This paper presents a literature review on magnetic gears, highlighting the advantages of using these technologies for mechanical power transmission applications in wind energy conversion systems and transportation, such as in electric vehicles. Magnetic gear technologies have important advantages over their mechanical counterparts. They can perform the speed change and torque transmission between input and output shafts by a contactless mechanism with a quiet operation and overload protection without the issues associated with conventional mechanical gears. The paper describes the fundamentals and operating principle of the field-modulated magnetic gear topologies and investigates the magnetic torque transmission mechanism. However, despite all the advantages highlighted in different research and development reports, there is still no convincing evidence to show that magnetic gear technologies are an acceptable alternative for industrial applications. The aim of this paper is to summarize previous work on magnetic gears to identify the topologies most suited for mechanical power transmission systems in wind energy conversion systems and electric vehicle applications. These applications will show that research and development of magnetic gear technologies contribute significantly to solutions for sustainable systems, a subject to which our current civilization must pay a lot of attention.

In this paper, a wide-speed-range predictive direct torque control scheme for surface-mounted permanent magnet synchronous machines without rotational transducer is presented. At very low and zero speeds of operation, the identification of the rotor position is carried out by applying test voltage signals within the regular commutation process for predictive torque control, in order to detect the machine saliency produced by the stator magnetic saturation. Then, the acquired signals that are function of the rotor position are digitally processed through a quadrature phase-locked loop tracking observer. At middle-and-high speeds of operation the angular position of the rotor is estimated by using a predictive sliding-mode observer of the stator flux. A changeover algorithm is programmed for coupling both estimated values of the angular position of the rotor. Experimental results in a wide-speed-range obtained by using a hybrid digital system which consist of a digital signal processor (DSP) and of a field-programmable gate array (FPGA) verify the effectiveness of the proposed encoderless predictive control scheme.

This paper presents the performance evaluation of a proposed single-phase bidirectional active neutral-point-clamped (ANPC) five-level converter (5LC) for active filtering in grid-to-vehicle (G2V), vehicle-to-grid (V2G) and vehicle-to-home (V2H) applications. In the system, the 5LC is series-connected with an electric vehicle battery charger based on a dual active half-bridge DC-DC converter (DAHBC). A cascaded strategy with a proportional-resonant compensator controls the 5LC whereas the DAHBC is controlled by a single phase-shift technique. A performance comparison is carried out for the proposed topology versus three NPC/ANPC 5L converters found in literature with similar topologies. The simulation results obtained in MATLAB-SimPowerSystems along with the experimental results obtained from a laboratory prototype validate the following: a) the first two literature topologies have the highest efficiencies, but they are incapable of balancing the voltage of the split-capacitors what increases the total harmonic distortion of both the grid current in the G2V/V2G modes and the 5LC output voltage in the V2H mode while producing a sluggish transient response, b) the 3rd literature topology like the proposed one properly balance the capacitors voltage and achieve a faster dynamics, but at the expense of an efficiency reduction, and c) unlike the former topology that continuously applies a natural but wasting unfedback voltage balancing strategy, the proposed one that includes a closed-loop control allows achieving an ANPC 5LC with a superior performance featured by a higher efficiency, a reduced switches stress, a flatter power losses distribution and a better power quality improving under the three modes of operation.

This paper presents a multi-objective design optimization of a power transformer to find the optimal geometry of its core and the low- and high-voltage windings, representing the minimum power losses and the minimum core and copper weights. The optimal design is important because it allows manufacturers to build more efficient and economical transformers. The approach employs a manufacturer’s design methodology, which is based on the usage of the laws of physics and leads to an analytical transformer model with the advantage of requiring a low amount of computing time. Afterward, the multi-objective design optimization is defined along with its constraints, and they are solved using the Non-Sorting Genetic Algorithm III (NSGA-III), which finds a set of optimal solutions. Once an optimal solution is selected from the Pareto front, it is necessary to fine-tune it with the 3D Finite Element Analysis (FEA). To avoid the large computing times needed to carry out the 3D Finite Element (FE) model simulations used in multi-objective design optimization, Response Surface Methodology (RSM) polynomial models are developed using 3D FE model transformer simulations. Finally, a second multi-objective design optimization is carried out using the developed RSM empirical models that represent the cost functions and is solved using the NSGA-III. The numerical results of the optimal core and windings geometries demonstrate the validity of the proposed design methodology based on the NSGA-III. The used global optimizer has the feature of solving optimization problems with many cost functions, but it has not been applied to the design of transformers. The results obtained in this paper demonstrate better performance and accuracy with respect to the commonly used NSGA-II.

This paper presents a novel single-phase (SP) active-neutral point clamped (ANPC) five-level bidirectional converter (FLBC) for enhancing the power quality (PQ) during the grid-to-vehicle (G2V) and vehicle-to-grid (V2G) operation of an electric vehicle (EV) charger connected in series. This EV charger is based on a dual-active half-bridge DC-DC converter (DAHBC) with a high frequency isolation transformer. Unlike the comparable ANPC topologies found in literature, the proposed one has two more switches, i.e., ten instead of eight. However, with the addition of these components, the proposed multilevel converter not only becomes capable of properly balancing the voltage of the DC-link split capacitors under various step-changing conditions but it achieves a better efficiency, a lower stress of the switching devices and a more even distribution of the power losses. The resulting grid-tied ANPC-SPFLBC and DAHBC are accurately controlled with a cascaded control strategy and a single-phase shift (SPS) control technique, respectively. The simulation results obtained with MATLAB-SimPowerSystems as well as the experimental results obtained in laboratory validate the proposed ANPC-SPFLBC for a set of exhaustive tests in both V2G and G2V modes. A detailed power quality analysis carried out with a Fluke 43B alike demonstrates the good performance of the proposed topology.

The use of a suitable modeling technique for the optimized design of a magnetic gear is essential to simulate its electromagnetic behavior and to predict its satisfactory performance. This paper presents the design optimization of an axial flux magnetic gear (AFMG) using a two-dimensional (2D) magnetic equivalent circuit model (MEC) and a Multi-objective Genetic Algorithm (MOGA). The proposed MEC model is configured as a meshed reluctance network (RN) with permanent magnet magnetomotive force sources. The non-linearity in the ferromagnetic materials is accounted for by the MEC. The MEC model based on reluctance networks (RN) is considered to be a good compromise between accuracy and computational effort. This new model will allow a faster analysis and design for the AFMG. A multi-objective optimization is carried out to achieve an optimal volume-focused design of the AFMG for future practical applications. The performance of the optimized model is then verified by establishing flux density comparisons with finite element simulations. This study shows that with the combination of an MEC-RN model and a GA for its optimization, a satisfactory accuracy can be achieved compared to that of the finite element analysis (FEA), but with only a fraction of the computational time.