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Abstract The authors present two fast and accurate methodologies for the computation of eddy current losses in the axially laminated rotor of a synchronous reluctance machine. The methodologies are based on different combinations of the finite element method in time and frequency domains with 2D and 3D formulations. First, a comparative study of the 2D and 3D formulations for loss calculation is presented, considering various load angles of the machine to illustrate the problem of eddy‐current losses in this type of machines and its dependence on the load angle. The influence of the iron saturation on the loss calculation is also evaluated in these computations. A novel correction factor based on the computations at two load angles is proposed to convert the losses computed from a 2D model to match those computed from a 3D model. For the sake of generality, investigations are also conducted for various thicknesses of the lamination layers and different machine lengths, and an analytical method to describe the dependency of eddy‐current losses on the load angle of the machine is introduced. Moreover, a simplified method is proposed for modelling eddy currents in the frequency domain and calculating losses in an axially laminated structure based solely on the results of a magnetostatic solution. The results obtained by the simplified model demonstrate excellent agreement with the full 3D magneto‐dynamic simulation. Overall, the findings contribute to understanding and accurately characterising the eddy current losses in axially laminated rotors, offering potential insights for designing and optimising axially laminated synchronous reluctance electric machines.

Abstract This paper introduces a novel application of the inverse modelling method, which is designed to estimate core losses in both the stator and rotor regions of an electrical machine. The technique focuses on the use of short‐term transient temperature measurements obtained from the stator core of a slotless induction machine, with a focus on validating the measured temperature rise through a forward model. The measurement setup involves two primary approaches: (i) the use of thermal sensors embedded in a printed circuit board inside the stator core and (ii) surface sensors embedded on the stator yoke. Through the use of this innovative approach, the results indicate that the inverse modelling technique is highly effective in predicting core losses based on short‐time transient temperature rise measurements.

Industry is increasingly adopting high-speed electric machines due to their high-power density and low cost. However, increasing the speed leads to many challenges that are worth to study since they have relevant impact on the design of the machine. One of these challenges is the losses in stator winding assembly. At low frequency, losses in windings are usually assumed equal to DC losses. However, at high frequency some phenomena become noticeable and worth evaluating since they lead to noticeable losses. AC losses in windings are due to skin effect, proximity effect, and circulating currents. The main models of AC losses in windings in the literature are presented for both circular and rectangular wires. These models are compared using both interior and surface-mounted permanent magnet synchronous machines. Results show that for proximity effect, analytical model is convenient when considering armature reaction only, however, when considering on load case, hybrid model with simple FEA is suitable thanks to its interesting trade-off between accuracy and computation time, which makes it suitable for large-scale optimization. For circulating currents effect, finite element model is unavoidable. Moreover, two important properties concerning circulating currents are stated as well as their mathematical proofs.

The aviation industry is undergoing electrification due to the increased global focus on reducing emissions in air traffic. Regarding the volatility of raw material prices, one main objective is the increase in the specific power of the motor. This matches the ambitious targets of the CoE project (Center of Excellence) in Finland on high-speed electric motors. The targeted specific power is 20 kW/kg. In this work, motors are designed and optimized for a fully electric regional aircraft. motors with different slot/pole configurations and rotational speed values are studied to determine the advantage of increasing speed in terms of weight reduction. As increasing speed requires the use of a gearbox, the overall weight of the motor and the gearbox is evaluated in post-processing, which allows for determining the impact of high speed on the overall weight. An optimization tool coupled with an electromagnetic and mechanical analysis is used to optimize 1 MW surface mounted permanent magnet synchronous motors (S-PMSMs) for given specifications of regional electric aircraft. Optimization results indicate that there is considerable gain in terms of overall weight only when increasing the speed to the range of 10,000–15,000 rpm.

Vibration signals carry important information about the health state of a ball bearing and have proven their efficiency in training machine learning models for fault diagnosis. However, the sampling rate and frequency resolution of these acquired signals play a key role in the detection analysis. Industrial organizations often seek cost-effective and qualitative measurements, while reducing sensor resolution to optimize their resource allocation. This paper compares the performance of supervised learning classifiers for the fault detection of bearing faults in induction machines using vibration signals sampled at various frequencies. Three classes of algorithms are tested: linear models, tree-based models, and neural networks. These algorithms are trained and evaluated on vibration data collected experimentally and then downsampled to various intermediate levels of sampling, from 48 kHz to 1 kHz, using a fractional downsampling method. The study highlights the trade-off between fault detection accuracy and sampling frequency. It shows that, depending on the machine learning algorithm used, better training accuracies are not systematically achieved when training with vibration signals sampled at a relatively high frequency.

