High-performance propulsion motors for more electric aircrafts (MEAs) are highly appreciated in transportation electrification. For the widely adopted permanent magnet synchronous machines (PMSMs) for MEA propulsion, the segmented Halbach magnetization has distinct merit that it contributes to sinusoidal air-gap magnetic fields for smooth operation. However, it is hard to fast predict the field distribution and machine performance due to the model complexity. Based on the subdomain method, this paper first proposes an exact analytical model for slotless PMSM with segmented Halbach PMs. Then, the model is applied in the fast computation of magnetic field and performance prediction. Subsequently, the analytical results are compared with the finite element method (FEM), including the flux density, flux linkage, induced voltage, and steady-state torque at different loads. Also, the computation burden and prediction error are included to validate the model rapidity and accuracy. Finally, by prototype fabrication and unique field measurement methods, the flux density distribution is originally validated.

This paper proposes the multi-objective optimization of a double-stator hybrid-excited flux-switching permanent-magnet (DSHE-FSPM) machine, which aims at the contradictory objective achievement. Firstly, the proposed machine topology is presented. Also, the general analytical geometric model is built, which matches the typical DS-FSPM machines. Secondly, the archive-based multi-objective genetic algorithm (AMOGA) optimization is carried out. The contradictory multi-objectives contain the maximum efficiency, minimum PM cost, and minimum torque ripple. Also, the minimum inner and outer radial force difference is included, which can effectively lower the vibration and manufacturing difficulty. Additionally, before the optimization process, the sensitivity analysis is employed to alleviate the calculation burden. Thirdly, the hybrid flux-modulating machine performances and the prototype experimental results are presented, which can verify the feasibility of the proposed optimization strategy and the optimal machine design.

This paper presents a harmonic modeling method (HMM) for coaxial magnetic gears (CMGs) by considering the nonlinear B-H curve for soft magnetic material. CMGs are divided into several annular layers, where Maxwell equation is solved. A convolution matrix of permeability is introduced to obtain the relationship between the magnetic flux density and magnetic field strength, and the continuous boundary conditions can be further satisfied. Subsequently, the magnetic field distribution and electromagnetic performance of CMGs can be calculated by solving the system equations. The computation time and accuracy of HMM are compared with those of finite element analysis (FEA) and subdomain method. The results show that the HMM is relatively fast and precise, and its computation time is about 80% of that of FEA. Then, the parameters of CMGs are optimized based on the HMM and NSGA-II combined optimization method. Particularly, the modulator is divided into multi layers, so its saturation can be modelled more precisely, and its shape can be further optimized. Finally, two optimal cases are evaluated and compared, and the result proves the effectiveness of the integrated optimization method.

Accurate estimation of electrochemical states plays a fundamental role in guaranteeing safe, reliable, and efficient operations of lithium battery systems. However, current estimation algorithms rely on semi-empirical models that lack physical insights or expensive physics-based models that are hard to implement. Thus, this paper proposes an efficient hybrid physics-based and data-driven electrochemical state estimation framework of lithium-ion batteries by leveraging the advantages and circumventing the disadvantages of physics-based and data-driven models. First, a hybrid physics and data-driven battery model is established through systematic integration of the full-order pseudo-two-dimensional model and long short-term memory recurrent neural network. Second, a data argumentation technique is employed to establish physics-informed training datasets and a battery model with an output error of 10.45mV is trained using the data. Third, the unscented Kalman filter is pertinently designed to detect local concentrations, i.e., state-of-charge-related physical variables. Finally, the estimation performance is comprehensively examined under both simulation and experimental scenarios. The results demonstrate that the proposed framework can accurately provide physically meaningful state variables under a wide operation range, with an estimation error of less than 3% for SOC under standard conditions.

This paper proposes a new electric magnetic-geared (EMG) machine, which artfully incorporates the magnetic gear into an outer-rotor inserted permanent-magnet (PM) machine, purposely for electric unmanned aerial vehicle (e-UAV) application. The proposed EMG machine artfully shares the sandwiched-rotor for both the PM machine and the magnetic gear. As a result, the proposed machine can run in the high speed and high power for propelling the e-UAV. The design details, operation principles, static characteristics, and machine performances, as well as the propulsion performances of the proposed machine, are elaborated and discussed. The finite-element method is utilized to verify the validity of the machine design and performances, with emphasis on the e-UAV application.

This paper proposes a new double-stator magnetless vernier (DSMV) machine for direct-drive robotics. By artfully incorporating the double-stator structure, vernier configuration and dc excitation together, this DSMV machine yields the merits of the flux control capability, low-speed operation, high torque output, free maintenance, and high cost-effectiveness. Also, the optimizations of the rotor-teeth arc angle, stator-teeth arc angle, and current phase angle are carried out for the improvement of torque performance. In this way, the torque ripple of the machine is significantly reduced up to a tiny value of 3.67%. Moreover, the rational dc current range and the rotational speed range are obtained through the performance analysis with flux regulation. In addition, the comparison is performed with the conventional surface-mounted permanent-magnet machine. Therefore, the proposed machine shows the distinct features and is fit for the special application. Finally, both theoretical derivation and simulation are given for the validation of the machine design.