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Reducing eddy current losses in magnets of electrical machines can be obtained by means of several techniques. The magnet segmentation is the most popular one. It imposes the least restrictions on machine performances. This paper investigates the effectiveness of the magnet circumferential segmentation technique to reduce these undesirable losses. The full and partial magnet segmentation are both studied for a frequency range from few Hz to a dozen of kHz. To increase the efficiency of these techniques to reduce losses for any working frequency, an optimization strategy based on coupling of finite elements analysis and genetic algorithm is applied. The purpose of this paper is to define the parameters of the total and partial segmentation that can ensure the best reduction of eddy current losses.

First, a model to analyze eddy current losses is presented. Second, the effectiveness of full and partial magnet circumferential segmentation to reduce eddy loss is studied for a range of frequencies from few Hz to a dozen of kHz. To achieve these purposes a 2-D finite element model is developed under MATLAB environment. In a third step of the work, an optimization process is applied to adjust the segmentation design parameters for best reduction of eddy current losses in case of surface mounted permanent magnets synchronous machine.

In case of the skin effect operating, both full and partial magnet segmentations can lead to eddy current losses increases. Such deviations of magnet segmentation techniques can be avoided by an appropriate choice of their design parameters.

Few works are dedicated to investigate partial magnet segmentation for eddy current losses reduction. This paper studied the effectiveness and behaviour of partial segmentation for different frequency ranges. To avoid eventual anomalies related to the skin effect an optimization process based on the association of the finite elements analysis to genetic algorithm method is adopted.

The purpose of this paper is to study the performance and efficiency of two different permanent magnet (PM) machine rotor configurations under magnetic core saturation conditions.

Since the accuracy of conventional analytical methods is limited under saturation conditions, a finite element model of the machine is built; which is used to predict the various losses over its operating range such as eddy current, hysteresis, copper and magnet losses. Using this model, the efficiency map of the machine is derived which is used to investigate its efficiency corresponding to a heavy vehicle drive cycle. The performance of two different rotor designs are studied and the efficiency of each design is compared under the considered drive cycle.

It has also been proved that the performance advantage due to reluctance torque in the v-shaped interior PM (IPM) machine is offset by its core steel saturation at higher current/torque levels. The magnitude of iron losses in the IPM is higher than that in the surface PM (SPM) machine, however, the magnet loss in the SPM is higher than in the IPM.

An investigation of the performance of the IPM design in comparison with the SPM∼design under magnetic saturation conditions is not known to the authors. Hence, in this paper, it will be determined if the assumed performance advantage of the IPM over the SPM still holds true under these conditions.

The purpose of this paper is to present a novel method to identify demagnetization faults in the magnet of a permanent magnet synchronous machine (PMSM) using some externally measurable parameter.

The machine is modelled by using permeance network theory. The new feature introduced in the permeance network is the subdivision of magnets into segments, modelled as bidirectional elements. These bidirectional elements allow taking into account the effect of one element on the other. To detect the demagnetization faults, a gradient‐based algorithm is also developed. This algorithm uses the permeance network model of the PMSM and measurement data of some parameter to find the distribution of remanent induction in the magnet segments.

The methodology presented is able to detect the demagnetization fault using an external data. The measurement data in this paper is obtained through finite element simulations. The fast and accurate convergence of the algorithm makes the model to find its place in magnet fault diagnosis. Results for different magnet fault types have been presented.

This new approach to detect demagnetization fault can serve as a step towards development of better fault‐detection algorithms and fault‐tolerant control schemes.

Fractional slot permanent magnet (PM) brushless machines having concentrated non‐overlapping windings have been the subject of research over last few years. They have already been employed in the commercial hybrid electric vehicles (HEVs) due to high‐torque density, high efficiency, low‐torque ripple, good flux‐weakening and fault‐tolerance performance. The purpose of this paper is to overview recent development and research challenges in such machines in terms of various structural and design features for electric vehicle (EV)/HEV applications.