This paper presents an inverse thermal modeling technique to determine the core losses from the temperature rise inside the transformer core. For this purpose, initially, a customized printed circuit board (PCB) with thermal sensors is used to measure the temperature rise. Afterward, a 3D magneto-thermal forward model is developed to validate the temperature rise. The accuracy of the forward model is checked by comparing the simulated core losses and temperature rise of the transformer with experimental measurements for different supply conditions. The results show that the forward model can accurately estimate the core losses with a maximum relative error of less than 2.7% and predict the temperature rise in the core with a maximum relative error of less than 6.2%. Lastly, after ensuring the accuracy of the forward model, an inverse modeling technique is applied to the 3D thermal model to predict the core losses of the transformer directly from the measured temperature rise. The accuracy of the inverse model in estimating the core losses is checked by comparing the results with experimental measurements. The novel approach for the PCB design besides the inverse model shows that the technique can be applied to estimate the core losses directly from the measured temperature rise inside the core with a relative error of 2.7% compared to experiments.

This article presents an intelligent and accurate framework for fault diagnosis of induction motors using light gradient boosting machine (LightGBM). The proposed framework offers promising generalization ability when the testing data contains new unseen operating conditions unavailable during the training process. After the acquisition of vibration signals and feature extraction in multiple domains, we perform an iterative feature selection (FS) approach by utilizing a modified version of recursive feature elimination (RFE) and the features’ importance scores obtained by LightGBM. To prevent overfitting and subsequent selection bias, an outer resampling loop encompasses the whole process of our RFE-LightGBM algorithm. Moreover, instead of the conventional resampling methods based on K-fold cross-validation (CV) or leave-one-out CV (LOOCV), we use a new scheme called leave-one-loading-out CV (LOLO-CV). Leveraging LOLO-CV, the proposed FS method identifies the optimal feature subset, making the fault diagnosis robust under changing operating conditions. Then, the final classification is performed with optimal feature subset by training a new LightGBM model with adjusted hyperparameters employing Bayesian optimization. Experimental results from two real case studies show that our proposed fault diagnosis framework achieves accuracies between 98.55% and 100% for various testing scenarios. For example, for the worst-case testing scenario in the bearing dataset of Case Western Reserve University where the no-load data (0hp) is absent during the training process and is only used for testing, the testing accuracy of LightGBM classifier before and after applying the proposed RFE-LightGBM-FS method is 88.04% to 97.23%, respectively. Using the Bayesian hyperparameter optimization further improves the accuracy to 98.55%.

Iron losses in laser-cut toroidal samples of 12 mm thick steel laminations used in large synchronous motors are studied. Eddy currents in the lamination cross-section are solved with the 2-D finite element method while applying a constitutive law based on the Jiles-Atherton hysteresis model. The effect of cutting on the material properties is included by a continuous local material model approach, which enables to express the material properties as a function of distance from the cutting edge. The accuracy of the model is validated by comparing the simulations and experimental measurements of five toroidal samples assembled from concentric rings with different widths. Highly accurate results are obtained in terms of both the matching of B-H loops and the total loss values with an average relative error less than 2.9%. The results show that the hysteresis loss under quasi-static excitation increases up to 20.4% due to the effect of cutting. It is observed that the eddy-current loss becomes dominant over the hysteresis loss even at 5 Hz, and this eddy-current loss decreases up to 72.5% as the number of concentric rings increases. The presented model and the results accurately show how iron losses occur in thick materials and how they are affected by the cutting process.

