In the double-vector-based model predictive torque control (MPTC), two voltage vectors are applied in one control period. Due to a large number of possible combinations among voltage vectors, the determination of optimal voltage vector pair is often complicated. This article proposes a new approach to reduce the number of candidate active voltage vectors. The concept is to preselect the active voltage vectors according to the stator flux vector error. The proposed MPTC is implemented in a stationary frame and avoids the complicated coordinate transformation as well as the tangent inverse calculations. Furthermore, a promising approach is conceived for calculating the duty cycle of active voltage vector, which can contribute to the less dependence on the system model, thus alleviating the sensitivity to motor parameter variations. To further improve the steady-state performance, a modified three-vector-based MPTC is newly put forward. Meanwhile, the proposed duty cycle calculation method is used to achieve the deadbeat control of torque and stator flux. Theoretical analyses and experimental results are given to verify the effectiveness of the proposed MPTC methods.

In conventional model predictive torque control (MPTC), only one voltage vector (VV) is applied during a whole control period, thus causing a large torque ripple. To improve the steady-state performance, some two-vector-based control schemes have been proposed. However, the selection of the optimal VV pair is complex as well as has a large computational burden, and the improvement of performance is still limited by the direction and amplitude of the output VV. This article proposes a low-complexity three-vector-based MPTC for SPMSM drives, which can precisely determine the appropriate active voltage vectors (AVVs) with the predicted torque error. Then, a modified switching table is developed to directly select the AVVs, thus greatly reducing the complexity and computational burden of the algorithm. To obtain a better steady-state performance, a duty cycle calculation method based on torque and stator flux difference parameters is newly proposed to achieve the deadbeat control of torque and stator flux. And then, the experimental comparisons with the double-vector-based MPTC are conducted. The results show that the proposed MPTC can effectively reduce the steady-state torque ripple while maintaining a good dynamic performance as well as almost a fixed switching frequency for all speed ranges.

This paper proposes a novel hybrid-excitation flux-switching (HEFS) machine with axial partitioned stator (APS) arrangement, which can achieve the independent accommodation of armature winding and field-excitation winding, thus enhancing the field-excitation capability and improving the winding heat dissipation. More importantly, a particular parallel-magnetic-circuit (PMC) hybrid-excitation pattern consisting of the axial and radial magnetic paths is innovatively developed in the HEFS machine, which is an effective solution for improving the flux-regulation ability. Consequently, the PMC-based flux-regulation principle is comprehensively illustrated. Moreover, by investigating the flux-regulation characteristics of three structural feasible APS-HEFS topologies, the bilateral complementary structure is finally determined as the preferred option. By using the multi-objective optimization method and the three-dimensional finite element analysis (3D-FEA), the dimensional parameter optimization design of the proposed machine is conducted, and the performance comparison with a conventional HEFS counterparts is also carried out to prove the flux-regulation superiority of the proposed design. Finally, a prototype is fabricated for experimental testing, and the measured results are in good agreement with the FEA, which verifies the validity of the proposed APS-HEFS topology and its theoretical flux-regulation principle analysis.

As the world transitions towards sustainable transportation, the advancement of electric vehicles (EVs) has become imperative. Wireless power transfer (WPT) technology presents a promising solution to enhance the convenience and efficiency of EV charging while alleviating the challenges associated with traditional wired systems. This paper conducts an in-depth exploration of WPT technologies for EVs, focusing on their theoretical foundations, practical implementation, optimization strategies, development trends, and limitations. The theoretical principles of wireless charging are first elucidated, categorizing them into near-field methods, such as inductive and capacitive charging, and far-field methods, including microwave and laser-based charging. A comparative analysis reveals the advantages and limitations inherent to each technology. The implementation section examines various charging strategies, encompassing stationary, dynamic, and quasi-dynamic wireless charging, assessing their feasibility and effectiveness in practical applications. Furthermore, optimization techniques aimed at enhancing WPT system performance are examined in depth, with particular emphasis on coil structure optimizations, anti-misalignment solutions, compensation topology optimizations, modulation strategy optimizations, and parameter identifications. The discussion section outlines current development trends in wireless charging technologies for EVs, highlighting the limitations that hinder the widespread adoption of wireless charging technologies in the EV market. Finally, potential research directions and the implications of wireless charging technology on the development of EVs are summarized. This critical review aims to provide valuable insights for researchers and practitioners dedicated to advancing the field of wireless charging for EVs.

Compared with the single-vector-based model predictive control (MPC) technique, the multiple-vector-based MPC technique gains interests in improving the total harmonic distortion (THD) of the stator currents and reducing electromagnetic torque and stator flux ripples for a three-phase surface permanent magnet synchronous motor (SPMSM). However, the complicated enumeration and operation in vector selection and duty cycle calculation increase the computational burden and limit its penetration. Hence, in this article, a simplified virtual-vector-based MPC with a control factor for a three-phase SPMSM is proposed. Without sector identification and enumeration operations, the proposed technique utilizes the control factor to automatically generate a large number of virtual vectors to achieve equivalent performance effectiveness with a multiple-vector-based MPC technique with a lower sampling frequency. Subsequently, regardless of the number of virtual vectors, the candidate virtual vectors are directly identified in an $x-y$ coordinate system to greatly reduce the computational burden. In addition, the duration of the selected optimal virtual vector and the resultant switching sequences are easily obtained and arranged to even the switching losses among the switching devices and further optimize the THD of the stator current. The proposed technique is simplified and flexible. Its control effectiveness is verified by experimental results.

Model predictive control (MPC) technology for multi-phase electric drives has received increasing attention in modern industries, especially in electric vehicles, marine electrical propulsion, and wind power generation. However, MPC has several challenges in controlling multi-phase electric drives, including the design of weighting factors, high computational complexity, large harmonic currents, heavy reliance on the system model, fault-tolerant control operation, common-mode voltage, and zero-sequence current hazards. Therefore, this paper gives a comprehensive review of the latest and most effective solutions to the existing major technical challenges and prospects for the future trends of MPC for multi-phase electric drives.