In the paper, fractional slot PM brushless machines are overviewed according to the following main and sub‐topics: first, machine topologies: slot and pole number combinations, all and alternate teeth wound (double‐ and single‐layer windings), unequal tooth structure, modular stator, interior magnet rotor; second, machine parameters and control performance: winding inductances, flux‐weakening capability, fault‐tolerant performance; and third, parasitic effects: cogging torque, iron loss, rotor eddy current loss, unbalanced magnetic force, acoustic noise and vibration.

Many fractional slot PM machine topologies exist. Owing to rich mmf harmonics, fractional slot PM brushless machines exhibit relatively high rotor eddy current loss, potentially high unbalanced magnetic force and acoustic noise and vibration, while the reluctance torque component is relatively low or even negligible when an interior PM rotor is employed.

This is the first overview paper which systematically reviews the recent development and research challenges in fractional‐slot PM machines. It summarizes their various structural and design features for EV/HEV applications.

This paper aims to show a complete optimization tool that can be used for the design of coaxial magnetic gears. In the first part, the paper deals with the semi-analytic modelling of these machines and also discusses how to reduce the computational efforts. In the second part, an optimization algorithm is adopted for finding the Pareto optimal geometries.

The machine is subdivided into a set of domains according to their physical and geometrical properties, and the potential distribution is found semi-analytically in them under some simplifying hypothesis. A loss estimation is performed for both ferromagnetic and permanent magnet regions. A stochastic differential evolution (DE) algorithm for multi-objective constrained problems is then applied.

It is shown that the presented design tool gives results in accordance to finite element method (FEM)-based analysis keeping the advantages of robustness and simplicity of the analytical methods. The DE-based strategy performs well on the magnetic gear optimization problem.

The proposed tool appears to be a good starting point when designing coaxial magnetic gears. The optimal Pareto points can be used as initial seeds of FEM-based optimizations, resulting in a cheaper computational method with respect to a full FEM optimization.

This paper takes inspiration from recent works on magnetic gear modelling and completes the design procedure with a suitable efficiency estimation. The paper also shows how to use mature optimization strategies to solve the constrained multi-objective magnetic gear design problem.

In semi-analytical modeling of spoke-type permanent-magnet (PM) machines (STPMM), the saturation effect is usually neglected (i.e. iron parts are considered to be infinitely permeable) and the PM magnetization is assumed tangential (i.e. magnetization pattern is considered to be tangential-parallel). This paper aims to present an improved two-dimensional (2D) subdomain technique for STPMM with the PM magnetization orientation in quasi-Cartesian coordinates by using hyperbolic functions considering non-homogeneous Neumann boundary conditions (BCs) in non-periodic regions and by applying the interfaces conditions (ICs) in both directions (i.e. t- and θ edges ICs).

The polar coordinate system is transformed into a quasi-Cartesian coordinate system. The rotor and stator regions are divided into primary subdomains, and a partial differential equation (PDE) is assigned to each subdomain. In the PM region, the magnetization orientation is considered in the equations. By applying BCs, the general solution of the equations is determined, and by applying the ICs, the corresponding coefficients are determined.

Using the proposed coordinate system, the general solution of PDEs and their coefficients can mathematically be simplified. The magnetic field and non-intrinsic unbalanced magnetic forces (UMF) calculations have been performed for three different values of iron core relative permeability (200, 800 and ∞), as well as different magnetization orientation values (135 and 80 degrees). The semi-analytical model based on the subdomain technique is compared with those obtained by the 2D finite-element analysis (FEA). Results disclose that the PM magnetization angle can affect directly the performance characteristics of the STPMM.

A new model for prediction of electromagnetic performances in the STPMM takes into account magnetization direction, and soft magnetic material relative permeability in a pseudo-Cartesian coordinate system by using subdomain technique is presented.

Introduces the fourth and final chapter of the ISEF 1999 Proceedings by stating electric and magnetic fields are influenced, in a reciprocal way, by thermal and mechanical fields. Looks at the coupling of fields in a device or a system as a prescribed effect. Points out that there are 12 contributions included ‐ covering magnetic levitation or induction heating, superconducting devices and possible effects to the human body due to electric impressed fields.

