Multilevel converters (MCs) are popular due to their higher voltage levels, low dv/dt, low harmonics, and high-power handling capabilities. This paper evaluates the use of two pulse width modulation (PWM) schemes for MCs: Phase-Shifted PWM (PS-PWM) and Level-Shifted PWM (LS-PWM). At first, the Cascaded H-Bridge (CHB) converter is utilized to demonstrate the principles and switching patterns of PS-PWM and LS-PWM. Thereafter, a 23 level CHB converter is designed for a permanent magnet synchronous machine (PMSM) drive application as a case study. The switching and conduction losses in different switches and submodules are analyzed in details under different PMSM operation points. The total harmonic distortion (THD) and efficiency of the converter are also compared. The simulation results show that PS-PWM performs better owing to reduced THD in the voltage and current as well as equal loss distribution, compared to LS-PWM. However, PS-PWM contributes to higher switching losses.

This paper presents an unscented Kalman filter (UKF) based method to estimate the severity level of stator interturn short circuit fault in permanent magnet synchronous machines (PMSMs). A mathematical model is firstly built for PMSMs to represent the machine dynamic with inter-turn faults (ISF). The voltage imbalance, inherent asymmetry, and ISF effects are all differentiated analytically by the model. To quantify the fault severity in the presence of unmeasurable fault parameters, the UKF is employed to estimate the nonlinear fault-related quantities in PMSMs, such as the short circuit current, the percentage of shorted turns, and fault loop resistance. The effectiveness and reliability of the proposed method have been validated by extensive simulations.

The traditional higher order sliding mode observer (HSMO) based sensorless control strategy uses a constant gain. However, very large gain value can decrease the output accuracy, while very small gain value will degrade the sliding mode observer’s stability. Therefore, under uncertain disturbances, an adaptive gain should be set for the higher order sliding mode observer. This paper proposes a variable universe fuzzy adaptive high order sliding mode observer based sensorless control (VUF-HSMO) strategy for permanent magnet synchronous machines (PMSMs) to achieve high precision sensorless control. Firstly, fuzzy logic control technique is used to get an adaptive gain in the HSMO. Then, variable universe theory is employed to self-tune the universe of discourse according to the change of inputs, and this results in the improvement in position estimation accuracy. Experiment results under different conditions demonstrate the effectiveness and the superiority of the proposed VUF-HSMO based sensorless control compared with the traditional HSMO and fuzzy logic based HSMO.

The power-hardware-in-the-loop (PHIL)-based machine emulator systems use controlled power converters to mimic machine behavior. This testing method allows testing of the drive inverter and drive controller prior to the manufacture of an electric motor prototype. In this paper, a PHIL-based machine emulation system is used to mimic the behavior of a three phase induction machine (IM) which is connected to the grid. However, it is essential for the emulator inverter to have a dead time between the upper and lower switches. This dead time in the emulator inverter leads to lower order harmonics which deteriorate the motor emulation accuracy. Thus, it is important to investigate and analyze the effect of dead time in emulation. A detailed analysis of the dead time effect on motor emulation is presented. Experimental results are obtained from the emulator system and from a squirrel cage induction motor to validate the proposed emulator with various dead times.

This paper focuses on the sizing approximation and parameter estimation of a Linear Generator (LG) to be driven by a direct drive free-piston engine. The modeled and designed LG is of tubular topology comprising of an outer stator with a 3-phase winding configuration and inner slider having layers of axially magnetized PMs and spacers. Two design approaches have been used for the linear generator sizing approximations based on initial requirements. Equations for determining geometrical parameters have been derived. Finite Element Analysis (FEA) simulation and a MATLAB model were developed highlighting the inputs and outputs of the designed LG.

