for quickly estimating the worst-case cogging torque under manufacturing uncertainties. [ABSTRACT FROM AUTHOR]

One of the main obstacles to applying the robust optimization for the permanent magnet motors is the high computational burden, which is mainly caused by the robustness evaluation considering the manufacturing uncertainties. In this article, efforts are made to speed up the process of robust optimization, from the aspects of reducing the computational cost, and avoiding the computing resources being wasted. Firstly, several widely used methods, aiming to identify the worst-case performance under uncertainties, are examined, and compared. And the efficient worst-uncertain-combination-analysis (WUCA) method is consequently adopted to significantly reduce the computational cost of robustness evaluation. From another aspect, the parameter rounding (PR) operation is inevitable due to the constraint of manufacturing accuracy, but its adverse effects on the optimization are often ignored. It is found that parts of the computing resources might be wasted during the optimization. A new PR during the calculation (PRDC) method is proposed, and incorporated into the process of robust optimization. Compared with the conventional one, similar results can be achieved with the computational time reduced by approximately $\text{11}\%$ in the PRDC modified robust optimization. [ABSTRACT FROM AUTHOR]

Conventional finite-control set model predictive current control predicts the motor behavior for every switching state and selects the voltage vector that can minimize the cost functions as an optimum voltage vector. To get rid of the model parameter dependency, a finite-control set nonparametric predictive current control (NPCC) was proposed before, which can predict future current behavior using the measured data instead of initial model parameters. However, one of the main issues that are limiting the current and torque ripple performance is a stagnant current update mechanism. To overcome this issue, this article proposes a novel current update mechanism that based on two most recent current variations to reconstruct the permanent magnet synchronous machine model. The proposed nonparametric predictive current control based on the current update mechanism can effectively reduce the torque ripple and enhance current performance in contrast to NPCC. Experiment results are demonstrated to verify the effectiveness of the proposed method under different operating conditions. [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]

Model predictive current control (MPCC) can predict future motor behavior according to a motor model. In practice, however, motor parameters will vary at run time, and the parameter mismatch disturbances caused by the variation in motor parameters will deteriorate the MPCC performance. To suppress the parameter mismatch disturbances effectively, this paper proposes a modified MPCC with a current variation update mechanism. In contrast with the traditional current prediction equation that contains crude model parameters, the modified current prediction equation contains only measured information, taking advantage of the proposed current variation update mechanism, which can update the modified prediction equation within each sampling period. A simulation established by MATLAB software indicates that the proposed method can effectively suppress the parameter mismatch disturbances. Experiments are carried out to verify the correctness of the proposed method. [ABSTRACT FROM AUTHOR]

In an open-end winding permanent magnet synchronous machines drive with single dc voltage source, zero-sequence current (ZSC) can lead to a high current stress of power modules and loss of motor. Therefore, some modulation strategies have been employed to alleviate ZSC by suppressing zero-sequence voltage (ZSV). However, ZSC still exists in the system because ZSV can also be generated by other nonlinear factors of inverter such as the dead time of system. In addition, zero-sequence back electromotive force in the zero-sequence path can also enlarge torque ripple and ZSC. In order to deal with above problems, first, this paper proposes a full-order adaptive zero-sequence observer to estimate future ZSC and ZSV, which are able to compensate one-step control delay. Second, to achieve the maximum voltage dc bus utilization, this paper proposes a novel deadbeat predictive current control (DPCC) scheme with alternate sub-hexagonal center pulsewidth modulation strategy to suppress ZSC and torque ripple simultaneously. Finally, this paper presents a comparative study of two types of methods, namely traditional DPCC scheme and the proposed DPCC scheme. Simulation and experimental results are demonstrated to verify the effectiveness of the proposed DPCC scheme. [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]

Soft magnetic composite (SMC) materials and SMC electromagnetic devices have attracted strong research interest in the past decades. However, SMC devices have large core loss that needs to be put into consideration even at the design stage. Effective and accurate prediction of the core loss becomes crucial for the design and optimization of high-performance motors with these materials. Equivalent circuit model is a widely used method for machine analysis, due to the advantages in the fast calculation with a clear physical mechanism. This paper presents an equivalent circuit model to predict the core loss of a claw-pole permanent magnet motor with SMC stator core. All the parameters including the equivalent core-loss resistance in the equivalent circuit model are identified based on the finite-element method to achieve high accuracy, and the effectiveness of the parameters identification methods is experimentally verified. The proposed equivalent circuit model can predict the core loss and motor’s performance efficiently both under no-load and loading conditions. [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]

This paper examines the problem of optimizing hybrid electric vehicle (HEV) power management for fuel economy. This paper begins by presenting a pseudospectral algorithm to solve this optimization problem. Compared with traditional dynamic programming (DP)-based optimal power management approaches, this algorithm has two key advantages: It is numerically more efficient, and it furnishes both the optimal state and costate trajectories. Building on the second advantage, this paper proposes a two-level strategy for optimal power management in vehicles commuting along fixed routes. The upper level of the proposed strategy is a costate adaptation algorithm employing pseudospectral optimization, whereas the lower level is an instantaneous optimization controller employing Pontryagin's minimum principle (PMP). This paper shows its pseudospectral optimization algorithm and two-level strategy using numerical simulation for a series hybrid school bus. Parameters of the bus powertrain model are obtained from experimental component tests performed at the National Engineering Laboratory for Electric Vehicles, Beijing Institute of Technology. Simulation results show that the pseudospectral method reaches a solution close to DP with higher computational efficiency. Furthermore, the proposed two-level strategy is capable of adapting vehicle power management based on road-grade predictions, i.e., an attractive feature compared with more traditional online hybrid power management approaches such as the use of proportional integral derivative (PID) control for adaptive equivalent fuel consumption minimization [PID equivalent consumption minimization strategy (PID-ECMS)]. [ABSTRACT FROM PUBLISHER]

This paper describes a computationally efficient approach for mapping rotor power loss in permanent magnet (PM) machines. The PM loss mapping methodology discussed here utilizes a small number of time-step finite-element analyses (FEAs) to determine the parameters of a functional representation of loss variation with speed (frequency) and stator current and is intended for a rapid evaluation of machine performance over the entire torque–speed envelope. The research focus is placed on field-oriented-controlled brushless AC PM machines with surface-mounted PM rotor construction, although the method could be adapted for other rotor formats. The loss mapping procedure accounts for the axial segmentation of the PM array through the use of an equivalent electrical resistivity of the segmented PM array, which is obtained from three-dimensional (3-D) FEA. The PM loss can be accurately mapped across the full operational envelope, including the field-weakened mode, through a single 3-D and four two-dimensional time-step FEAs. The proposed methodology is validated on an 18-slot 16-pole surface-mounted brushless AC PM machine design. The loss mapping procedure results closely agree with the computationally demanding alternative of direct 3-D finite-element prediction of the PM power loss undertaken at each of the machine's operating points. [ABSTRACT FROM PUBLISHER]