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The square-wave voltage signal injection into the estimated d-axis is an effective and novel sensorless control scheme for interior permanent magnet synchronous motor at zero speed and low speed. However, the notable acoustic and electromagnetic noises induced by the conventional fixed-frequency signal injection are very harsh and shrill to hear, which limits the practical application. Aiming at reducing unnecessary acoustic noise, a novel random voltage injection method based on the Markov chain (MC) is proposed in this article. Two high-frequency square-wave voltages with different types of frequencies and amplitudes are randomly injected into the estimated d-axis by MC rules, and this way has spread spectrum characteristics, which can effectively reduce the unnecessary noise. In addition, a new signal demodulation compensation method considering the digital delay effect in high-frequency signals is proposed, which effectively reduces the position and velocity estimation errors. Then, the power spectra density of the induced high-frequency currents under the fixed frequency injection method and the proposed MC method are compared and analyzed. The high performance of the proposed MC method has been validated by the experiment drive platform and compared with the traditional fixed frequency under different control conditions. [ABSTRACT FROM AUTHOR]

The Taguchi optimization method is an efficient method for motor design optimization. However, it is hard to handle the multiobjective motor optimization problem with big design space for the parameters. To deal with this problem, in this article, a fuzzy method and sequential Taguchi method to optimize an inter permanent magnet synchronous motor (IPMSM) is employed. The fuzzy inference system is introduced to convert the multiple objectives to a single-objective optimization problem. The sequential Taguchi method is used to optimize the structural parameters at multiple levels to improve the accuracy of optimization. After the optimal selection analysis, the best combination of motor structure factors is obtained. By comparing the optimization result of the proposed method with that of the conventional Taguchi optimization method, the effectiveness and superiority of the proposed method are verified. [ABSTRACT FROM AUTHOR]

Model predictive current control (MPCC) is a high-performance control strategy for permanent-magnet synchronous motor (PMSM) drives, with the features of quick response and simple computation. However, the conventional MPCC results in high torque and current ripples. This article proposes an improved MPCC scheme for PMSM drives. In the proposed scheme, the back electromotive force is estimated from the previous stator voltage and current, and it is used to predict the stator current for the next period. To further improve the steady state and dynamic performance, the proposed MPCC selects the optimal voltage vector based on a current track circle instead of a cost function. Compared with the calculation of cost function, the prediction of the current track circle is simple and quick. The proposed MPCC is compared with conventional MPCC and a duty-circle based MPCC by simulation and experiment in the aspect of converter output voltage and sensitivity analysis. Results prove the superiority of the proposed MPCC and its effectiveness in reducing the torque and current ripples of PMSM drives. [ABSTRACT FROM AUTHOR]

This article investigates the efficient robust design optimization of a five-phase permanent magnet (PM) hub motor for electric vehicles. Besides the requirement of high-performance, like high torque density, low torque ripple and efficiency, fault-tolerant operation capability are also considered in the design optimization. To ensure that the motor performance is not sensitive to the variations of manufacturing tolerances, robust design optimization is employed to the investigated motor. To improve the fault tolerant capability of the motor, the motor performances under fault operation are also considered in the optimization. A Fuzzy-based sequential Taguchi robust optimization method is proposed to improve the comprehensive performance and save computing time. The proposed method is efficient because it holds the advantages of Taguchi method, fuzzy theory, and sequential optimization strategy. The motor performance is improved significantly by using the proposed method. Experimental results verify the accuracy of the model used in this study. [ABSTRACT FROM AUTHOR]

This article presents a novel method for the sensorless control of interior permanent-magnet synchronous motors. An iterative search strategy based on dichotomy is proposed to provide a finite number of rotor position angles with good accuracy. These position angles are used to calculate the back electromotive force (EMF) in d-axis. The optimal rotor position angle is the one that yields a back EMF minimizing the defined cost function. With the increase of the iterations, the accuracy of rotor position angle increases geometrically. To effectively extract the back EMF signal under the low-speed condition, the high-frequency signal injection method is used to realize the low-speed operation of the motor. A hybrid control strategy is adopted to achieve the smooth switching from the low-speed to high-speed. The performance of the proposed method has been validated experimentally and compared with that of the conventional phase locked loop under different conditions. [ABSTRACT FROM AUTHOR]

This paper presents an optimal control strategy for a permanent-magnet synchronous hub motor (PMSHM) drive using the state feedback control method plus the gray wolf optimization (GWO) algorithm. First, the linearized PMSHM mathematical model is obtained by voltage feedforward compensation. Second, to acquire satisfactory dynamics of speed response and zero d-axis current, the discretized state-space model of the PMSHM is augmented with the integral of rotor speed error and integral of d-axis current error. Then, the GWO algorithm is employed to acquire the weighting matrices Q and R in linear quadratic regulator optimization process. Moreover, a penalty term is introduced to the fitness index to suppress overshoots effectively. Finally, comparisons among the GWO-based state feedback controller (SFC) with and without the penalty term, the conventional SFC, and the genetic algorithm enhanced proportional–integral controllers are conducted in both simulations and experiments. The comparison results show the superiority of the proposed SFC with the penalty term in fast response. [ABSTRACT FROM AUTHOR]

This paper presents a compound propulsion system with a high-speed permanent-magnet synchronous motor (PMSM) and two in-wheel motors for electric vehicles (EVs). In this paper, the longitudinal dynamic model of EVs is first presented. Then traction distribution ratio $\alpha $ is introduced to express the traction distribution between the front and the rear axles. Moreover, the function of power consumption concerned with the traction distribution ratio $\alpha $ is established. Therefore, the $\alpha $ that minimizes the power consumption function is selected as the optimal traction distribution ratio. To improve the performance of motor controllers, the model predictive torque control (MPTC) method is employed for high-speed and in-wheel motor drives. Experimental comparison with field-oriented control (FOC) shows the advantages of MPTC in dynamic response. Finally, experimental comparisons and hardware-in-loop (HiL) tests are presented to verify the MPTC method and the proposed energy allocation method, respectively. [ABSTRACT FROM AUTHOR]