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As coaxial magnetic gears use NdFeB permanent magnets with high-energy density, they have a torque density comparable to that of mechanical gears, and active research activities have been conducted since. However, NdFeB permanent magnets have conductivity and cause high permanent magnet eddy current losses due to the driving characteristics of coaxial magnetic gears. Ferrite has no conductivity, but because of its weak magnetic force, its competitiveness is poor. In this article, Nd-bonded permanent magnet which has high magnetic force and no conductivity is used. Finite element analysis (FEA) calculations and experimental results of coaxial magnetic gears using Nd-bonded permanent magnets were compared, and the direction of applying Nd-bonded to coaxial magnetic gears was presented by comparing the magnetization method of Nd-bonded.

In this paper, a novel genetic algorithm (GA) that employs species differentiation (SD) is proposed. The GA with SD (GA-SD) improves the convergence speed of the GA through the separation and progress of elite species that are classified by a kernel support vector machine. Furthermore, the GA-SD maintains exploration capability through the progress of inferior species and the gradual transition between species. To verify the effectiveness of the GA-SD, GA-SD was compared with the conventional GA on test functions. Finally, we applied the GA-SD for the optimal design of the wind generator.

This paper proposes an adaptive particle swarm optimization (APSO) based on a kernel support vector machine (KSVM). The proposed algorithm improves the convergence speed and exploration capability of the APSO by employing an individual adaptive parameter control based on the KSVM. To verify the algorithm’s effectiveness, it was compared with both the conventional PSO and APSO based on test functions. Finally, we applied the proposed algorithm to the optimal design of a synchronous reluctance motor (Syn-RM).

In this paper, an analytical method for predicting the rotor overhang (OH) effect is presented using two types of conformal mappings (CMs), which are the ${r}$ – ${\theta }$ and zr plane CMs. First, the no-load air-gap flux density (AFD) of a slotted air gap without OH is determined using the ${r} {\theta }$ plane CM. Second, the complex relative air-gap permeance (CRAP) in the zr plane is obtained by the zr plane CM. This helps in taking the OH effect into account. Finally, the no-load AFD of the slotted and OH air gaps in cylindrical coordinates is derived from the no-load AFD in the ${r} {\theta }$ plane with CRAP along the ${z}$ -axis. The accuracy of the presented method is verified by comparing the results of no-load analysis with those of 3-D finite-element analysis.

In this paper, the optimization for the reduction of copper loss, iron loss, and permanent magnet (PM) eddy current loss considering harmonics is carried out to improve thermal performance. The effect in terms of thermal performance is confirmed through thermal analysis using a lumped-parameter thermal network (LPTN). First, the optimal shape is determined using an automatic optimization method that combines the explorative particle swarm optimization algorithm and finite element analysis. Then, the iron loss and PM eddy current loss considering carrier harmonics are recalculated by inputting the actual pulsewidth modulation current. Finally, an LPTN-based thermal analysis is performed with the calculated loss as the input. In particular, the thermal parameter is confirmed through an experimental method to validate the accuracy of the thermal analysis. The advantages over the conventional motor are clarified.

Optimal design of electric machines using finite-element analysis (FEA) necessitates too much computation time to maintain high accuracy. To reduce the computation time and obtain a global optimum in multi-modal problems, this paper presents differing extent mesh adaptive direct search (DEMADS). The proposed algorithm is classified into three groups according to the poll searching frame and mesh size for assigning a global and local searching. In particular, DEMADS shares information among solutions based on a parallel multi-start strategy for earlier stopping by deviation and checking revisits. The effectiveness of DEMADS has been validated and evaluated as the function call number and the convergence number at a global optimum using benchmarking function. Finally, it is applied to an optimization problem that minimize torque ripple of a spoke-type permanent magnet synchronous machine via FEA.

