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The electric machine emulator (EME), using digital simulation and power electronics to emulate the characteristics of actual machines, can greatly accelerate the testing of electric drives. However, most existing EMEs are based on typical L filter and linear controller, which causes control conflicts and bandwidth limitation. To address this issue, this paper presents an EME based on LCL filter with passive damping for a three-phase permanent magnet synchronous motor. To improve the dynamic emulating accuracy, a dual closed-loop deadbeat predictive current control algorithm is proposed, which is computationally efficient and easy to implement. The system stability is analyzed in the discrete domain, and the parameter constraints of the filter are obtained. Then, two unknown input observers are designed to compensate for the disturbance currents and voltages caused by modeling errors. Moreover, instead of the empirical method, a theoretical one considering the harmonic suppression, bandwidth, stability and resonance is presented for filter design. Finally, the performance of the proposed EME is validated through simulation and experimental results under various conditions such as machine start-up, torque step change, and speed reversal.INDEX TERMS Electric machine emulator (EME), LCL filter, dual closed-loop deadbeat predictive current control, unknown input observer. NOMENCLATURE

Battery safety and aging have been considered as most import issues restricting the further deployment and development of electric vehicles in real-world applications. Due to differences in technology, materials, groups and physical and chemical reactions inside the battery, lithium-ion concentration gradient will be formed inside the battery, which is directly reflected in the inconsistency of external parameters.To study the correlation between battery faults and external parameters, in this study, alllife-cycle big-data of 41 electric vehicles are analysed by data mining. Firstly, the characteristic parameters representing voltage consistency are studied, and then the selected related parameters are statistically analysed in each dimension to explore the factors affecting the safety and reliability of batteries. The interesting knowledge discovered by this study can provide follow-up support for battery safety.

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