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Electric motor emulator (EME) is being increasingly utilized during the development and test process of motor drive units (MDUs) for electric vehicle applications. However, the high-speed instability issue, without sufficient corresponding theoretical analysis currently, limits its further application. Hence, a comprehensive modeling and stability analysis approach for the high-speed EME is presented here to enrich the understanding of this special power hardware-in-the-loop testing scenario. A detailed model of the EME testing system with MDU included is developed in complex vector (CV) form for the first time, the poles loci of which are then plotted for accurately predicting the system stability. Influences of various factors including control bandwidth, time delay, and control structure on the speed limit of the EME are investigated, with parameter nonlinearities in the motor model also considered to demonstrate the extended applicability of the CV-based modeling approach. The maximum error between the predicted speed limits and the experimental results is less than 20 Hz, and the EME running up to 800 Hz is also experimentally achieved by adopting the internal model control structure, both validating the effectiveness and accuracy of the presented CV modeling and stability analysis approach for the high-speed EME.

In this article, a new data-driven diagnosis method is proposed for mixed eccentricity (ME) in the external rotor permanent magnet motors (ERPMMs). Differently from the previous articles, the proposed method has the advantages of large sample size, high sample diversity, high efficiency, and strong generalization ability. First, the improved parametric analytical model (AM) of back electromotive force (EMF) of the ERPMMs is established. Then, the characteristics of the back EMF are analyzed. Accordingly, its amplitudes of fundamental waves and sideband harmonics are selected as the ME indexes. Afterwards, by using the proposed parametric AM, a database of ME signals is established efficiently, which contains tens of thousands of labeled samples. Furthermore, based on the back propagation neural network, a high-precision diagnosis model for ME in the ERPMM is established. Interference faults, such as unbalanced stator windings and uneven magnetization are also discussed. Finally, an experimental prototype for simulating the ME is manufactured and the effectiveness of the proposed method is verified. The maximum absolute diagnostic error is less than 4.0%. It provides a new idea for multiparameter diagnosis for ME.