The characteristics analysis of an electric machine requires the finite-element method. Hence, a large amount of computation occurs in the design process to take into account uncertainties as the manufacturing tolerances. In this paper, an efficient and useful multimodal optimization algorithm using the kriging surrogate model is proposed for the robust optimization of an electric machine. However, the conventional kriging (CK) method has a memory problem in multi-dimensional problem due to the enlarged correlation matrix. Thus, a sub-domain kriging (SDK) strategy and improved Latin hypercube sampling (ILHS) are proposed not only to solve the memory problem of the CK method, but also to increase the convergence speed. In addition, a gradient-free sensitivity index is proposed for robust optimization in order to address the conventional first and second gradient index which causes a numerical error. The outstanding performance of the proposed algorithm is verified by comparing with other optimization methods via several mathematical test functions which includes multi-dimensional problem. Moreover, the proposed algorithm is applied to a cogging torque reduction design case for interior permanent magnet motor.

Numerical analysis in frequency-domain guarantees convincing accuracy by reflecting non-linearity of material without massive computation time, combining advantages of the equivalent circuit method and numerical analysis method. However, at highly magnetic saturated regions where non-linearity of the material is severe, error in torque calculation is evident, as parameters in frequency domain are presumed to be in a sinusoidal condition. Many researchers have been suggested a compensating method by creating fictious material with logically modified $B$ – $H$ curve. Nevertheless, the error between the results of the numerical analysis in frequency-domain and time-domain analysis is not small enough to estimate motor characteristic. Therefore, the compensation method for improving accuracy on the estimation of induction motor characteristics is proposed in this paper. The numerical analysis based on finite-element method in frequency domain has been conducted using the proposed fictious material and the result is compared to those of the conventional methods and time-domain analysis to validate its improvement on accuracy.

This paper presents an analytical method based on the magnetic equivalent circuit to derive permanent magnet (PM) load-lines of two general types of a variable flux memory motor (VFMM), which are circumferentially embedded (CE)-type and radially embedded (RE)-type PM motors. The proposed PM load-line models are valid and suitable for investigating the PM magnetization characteristics of VFMMs. It is validated via visible comparison with the PM load-lines derived by the finite-element method. Furthermore, by making use of the proposed method, the magnetization characteristics of several CE and RE VFMMs, which have different geometry parameters, have been examined and compared. According to the analysis results, RE-type VFMMs require approximately ten times larger remagnetizing field than CE-type VFMMs, although RE VFMMs are further influenced by demagnetizing field than CE VFMMs.

This paper discusses the design process of a generator to utilize waste diesel engines in the automotive industry, and presents the results of the design, analysis, and test evaluation of a 78 kW permanent magnet synchronous generator. In the initial design process using the finite-element analysis and space harmonic analysis, the number of poles and slots was determined. Then, the generator characteristics were investigated by the torque per rotor unit volume method and the $d$ , $q$ -axis equivalent circuit. Last, the optimal size of the generator was determined. This process makes it possible to design in consideration of efficiency, inductance, material cost, and temperature characteristics. A prototype of the model was created and tested with uncertainty analysis, and a comparison between the experimental results and the results of the optimization was conducted to validate the analytic approach.

This paper presents study of design characteristics with wide constant power speed range (CPSR) for electric vehicle Propulsion. Through analysis of interior permanent magnet synchronous motor (IPMSM) control logic, design characteristic of extending CPSR is proposed. To achieve wide CPSR, center of voltage limit ellipse should be placed at cross point of current limit circle and d-axis. To analyze characteristic of voltage ellipse, four types of representative IPMSM rotor topologies are selected. Through rotor topology analysis, tendency of φ f and L ds parameters which decide center of voltage limit ellipse is analyzed. This paper also considers shifting of voltage limit ellipse center according to change in motor speed. Reflecting the result, this paper focuses on center of voltage limit ellipse position at maximum speed, because central coordinates of high-speed range determine characteristics of CPSR. Through the analysis result, this paper suggests direction of IPMSM design for wide CPSR.