The purpose of this paper is to propose a conceptual framework – the 4V model – for better understanding how global brands create firm value. Organized around the global brand value chain, the 4V model includes four sets of value-creating activities: first, valued brands; second, value sources; third, value delivery; and fourth, valued outcomes.

The approach is conceptual with illustrative examples.

The sources of global brand value and the processes through which global brands contribute to firm value differ systematically across types of global brands. This paper highlights interrelations and how different activities built upon and reinforce each other.

The 4V model ties together broad strands of research conducted to date and offers insights into ways the paper might better understand and study global brands. It should inspire empirical research on the associations between the 4Vs.

International marketing managers should be able to develop and evaluate global brand strategies more effectively using the 4V model described in this paper. Linking their strategies to valued outcomes puts marketers more firmly at the level in the organization they deserve, namely, the C-Suite.

The framework offered in this paper is unique in that it blends insights obtained from multiple sources, namely, academic research, articles that appeared in the business press, case studies, and interactions with managers and policy makers around the world.

Analysis of test data monitored for a number of electric machines from the low volume production line can lead to useful conclusions. The purpose of this paper is to trace the machine performance to find quality-related issues and/or identify assembly process ones. In this paper, the monitoring of experimental data is related to the axial flux motor (AFM) used in hybrid electric vehicle (HEV) and in electric vehicle (EV) traction motors in the global automobile market.

Extensive data analyses raised questions like what could be the causes of possible performance deterioration of the AFM and how many electric motors may not pass requirements during operation tests. In small and medium research units of AFM for HEV or EV, engineers came across a number of serious issues that must be resolved. A number of issues can be eliminated by implementing methods for reducing the number of failing AFMs. For example, improving the motor assembly precision leads to reduction of the machine parameters deterioration.

Assembly tolerances on electric motor characteristics should be investigated during motor design. The presented measurements can be usable and can point out the weakest parts of the motor that can be a reason for the reduced efficiency and/or lifetime of the AFM. Additionally, the paper is addressed to electric motor engineers designing and/or investigating electric AFMs.

Performance of AFM was monitored for a number of identical motors from low volume production line. All tested motors were operated continuously for a long period of time and the tests were repeated every few weeks for half a year to check the reliability of motor design and indicate how much the motor parameters may change. The final results point how many motors fail the requirements of motor performance. A few batches of AFM were selected for testing. Each batch represents a different size (nominal power) of the same type of AFM.

The purpose of this paper is to develop surrogate models, using deep learning (DL), that can facilitate the application of EM analysis software. In the current status quo, electrical systems can be found in an ever-increasing range of products that are part of everyone’s daily live. With the advances in technology, industries such as the automotive, communications and medical devices have been disrupted with new electrical and electronic systems. The innovation and development of such systems with increasing complexity over time has been supported by the increased use of electromagnetic (EM) analysis software. Such software enables engineers to virtually design, analyze and optimize EM systems without the need for building physical prototypes, thus helping to shorten the development cycles and consequently cut costs.

The industry standard for simulating EM problems is using either the finite difference method or the finite element method (FEM). Optimization of the design process using such methods requires significant computational resources and time. With the emergence of artificial intelligence, along with specialized tools for automatic differentiation, the use of DL has become computationally much more efficient and cheaper. These advances in machine learning have ushered in a new era in EM simulations where engineers can compute results much faster while maintaining a certain level of accuracy.

This paper proposed two different models that can compute the magnetic field distribution in EM systems. The first model is based on a recurrent neural network, which is trained through a data-driven supervised learning method. The second model is an extension to the first with the incorporation of additional physics-based information to the authors’ model. Such a DL model, which is constrained by the laws of physics, is known as a physics-informed neural network. The solutions when compared with the ground truth, computed using FEM, show promising accuracy for the authors’ DL models while reducing the computation time and resources required, as compared to previous implementations in the literature.

The paper proposes a neural network architecture and is trained with two different learning methodologies, namely, supervised and physics-based. The working of the network along with the different learning methodologies is validated over several EM problems with varying levels of complexity. Furthermore, a comparative study is performed regarding performance accuracy and computational cost to establish the efficacy of different architectures and learning methodologies.