Guodong Feng

Current Injection-Based Online Parameter and VSI Nonlinearity Estimation for PMSM Drives Using Current and Voltage DC Components

To develop a high-performance and reliable permanent-magnet synchronous machine (PMSM) drive for electric vehicle (EV) applications, accurate knowledge of the PMSM parameters is of significance. This paper investigates online estimation of PMSM parameters and voltage source inverter (VSI) nonlinearity using current injection method in which magnetic saturation is also considered. First, a novel dc component-based current injection model considering VSI nonlinearity is proposed, which employs the dc components of dq-axis currents and voltages for PMSM parameter and VSI-distorted voltage estimation. This method can eliminate the influence of rotor position error on VSI nonlinearity estimation. Second, a simplified linear equation is employed to model the cross- and self-saturation of the dq-axis inductances during current injection, which can facilitate the estimation of the inductance variations induced by magnetic saturation. Third, a novel current compensation strategy is proposed to minimize the torque ripples caused by current injection, which contributes to making our approach applicable to both surface and interior PMSMs. Therefore, the proposed online parameter estimation approach can estimate the winding resistance, rotor flux, VSI-distorted voltage, and the varying dq-axis inductances under different operating conditions. The proposed approach is experimentally validated on a down-scaled laboratory interior PMSM prototyped for direct-drive EV powertrain.

A Novel Current Injection-Based Online Parameter Estimation Method for PMSMs Considering Magnetic Saturation

This paper studies the online parameter estimation of permanent magnet synchronous motor (PMSM) with the consideration of magnetic saturation and proposes a novel current injection method to estimate parameters, including winding resistances, dq-axis inductances, and rotor flux. During the current injection, the inductances will vary due to magnetic saturation, neglecting which will cause great estimation error especially in the inductance estimation. This paper proposes to use simplified equations to model the self- and cross-saturation effects during the current injection. By incorporating this saturation model into the PMSM steady-state equations, the varying dq-axis inductances due to magnetic saturation as well as the rotor flux can be accurately estimated. In addition, the estimation of winding resistance is independent of other parameters and does not get affected by the magnetic saturation. The proposed approach is validated through both the numerical and experimental studies on a laboratory interior PMSM.

A Closed-Loop Fuzzy-Logic-Based Current Controller for PMSM Torque Ripple Minimization Using the Magnitude of Speed Harmonic as the Feedback Control Signal

This paper investigates torque ripple minimization for permanent-magnet synchronous machine (PMSM), and proposes a closed-loop fuzzy-logic-based current controller by using the magnitude of the speed harmonic as the feedback control signal. The speed harmonic can be obtained from machine speed measurement, so the proposed approach does not require accurate machine parameters and is not influenced by the nonlinearity of the machine and drive. The torque harmonic can produce the speed harmonic of the same order, so their relation is investigated, which shows that the magnitude of the speed harmonic is proportional to the magnitude of the torque harmonic of the same order, so it can be used as a measure of torque harmonic for torque ripple minimization. Then, the torque harmonic model is developed to facilitate the design and analysis of the current controller. Afterward, a novel fuzzy-logic-based current controller is proposed to minimize the dominant torque harmonics. The proposed current controller is evaluated on a laboratory PMSM drive system under different load conditions and operation speeds.

Genetic algorithm based current optimization for torque ripple reduction of interior PMSMs

This paper investigates the torque ripple modeling and minimization for interior permanent magnet synchronous machine (PMSM). At first, a novel torque ripple model is proposed. In this model, both spatial harmonics of magnet flux linkage and current time harmonics induced by machine drive are considered, which includes the torque ripples resulted from magnet torque, reluctance torque and cogging torque. Based on the proposed model, a novel genetic algorithm (GA) based dq-axis harmonic currents optimization approach is proposed for torque ripple minimization. In this approach, the GA is applied to optimize both the magnitude and phase of the harmonic currents to achieve the objectives of: 1) minimizing the peak-to-peak torque ripple; 2) minimizing the sum of squares of the harmonic currents; and 3) maximizing the average torque component produced by the injected harmonic currents. The results demonstrate that the magnitude of the harmonic current can be significantly reduced by considering the phase angles of these harmonic currents as the optimization parameters. This leads to further suppression of the torque ripple when compared to that of a case where phase angles are not considered in the optimization. Also, an increase of the average torque is achieved when the optimum harmonic currents are injected. The proposed model and approach are evaluated with both numerical and experimental investigations on a laboratory interior PMSM.