Recently, overhang structures are increasingly used for various applications requiring high torque and power densities in a limited space such as traction motors of electric vehicles and servo motors of robot arms. In the design stage, overhang effects should be taken into account to predict the performances and the temperature rise of a motor with overhang. However, little research has been conducted on the thermal characteristics and analysis methods for a surface-mounted permanent-magnet (SPM) motor with overhang. To address this problem, the electromagnetic and thermal characteristics of the SPM motors with and without overhang are invested in this paper. Specifically, 3-D finite-element method is used in electromagnetic analysis to accurately calculate electromagnetic losses which are heat sources in thermal analysis. Moreover, the lumped-parameter thermal network (LPTN) which can consider overhang effects rapidly and precisely is proposed as an analytical method. Through the proposed LPTN, the computation time for thermal analysis is reduced considerably in comparison with a computational fluid dynamics method, and the temperature rise of the motors are precisely predicted. The results calculated in electromagnetic and thermal analysis are verified experimentally with 0.3 Nm, 20-pole/24-slot SPM motors adopting natural cooling.

Finite element analysis-based optimal design of electric machines takes up a considerable amount of time for characteristic analyses and the analysis region has many local optima. Thus, selecting the appropriate global search optimization algorithm, known as the with fast convergence characteristics is necessary in the optimal design of electric machines. In this paper, a novel global search optimization algorithm known as the global-simplex optimization (GSO) algorithm, is proposed. The GSO is an optimization algorithm that adopts the characteristics of the simplex method for searching the optimum point but utilizes its global searching capability by scattering many initial points at the beginning of the algorithm. The global searching capability and convergence speed of GSO is validated through a comparison with particle swarm optimization, which has already been proven for its global searching capability through its application in the well-known Goldstein-Price benchmark function. In addition, the GSO has been applied to the optimal design of an interior permanent magnet synchronous machine to reduce the torque ripple and total harmonic distortion in its back electromotive force.

Three-dimensional (3-D) finite element analysis is generally required to analyze the performance of an axial flux permanent magnet (AFPM) motor because it has asymmetrical characteristics along the z-axis. Although the computing performance of CPU is enhanced owing to technological development, a large amount of analysis time is required for 3-D finite element analysis (FEA). This paper presents a method of AFPM motor to an equivariant linear synchronous permanent magnet (ELSPM) motor, both of which have identical output performance. The output performance of the proposed ELSPM motor becomes identical to that of the AFPM under the mandatory condition that the PM magnetic energy of both the models must be identical. The validity of the proposed ELSPM motor in terms of the accuracy and computation time is verified by comparing its results with the results of the AFPM motor based on the FEA.

In the preliminary design of the variable flux memory motor (VFMM), it is essential to examine the magnetization characteristics of the permanent magnet (PM) according to different direct-axis current pulses, which requires numerous parametric studies. In this paper, we propose a non-linear PM load-line model for concentrated winding surface-mounted PM VFMMs by making use of the magnetic equivalent circuit method, which is valid for investigating the PM magnetization state considering non-linear magnetic characteristics. The proposed method is validated through a comparison with the results of non-linear finite-element analysis.

In this paper, we propose a novel particle swarm optimization (PSO), which is based on the Euclidian distance of particles. In the conventional PSO, the convergence speed is decreased, since several particles that are far from swarm does not converge but wander around search area. The proposed algorithm, which is named after distance based intelligent PSO, assigns a new position and velocity to the furthest particle. Therefore, all particles gather around global optimum, and it is able to search for global optimum faster than that of the conventional PSO. To validate effectiveness of the proposed algorithm, we compare it to the conventional PSO using three numerical test functions. In addition, interior permanent magnet synchronous motor (IPMSM), which has characteristic of magnetic saturation in magnetic steel sheet, is chosen as target model of optimal design. Total harmonic distortion of back-electromotive force is optimized by optimization of rotor topology. Through optimal design of IPMSM, effectiveness of the proposed algorithm for electric machine design is verified.

