This paper proposes an improved position-offset based parameter estimation (POPE) method for permanent magnet synchronous machines. In the proposed method, dq -axis inductances and permanent magnet flux linkage can be estimated simultaneously based on three machine states corresponding to injections of zero, positive, and negative position offsets. The guideline is also provided for selecting appropriate amplitude of injected position offset. Compared with the conventional POPE methods, the proposed method has two distinguished advantages, i.e., (a) the influence of inverter nonlinearity can be fully eliminated and (b) the proposed method can be applied under both constant and variable speed operations. The effectiveness of the proposed parameter estimation method is verified by simulations and experiments.

Rotor position error under sensorless control will lead to parameter identification errors of surface-mounted permanent magnet (PM) synchronous machines (SPMSMs). In this article, a novel signal injection method is proposed for the first time, i.e., two additional virtual back-electromotive forces (EMFs) with equal amplitudes as perturbation signals are injected into a position observer. By injecting a positive and a negative virtual back-EMF, it is found that position error caused by parameter errors and inverter nonlinearity maintains constant and is only related to the ratio of q - and d -axis voltage fluctuations. As a result, the correlations of phase inductance, resistance, and PM flux linkage with the measured dq -axis currents/voltages can be established, in which the influence of both position error and inverter nonlinearity can be completely cancelled. Subsequently, by developing a full-rank model under two d -axis current conditions, the phase resistance, inductance, and PM flux linkage can be accurately identified. The experiment results show good performance of the proposed identification method under sensorless control.

Rotor position estimation error in sensorless controlled surface-mounted permanent magnet synchronous machines (PMSMs) will cause errors in parameter estimation. This paper proposes a novel rotor position-offset injection method for parameter estimation of sensorless controlled PMSMs. During the period of square wave rotor position-offset injection, it is found that the amplitude response of q -axis voltage fluctuation decreases with the increasing accuracy of parameters, and thus, stator inductance and resistance can be estimated by controlling the q -axis voltage fluctuation. The permanent magnet flux linkage is subsequently obtained from the back-EMF observation. Moreover, the guideline is provided for selecting appropriate amplitude and frequency of injected position offset. Compared with conventional methods, the proposed parameter estimation method is immune to rotor position error. The proposed parameter estimation method based on sensorless controlled PMSMs is verified by experiments at different speeds, loads, and temperatures.

This paper proposes a novel approach to predict the maximum permanent magnet (PM) temperature for surface-mounted PM synchronous machines (SPMSMs). By using the 3-D conduction heat transfer equation, a generic correlation between average and maximum PM temperatures is analytically derived for the first time. It depends on the basic geometric/thermal parameters, the PM eddy current loss, and the stator thermal behaviour. By using the average PM temperature obtained by rotor flux linkage estimation method, the PM loss calculated by the finite element analysis (FEA), and a thermal sensor installed on the stator, the maximum PM temperature can be determined semi-analytically. The contribution of the PM temperature rises caused by the stator thermal state and the rotor PM loss can also be evaluated separately. The proposed method exhibits similar estimation accuracy as the FEA while requiring significantly less computation time. The effectiveness of the proposed method is validated experimentally based on a 12-slot/10-pole SPMSM.

Under sensorless control, the position estimation error in permanent magnet (PM) synchronous machines (PMSMs) will lead to parameter estimation errors. In this article, by injecting a novel square wave virtual back EMF (VBEMF) into either of two position observers for both three phase sets in dual-three phase surface-mounted PMSMs (DTP-SPMSMs), it is found that the fluctuation of sum of q-axis voltages in both sets will decrease as the parameter estimation accuracies of stator resistance and inductance in both position observers increase. Therefore, the fluctuation of sum of q-axis voltages can be minimized to estimate stator inductance and resistance, which is immune to position error. The PM flux linkage is correspondingly estimated from identified back EMF utilizing Kalman filter (KF) algorithm. Moreover, utilizing the DTP winding characteristic of DTP-SPMSMs, the proposed VBEMF is only injected into one set, which can reduce the torque ripple by about 50%. The experiments are given to validate the proposed method.