Expectation-Maximization Particle-Filter- and Kalman-Filter-Based Permanent Magnet Temperature Estimation for PMSM Condition Monitoring Using High-Frequency Signal Injection

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.

Reduction of space harmonics in induction machines incorporating rotor bar optimization through a coupled IPSO and 3-D FEA algorithm

An induction machine with skewed rotor bars has enhanced starting performance and reduced current harmonic distortion. In a conventional skewed induction machine (IM) modeling, the axial magnetic flux variation is usually ignored in machine performance analysis in spite of significant parameter variation in the axial direction. Furthermore, the selection of optimal skew angle is vital to keep the output torque constant, especially in electric vehicle applications. In this paper, a 7.5 hp IM with one slot pitch skew angle is considered as an initial model and space harmonic reduction has been investigated by considering axial variation of the air-gap flux density. An analytical model incorporating spatial harmonics due to slotting has been implemented in parallel with a 3-D FEA model towards improvements in skew angle and rotor bar geometry selection using improved swarm intelligence technique. The air gap flux density distortion and torque ripple due to space harmonic reduction for improved design are compared with that of the initial design.

Direct Calculation of Maximum-Torque-Per-Ampere Angle for Interior PMSM Control Using Measured Speed Harmonic

This paper explores the use of speed harmonic for accurate and fast maximum-torque-per-ampere (MTPA) control of interior permanent magnet synchronous machines (IPMSMs). A novel MTPA angle calculation approach is proposed, in which the MTPA angle is directly calculated from the phase angle of the speed harmonic that is induced by an injected harmonic current. At first, the relation between the speed harmonic and machine parameters is derived from the IPMSM model, which shows that the phase angle of the induced speed harmonic can be utilized to cancel the machine parameters in existing MTPA equation. Thus, a new MTPA equation is derived, which does not involve machine parameters for MTPA angle calculation. Compared with existing methods, the proposed approach is independent from the machine and drive nonlinearities. Moreover, the MTPA angle is directly calculated from the derived MTPA equation, so the proposed approach is fast and computationally efficient. Thus, the proposed approach is suitable for both on-line applications and building off-line lookup tables of MTPA angles for IPMSMs. The proposed approach is experimentally evaluated on a laboratory IPMSM drive system.

Torque Ripple Minimization for Interior PMSM with Consideration of Magnetic Saturation Incorporating Online Parameter Identification

In this paper, an analytical torque ripple model for interior permanent magnet synchronous machine (IPMSM) is introduced at first, which can be used to estimate the torque ripple. Then, the inductance variation due to magnetic saturation is analyzed through experimental investigations, which demonstrates that magnetic saturation has a great influence on the inductances, and hence the torque ripple minimization performance. Thus, this paper proposes an online parameter identification approach to estimate the inductances and incorporates them into the torque ripple minimization. The proposed parameter identification approach can estimate the dq-axis inductances accurately such that it brings significant torque ripple minimization improvement for IPMSMs. Numerical and experimental investigations have been conducted to validate the proposed approach based on a laboratory IPMSM.

Online PMSM Magnet Flux-Linkage Estimation for Rotor Magnet Condition Monitoring Using Measured Speed Harmonics

Rotor magnet flux linkage is critical to monitor the state of the permanent magnet in a permanent magnet synchronous machine (PMSM). This paper proposes a novel online magnet flux-linkage estimation approach by using measured speed harmonics. The proposed approach is based on the PMSMs mechanical equation, so it is not influenced by magnetic saturation, inverter nonlinearity, and changes in machine parameters. The interaction of magnet flux linkage and harmonic current can produce torque harmonic, and thus speed harmonic. Therefore, we explore the use of speed harmonic for magnet flux-linkage estimation. At first, the torque and speed harmonic models are proposed. Then, the mathematical relationship between magnet flux linkage and speed harmonic is developed. Based on this model, a novel magnet flux-linkage estimation approach is proposed, which can eliminate the influence of the cogging torque and spatial harmonics in magnet flux-linkage estimation. The proposed approach is validated on a PMSM drive system under different loads and speeds.

Particle-Filter-Based Magnet Flux Linkage Estimation for PMSM Magnet Condition Monitoring Using Harmonics in Machine Speed

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.