In this paper, a novel algorithm “firework optimization” (FO) is introduced, as well as its hybridization with generating set search (GSS). FO is an exploration algorithm, which intends to explore a vast region within the search space in order to determine regions that may contain a regional optimum. As the regions with potential are defined, GSS, the exploitation algorithm is applied to determine whether regions determined by FO contain a local or a global optimum. The validity of FO and GSS hybridization algorithm is verified using Branin and Six-Hump Camel test functions, and the hybridization algorithm is applied to an interior permanent magnet synchronous motor, in order to minimize torque ripple.

Coaxial magnetic gears, which are capable of transferring torque without physical contact or a change in speed, have sufficient advantages, including high efficiency and reliability, allowing them to replace mechanical gears. Such the coaxial magnetic gears have therefore been receiving attention for various applications, including wind power generation and electric vehicles. However, when two rotors of a coaxial magnetic gear, which are attached using permanent magnets (PMs), rotate, a high transfer torque ripple is generated owing to the difference in magnetic resistance of the flux-modulator. In particular, the torque ripple of the inner rotor is relatively higher than that of the outer rotor. These torque ripples should be reduced as much as possible during the design process because they cause vibrations and noises in the machinery. Therefore, in this paper, various flux-modulator shapes are proposed to compare the transfer torque characteristics of a coaxial magnetic gear. The transfer torque characteristics resulting from the proposed shape were derived using nonlinear finite element analysis based on a 2-D numerical analysis.

Recently, interior permanent magnet synchronous motor (IPMSM) is paying attention because of its high power density and efficiency by adopting permanent magnet. The IPMSM has two control methods: maximum torque per ampere (MTPA) control and flux weakening control. Generally, an MTPA control is applied to a continuous operating point, and in case of a startup point, input current is applied which satisfies required output torque. To control the IPMSM, the various types of inverters with current and voltage limitations are used. The motor cannot be operated if these limitations are not satisfied. This paper proposes a method to satisfy the current and voltage limitations of the inverter with two different operating points. By changing the control method of the rated operating point by a stack compensation method, the current and voltage limitations of the inverter can be satisfied at a startup operating point, where the input current in MTPA control at the continuous operating point cannot be satisfied. The proposed method is verified by a 2-D finite element analysis.

Design of an electric machine such as the axial flux permanent magnet synchronous motor (AFPMSM) requires a 3-D finite-element method (FEM) analysis. The AFPMSM with a 3-D FEM model involves too much time and effort to analyze. To deal with this problem, we apply a surrogate assisted multi-objective optimization (SAMOO) algorithm that can realize an accurate and well-distributed Pareto front set with a few number of function calls, and considers various design variables in the motor design process. The superior performance of the SAMOO is verified by comparing it with conventional multi-objective optimization algorithms in a test function. Finally, the optimal design result of the AFPMSM for the electric bicycle is obtained by using the SAMOO algorithm.

According to the conventional study, it is possible to perform 2-D finite-element analysis (FEA) to consider rotor overhang effect through the adjusted remanence flux density by employing an overhang parameter. The latest study addressed that the overhang parameter can be obtained using the variation of operating point of permanent magnet (PM). In this paper, we propose the novel computational method to calculate the effective remanence flux density directly to consider PM overhang effect making use of magnetic energy, which is applied for a spoke-type PM synchronous motor. The proposed method is validated through a comparison with the results of 3-D FEA.

This paper derives an effective rotor shape by the optimal design of hybrid magnetic torque converter using the multiple linear regression analysis (MLRA). In particular, using a non-linear finite-element method based on the 2-D numerical analysis, we conduct a correlation analysis between independent variables (selected by the Box-Behnken design) and reaction variable. In addition, we derive a regression equation for reaction variables according to the independent variables using the MLRA and analysis of variance. We assess the validity of the optimized design by comparing the characteristics of the optimized model derived from the response surface methodology and the initial model.