This paper investigates permanent magnet temperature (PMT) modeling and estimation for permanent magnet synchronous machines (PMSMs) by using the measured speed harmonic. First, a linear temperature model is derived to demonstrate that the magnitude of the speed harmonic decreases linearly with the increase of PMT. To achieve this linear model, the speed harmonic is induced by the injected harmonic currents satisfying certain conditions developed in this paper. To improve the estimation performance, PMT estimation is represented in a state-space model based on the derived temperature model, and the Kalman filter is applied to estimate the PMT from the measured speed harmonic. Compared with existing methods, the proposed approach has advantages in terms of simplicity in estimation and robustness to the variation of machine resistance and inductances. The proposed Kalman filter based modeling and estimation approach is evaluated with extensive experiments on a laboratory PMSM drive system under different speed and load conditions.

This paper proposes a novel gradient descent based maximum torque per ampere (MTPA) control algorithm for interior permanent magnet synchronous machines (IPMSMs) by using the measured speed harmonics. The proposed approach does not require machine parameters and thus is not influenced by the machine and drive nonlinearities. Hence, the proposed approach can ensure a robust MTPA control under different loading conditions. Specifically, in the proposed approach, a small q-axis harmonic voltage is injected into the machine to induce a small harmonic component in the machine speed. Based on the PMSM torque equation, the mathematical relation between the induced speed harmonic and the output torque is derived, which shows that the magnitude of the induced speed harmonic is proportional to the output torque of an IPMSM. Therefore, the speed harmonic is explored to seek the MTPA angle, in which the MTPA angle is found when the speed harmonic magnitude is maximized. In particular, the gradient descent algorithm is employed to detect the MTPA angle, which is computationally efficient and converges quickly. The proposed approach is evaluated with both simulations and experiments based on a laboratory IPMSM drive system.

Permanent magnet synchronous motor (PMSM) drives include several design and control variables that affect power losses. The improvement in system efficiency through control techniques will reduce the size of inverter and motor cooling system as well as the operating costs of the system. This paper proposes a comprehensive analysis of loss controllability using various control parameters to reduce the motor as well as inverter losses. Power losses considered include switching and conduction losses in the inverter and fundamental and harmonic losses in the stator winding, core and rotor magnets. The inverter losses have been calculated using an analytical model as functions of DC link voltage, load current and switching frequency. A 2-D electromagnetic coupled loss model is developed for calculating the losses in a laboratory interior PMSM (IPM) for varying operating conditions. An analytical model is used to obtain a comprehensive understanding of the behavior of motor losses that were obtained from the electromagnetic model with respect to control parameters. The dependence of motor, inverter and the system level losses are tested and validated on a laboratory IPM drive system. Consequently, suggestions are made regarding selection of control variables in order to reduce system-level losses in PMSM drives.

Accurate information of permanent magnet (PM) flux linkage is of significance to high-performance control and condition monitoring of permanent magnet synchronous machines (PMSMs). During machine operation, the PM flux linkage can vary due to temperature rise and saturation. Thus, this paper investigates how temperature rise and saturation influence the PM flux linkage under different operation conditions. Under no-load condition, the PM flux linkage is estimated from the back-EMF test. Under load condition, a speed harmonic based PM flux linkage estimation approach is proposed, in which the PM flux linkage is estimated from the speed harmonic without requiring machine parameters. Thus, the proposed estimation approach is not affected by the machine and drive nonlinearities and thus can guarantee the estimation performance. The proposed approach is applied for PM flux linkage estimation under various loads and temperatures to investigate the influence of temperature rise and saturation on the PM flux linkage. Experimental results demonstrate that the PM flux linkage under no-load is larger than that under load due to magnetic saturation, the PM flux linkage under load decreases as the saturation level increases, and the one under both load and no-load decreases linearly as the PM temperature increases.

