Drive machines of hybrid electric vehicles (HEVs) employ axial flux permanent magnet (AFPM) motors on account of its high power density and high efficiency. The technique takes full advantage of the performance and manufacturing preponderances of soft magnetic composites (SMC). Specifically, it includes low iron consumption at high frequency and high speed, convenient machining and small relative magnetic permeability. An extra DC excitation winding is appended to the motor to conquer the drawbacks of the constant power speed ratio. It can provide an extremely wide constant power speed region, which ensures the excellent property of the machine. Finite element algorithm (FEA) is used to assess the property of axial flux hybrid excitation motor (AFHEM). Comprehensive analysis has fully indicated that larger torque density and wider speed range can be obtained by the AFHEM, which is appropriate for the application of HEV drives.

The model predictive thrust control (MPTC) is emerging as a promising control technique for the linear induction machine (LIM) drive applications. However, the weighting factor is designed based on empirical procedures that are tedious and time consuming. In this work, a novel fuzzy weighting factor optimization method for LIM is developed to identify the suitable control action. Different from conventional fuzzy methods, the tuning process of weighting factors is regarded as a fuzzy chance constraint so as to constitute a new optimal problem without weighting factors. Hence, the optimal model can be solved by probability optimization algorithm and the corresponding equivalence class is also conducted. At last, simulation results are presented to validate the feasibility and effectiveness of this method.

This paper presents a multi-disc coreless axial flux permanent magnet synchronous machine (MDC-AFPMSM) with N pole and S pole type series magnetic circuit and open-end winding for high-reliability applications, such as small power actuator system. Firstly, the topology and driving modes of MDC-AFPMSM are presented in detail. In this paper, a multi-objective optimization function is proposed to design the machine with full consideration of various influence factors. The drive performance indexes of four-phase, five-phase and six-phase machines are analyzed and discussed. Furthermore, the main parameters of the five-phase MDC-AFPMSM with open-end winding are calculated. In order to reduce the torque ripple, the air gap magnetic flux is optimized. Finally, the operation characteristics under short-circuit and open-circuit faults are fully analyzed based on the three-dimensional (3D) finite element algorithm. Comprehensive simulation results and theoretical analysis have demonstrated that the open-end winding MDC-AFPMSM has much stronger fault-tolerant ability in comparison to that of conventional machines.

Model predictive thrust control (MPTC) is one of the most effective approaches for linear induction machine (LIM) drive system. It can achieve the optimization of multiple objectives with suitable weighting factors, such as low ripples and fast dynamic performance. Due to the longitudinal end effect, the selection of weighting factor becomes particularly important in LIM. However, the process of tuning the weighting factors in the objective function is very long and tedious. In this paper, a dynamic fuzzy MPTC approach is proposed to solve the multi-objective problem for minimizing the flux and thrust ripples. Based on the fuzzy optimization technique, the tuning process of weighting factors can be transformed into a discrete membership function. A “max-min” objective function of voltage vectors is employed to select a more balanced switch combination on the Pareto optimal-frontier for LIM. Furthermore, this approach can adapt to different operating conditions by adjusting objective priority of membership function. At last, comprehensive simulation and experimental results are conducted to demonstrate the effectiveness and feasibility of the proposed method.