In this paper, a novel global search algorithm interstellar search method (ISM) and its hybridization with mesh adaptive direct search (MADS) are proposed. The ISM is a population-based algorithm that is suitable for multimodal function, due to its capability of finding points on the diverse basins of attraction in a short time. The hybridization with MADS supports the fast convergence on adjacent optima from the points that are discovered in global search using ISM. The effectiveness of ISM was validated through test functions, compared with genetic algorithm and particle swarm optimization. Last, the ISM was applied to the optimal design of brushless dc motor aimed for minimizing torque ripples, identified through finite-element analysis.

The optimal design of electric machines requires a significant amount of computation time, since the design is usually performed based on finite-element analysis (FEA) for better accuracy. For this reason, the development of an optimization algorithm is regarded as a prerequisite to reduce the number of objective function call, which is directly related to computation time. The local search algorithm does not guarantee that the global optimum solution can be searched effectively. Therefore, in this paper, we introduce the novel optimization method, search region management (SRM), to improve the efficiency of the local search algorithm. SRM uses a guided random bit generation based on Gray code for the region selection when the local search iterates the optimum search process for a multi-start. In addition, the SRM saves and manages the information of previous searched points to prevent overlapping calculation and to increase the effectiveness of the local search during every iteration process. To clarify the effectiveness of the proposed method for electric machine design, it is applied to the optimal design of brushless dc motor coupled with FEA minimizing torque ripple.

A finite-element analysis-based optimal design of an electric machine takes considerable time for its objective evaluation and has many local minima. Thus, selecting an appropriate global convergence optimization with fast convergence speed is necessary in the optimal design of an electric machine. In this paper, a novel global search optimization algorithm, mass ionized particle optimization (MIPO), is newly proposed. The MIPO is the population-based algorithm, which reflects the interactive force between the ionized particles. The global convergence and the convergence speed are validated by comparison with the particle swarm optimization, which have already been proved for its global convergence when applied to a well-known Goldstein–Price function as a benchmark function. In addition, the algorithm has been applied to the optimal design of an interior permanent magnet synchronous machine aiming for its torque ripple reduction.

A heat treatment of gears (increasing hardness in the surface layers of gear teeth) significantly enhances the quality and performance of gears in terms of resistance to ware and abrasion and fatigue strength. Recently, a simultaneous dual frequency (SDF) induction heating technology has emerged as the most effective and efficient heat treatment method. It strives to achieve “uniform” temperature distributions along the outer contour of gears by applying two input currents simultaneously [1, 2]. The amplitude and frequency of both currents are regulated independently to control the hardening depth at the root and the tip of the gear teeth [3]. Obtaining the best combinations of the input frequencies and powers experimentally is very costly and time consuming. Thus, many researches have been concentrated on determining these parameters using simulation methods, but they require tremendous computational time and resources [4, 5]. In an effort to reduce computational costs in determining SDF input parameters, this paper proposes a 2D FE modeling method, and it experimentally evaluates the heat treatments of gears using a SDF induction heating system with the parameters obtained with 2D FE analysis.

To optimize an interior permanent magnet synchronous motor (IPMSM) design for a fuel cell electric vehicle, a new surrogate-assisted multi-objective optimization (MOO) algorithm is proposed in this paper. The proposed algorithm is a multi-objective algorithm (MOO) that can account for three kinds of objectives such as the torque amplitude, torque ripple, and magnet usage simultaneously to improve the power transmission and to reduce the noise, vibration, and cost for various design variables. While the conventional MOO algorithms have a series that requires many function evaluations, especially considering many objectives and design variables, the proposed algorithm can create an accurate and well-distributed Pareto front set with few function evaluations. In comparison with the conventional MOO algorithms, the outstanding performance of the proposed algorithm is verified. Finally, the proposed algorithm is applied to an optimal design process of an IPMSM.