The equivalent circuit parameters of an interior permanent magnet synchronous machine (PMSM) vary under different operating conditions due to magnetic saturation and temperature rise. Knowledge of accurate machine parameters is of paramount importance for high performance PMSM control and condition monitoring. Therefore, this paper presents a novel parameter estimation method, which employs both the electrical and mechanical model of an interior PMSM (IPMSM) for simultaneously estimating three parameters; namely, the d- and q-axis inductances and permanent magnet flux linkage. Specifically, the proposed parameter estimation approach is mathematically justified at first. Thereafter, by using the machine mechanical model with the speed measurement, a third equation in addition to the dq-axis voltage equations is developed to make the estimation model full-rank for simultaneously estimating three parameters. Lastly, the proposed approach is implemented with the recursive least square algorithm to estimate machine parameters and it is validated with simulations and experimental tests on a laboratory IPMSM drive system.

Integrated charging (IC) technology in electric vehicles (EVs) employing conventional power electronics and motor drivetrain components facilitates level 3 fast charging capabilities with reduction in overall weight and cost of the vehicle. However, when the winding inductances of 3-phase interior permanent magnet synchronous machines (IPMSMs) are realized as line inductors for battery charging, due to machine saliency, the magnetic fields produced by the sinusoidal AC supply results in 1) asymmetrical voltages in the air-gap as a function of rotor position and 2) relatively high magnitudes of oscillating torques causing harmful noise and vibrations. This can lead to significant AC losses with risk of permanent magnet demagnetization in the machine. Since the same motor is employed for traction application as well, it is of significance to optimally design the machine for IC operation. Thus, this paper exclusively investigates three IPMSM rotor configurations to be employed for IC operation in EV. This paper firstly presents a conventional dq-axis circuit model based damper design approach implemented for mitigating the saliency effect during IC. Then, a comparative performance analysis of the rotor configurations with damper bars during IC operation on machine saliency; asymmetrical voltage waveforms; oscillating electromagnetic torque; permanent magnet operating point and magnet losses is performed using finite-element analysis (FEA). Results obtained are analyzed and discussed.

In permanent magnet synchronous machine, high-frequency (HF) signal injection has been extensively investigated for permanent magnet temperature (PMT) estimation, in which PMT is estimated from the temperature-dependent HF resistance. Existing studies require prior knowledge on the HF resistance and neglect the fact that PMT is temporally correlated. This paper proposes a state-space model for PMT estimation, in which PMT is modeled with a piecewise linear equation to explore the temporal correlation. The state-space model is nonlinear due to unknown model parameters, which is required to be known in existing studies. This paper proposes to use expectation maximization particle filter (EM-PF) for simultaneous PMT and model parameter estimation. After EM-PF estimation, the state-space model becomes linear, so Kalman filter is employed for online PMT estimation. The proposed EM-PF along with a Kalman-filter-based approach can explore the temporal correlation among PMTs to improve the estimation performance, which can be hardly achieved in existing studies regarding PMT as a time-independent parameter. It should be noted that EM-PF is for initial PMT and model parameter estimation, while Kalman filter is for online PMT estimation ensuring computation efficiency and real-time capability. Our approach is validated with both numerical and experimental investigations.

For permanent magnet synchronous machines (PMSMs), accurate magnet flux linkage information is critical for permanent magnet condition monitoring and drive performance improvement. This paper proposes a novel particle-filter-based magnet flux linkage estimation approach by using the harmonics in the machine speed. In the proposed approach, the harmonic current is first injected to change the speed harmonics, and the particle filter is then applied to estimate the magnet flux linkage from the speed harmonics. With a proper selection of injected harmonic current, it is capable of simultaneously estimating the magnet flux linkage and reducing the torque ripples as well as the speed ripples. The proposed approach is based on the machine mechanical equation, so it is not influenced by the magnetic saturation, the resistance variation, and the inverter nonlinearity. Specifically, at first, a novel state-space model is developed based on the machine mechanical equation, which models the relation between the magnet flux linkage and the speed harmonic. The state-space model is nonlinear, so the particle filter is employed for a magnet flux linkage estimation. Our particle-filter-based estimation approach is validated on a laboratory PMSM drive system under different loads, speeds, and temperatures.