This paper presents a delay-suppressed sliding mode observer (SMO) to observe the real-time rotor position of a permanent magnet synchronous machine (PMSM) controlled by vector control (VC) algorithms. Firstly, in order to solve the low pass filter (LPF) delay problem existing in the traditional signum function-based sliding mode observer (SMO), a brand-new hyperbolic function is initially selected as the switching function. Because a hyperbolic function with proper boundary layer is capable of reducing the chattering phenomenon of a SMO, it is not necessary to re-employ LPFs to eliminate the adverse impacts of chattering on the position estimation accuracy. In order to ensure the reachability and stability of the hyperbolic function-based SMO, the observer gain is calculated by the means of a Lyapunov function in this paper. Secondly, to solve the problem of calculation delay caused by digital computation, a current pre-compensation scheme based on dual-sampling strategy in one switching period is proposed. After compensating the calculation delay, the accuracy of position estimation as well as the motor control performance can be improved. Finally, the proposed SMOs with and without delay compensation are verified by both simulation and experiments which are conducted on a three-phase 1.5kW PMSM drive prototype.

In this paper, fault diagnosis and tolerant control strategies have been studied comprehensively for dual-three phase PMSM drives to improve the reliability. Based on direct torque control (DTC) with space vector modulation (SVM), a series of diagnostic and tolerant control methods have been proposed for five types of faults, namely speed-sensor fault, DC-link voltage-sensor fault, current-sensor fault, open-phase fault and open-switch fault. Firstly, diagnosis and tolerant schemes are proposed for speed-sensor fault by estimating the rotor angle speed with the rotating speed of stator flux. Secondly, diagnosis and tolerant schemes are proposed for DC-link voltage-sensor fault by combining the current model based stator flux observer with the voltage model based stator flux observer. Thirdly, a three-step method is designed to diagnose three types of faults related to current signals, namely current-sensor fault, open-phase fault and open-switch fault simultaneously. A vector space decomposition (VSD) based current estimation method is proposed to achieve fault-tolerant control for the current-sensor fault, and the voltage compensation based fault-tolerant control is presented for both open-phase fault and open-switch fault. The experiments have been taken on a laboratory prototype to verify the effectiveness of the proposed fault diagnosis and tolerance schemes.

As key component for the flexible dc distributed power system, the dual active bridge (DAB)-converter-based solid-state transformer (SST) with high efficiency for a wide operating range is essential. However, with the traditional phase-shift control, high backflow power and current stress will significantly affect the conversion efficiency. In this paper, the backflow power characteristics in both sides of DAB-based SST converters are comprehensively analyzed. On this basis, complete transmission power, backflow power, and peak current mathematical models are established. Then, a minimum-backflow-power-based extended-phase-shift control strategy is proposed with the determination of optimal phase-shift pairs by using the Karush-Kuhn-Tucker function for various scenarios. The backflow power and current stress curves with different algorithms are compared. It shows the proposed control can improve the output power regulation flexibility, minimize the backflow power, and improve the efficiency in wide operating range. Finally, a DAB-based SST prototype was developed and the experimental results verified the effectiveness of the proposed control strategy.

The motor drive system plays a significant role in the safety of electric vehicles as a bridge for power transmission. Meanwhile, to enhance the efficiency and stability of the drive system, more and more studies based on AI technology are devoted to the fault detection and diagnosis (FDD) of the motor drive system. This article reviews the application of AI techniques in motor FDD in recent years. AI-based FDD is divided into two main steps: feature extraction and fault classification. The application of different signal processing methods in feature extraction is discussed. In particular, the application of traditional machine learning and deep learning algorithms for fault classification is presented in detail. In addition, the characteristics of all techniques reviewed are summarized. Finally, the latest developments, research gaps, and future challenges in fault monitoring and diagnosis of motor faults are discussed.

In this paper, from a new perspective of flux and charge, we present in-depth analyses of two ideal memristor emulators and the fifth-order memristive Chua's circuit constructed based on them. The constitutive flux-charge relations of the two adopted memristor emulators are first formulated, and their initial-dependent characteristics are numerically revealed and experimentally verified. Thereafter, with these two constitutive relations, a third-order dimensionality decreasing flux-charge model for the fifth-order memristive Chua's circuit is constructed, in which five extra constant system parameters are introduced to indicate the initial states of the five dynamic elements. Numerical simulations confirm that this newly constructed model possesses several determined equilibria and maintains the initial- dependent dynamics of the original voltage-current model. Thus, the complex and sensitive initial state-related extreme multistability phenomenon can be deeply explored through theoretical analyses and hardware measurements. It is demonstrated that the sensitive extreme multistability phenomenon becomes detectable in the flux-charge domain, which is efficient for exploring the inner mechanisms and further seeking possible applications of this special phenomenon.