To design electric machines, the motor performance, cost, and manufacturing have to be considered. Hence, researchers have called this the multi-objective optimization (MOO) problem in which the goal is to minimize or maximize several objective functions at the same time. In order to solve the MOO problem, various algorithms, such as nondominated sorting genetic algorithm II and multi-objective particle swarm optimization, have been widely used. When these algorithms are applied to the electric machine design, much time consumption is inevitable due to many times of function evaluations using a finite-element method. To solve this problem, a novel surrogate-assisted MOO algorithm is proposed. Its validity is confirmed by comparing the optimization results of test functions with conventional optimization methods. To verify the feasibility of its application to a practical electric machine, an interior permanent magnet synchronous motor is designed.

In this paper, the optimal driving point of induction motor is selected for guaranteeing torque production, which is fed by d-q current combination, and numerical design compatibility employing current source FEM is verified via comparison with the one employing voltage source method. In particular, vector control considering voltage and current constraints has been implemented numerically with FEM when selecting the optimal driving point. Moreover, comparative analysis is performed for the design results, such as magnetic saturation and iron loss, according to driving points selected in designing procedure. With the identified results, the design compatibility of FEM engaged with the current source is validated with comparison of the one obtained by FEM and equivalent circuit method engaged with the voltage source.

Numerical design coupled with finite-element method (FEM) has been performed for realizing the adaptive efficiency distribution of interior permanent magnet synchronous motor (IPMSM) compatible to the frequent operating range of electric vehicle. In particular, the target operating region is specified with the representative driving modes of heavy-duty vehicle in Braunschweig and Seoul. Considering various control strategies, the dominant electromagnetic losses throughout the whole operating region of IPMSM are numerically identified in association with FEM and employed as design target to minimize. Furthermore, the purposely designed prototype is experimentally validated via the resultant efficiency map built-up by static and dynamic dynamometer tests.

Optimal design of the electric machines is interlinked with the optimization algorithm for obtaining the quantitative optimum and numerical analysis of electric machines for the objective function. The optimization algorithm is required to evaluate the objective function minimal times since numerical analysis of the electric machines consumes a lot of time. In this paper, intelligent MADS with a clustering and elastic net is newly adopted for the optimal design of electric machines to raise the reliability of the global optimization and fast convergence time. In particular, the proposed algorithm is employed to the optimal design of the interior permanent-magnet PM synchronous machine minimizing torque ripples, which has been regarded as one of the complicated design problems.

Permanent magnet synchronous machines are vulnerable to significant amounts of torque ripple if they are not carefully designed. Even though minimizing cogging torque can help reduce the torque ripple, but can not definitely give rise to a low level torque ripple. This paper presents a simple solution for minimizing the cogging torque and suppressing operation torque ripple simultaneously. The principle of that simple solution is illustrated, where a magnet with different width is used so that the flux density distribution in the machine is substantially changed. The magnet widths for minimizing cogging torque are obtained by using an analytical model. The influence of magnet widths on operation torque ripple and average operation torque is examined by using Finite Element Analysis (FEA) which gives more preciseness to calculations. It is found that the cogging torque and operation torque ripple can be greatly reduced, but with slight average output torque reduction. At last, the Unbalance Magnetic Pull (UMP) is examined, indicating that the presented method can substantially increase the UMP due to the asymmetric distribution of magnets.

The cogging torque in flux-switching permanent magnet machines (FSPMs) is high due to its unique structure and high air-gap flux density. The cogging torque principle in FSPM is different from that in traditional PM machines, which can not be correctly predicted by analytical consideration. The aim of paper is to present the investigation on cogging torque principle in FSPM by analyzing the flux density distribution and a simple cogging torque reduction technique, i.e., teeth notching. Various kinds of notching schemes and their influence on cogging torque are examined along with instantaneous torque and average output torque at different load conditions. Numerical optimization process combined with finite-element analysis, which gives more preciseness to calculations, is performed to minimize cogging torque. The results show that the cogging torque circle depends on the real flux density distribution in the machine rather than the number of stator/rotor poles and the presented method can greatly reduce the torque ripple at only slight cost of average output torque.