A discrete winding function analysis (DWFA) based approach for the modelling of skewed rotor cage-type induction motor with minimal simulation time is presented in this paper. The rotor slot skew has a significant attenuating impact on principal slotting harmonics (PSH) or rotor slotting harmonics (RSH). These harmonics can play a significant role in sensor-less speed estimation and condition monitoring of induction machines. The advanced fault diagnostic algorithms are becoming increasingly dependent on the fast and accurate mathematical models of electrical machines. The most accurate models are based on the finite element method (FEM), but the computational complexity and the required simulation time make them unsuitable for model-dependent fault diagnostic algorithms. Moreover, as most models are 2D, they cannot incorporate axial asymmetries such as rotor slot skews. Furthermore, most analytical models, such as modified winding function analysis (MWFA), depend upon the continuous integration functions, increasing complexity while implementing them in the online environment in digital signal processing boards. To resolve all those issues, DWFA based model is proposed in this paper, which can simulate the majority of the faults in negligible time compared to the corresponding FEM models. The impact of slot skews and unbalanced power supply on the current spatial harmonics is studied, and the results are compared with the practical measurements taken from the laboratory setup.

The additive manufacturing approach is considered a new manufacturing technology method and is evolving dynamically in recent years. It is advancing and achieving as the key enabling technology in a wide range of applications, from medical sciences to the aerospace and automotive industries. This novel approach opens a new path to overcome the conventional manufacturing problems and challenges by providing more design freedom, new ranges of materials, lightweight and complex geometries. According to demands metrics such as lightweight and high power density motors. This offers clear motivation to develop the advanced thermal management method with new materials and a novel additive manufacturing (AM) approach. The paper aims to provide a comprehensive review of all the attempts in various electrical machines' thermal management methods using AM method. It considers the opportunities and challenges that designers are facing while implementing these approaches. Finally, the authors make some comments/forecasts on how the AM could improve the performance and manufacturability of the future thermal management system of electrical machines.

Nowadays a lot of attention is paid on the issues of global warming and climate change. Human impact on the environment is noticeable from the aspect of resource life cycles. Energy efficiency requirements have led to the research and development of alternative technologies for the rotating electrical machines. The life cycle assessment brings out important procedures which can help to reduce machines’ impact on the environment, being therefore an instrument for the assessment of the influence of particular products on the environment from cradle to grave – beginning with working out the materials, followed by manufacturing, transporting, marketing, use, and recycling. Three types of electrical machines have been chosen for comparison: synchronous reluctance motor, permanent magnet assisted synchronous reluctance motor and induction motor. The article presents a life cycle assessment case study based on experimental results of motors designed by the research group.

As Guest Editors of this Special Issue, it was our responsibility to ensure that the contributions to the issue related to the extensive field of electromechanical energy conversion, with a special focus on the design, materials, and modeling of electrical machines [...]

This paper presents a practical thermal model of a synchronous generator for high-power applications. This model couples the lumped parameter thermal network and coolant network together to utilize the impact of the coolant’s temperature rising over the machine. Furthermore, the advanced multi-planes technique provides a more precise and higher resolution temperature distribution of various machine sections. Therefore, the machines are divided into five planes; three belong to the active part, and two are added to model the machine’s drive and non-drive end-regions. Furthermore, the paper pays special attention to describing the challenges and providing solutions to them during the heat transfer modeling and analysis. Finally, the analytical model is verified using experimental results on a synchronous generator with a salient pole rotor and an open self-ventilation (OSV) cooling system by comparing the analytical and experimental results. As a result, good correspondence between the estimated and measurement results is achieved.

The hairpin winding configuration has been attracting attention as a solution to increase the power density of electric vehicle motors by enhancing the slot-filling factor. However, this winding configuration brings high AC losses during high-speed operation and we require new approaches to tackle this challenge. This paper considers reducing AC losses by proposing two main methods: correct transposition of conductors in parallel paths, and enhancing the number of conductor layers in a slot. First, the proper connection of conductors in parallel paths is considered, and the essential rules for this purpose are described. Next, the paper uses a numerical approach to deal with the effect of incorrect conductor transposition in winding paths on generating additional AC losses due to circulating currents. Finally, the impact of the number of conductor layers in the mitigation of AC losses is also discussed in detail. According to the results, by increasing the number of layers, ohmic losses in the layer near the slot opening dramatically decrease. For instance, ohmic losses in the layer near the slot opening of the eight-layer setup were 82% less than the two-layer layout.

Electric retrofitting (e-retrofitting) is a viable option for accelerating the renewal of heavy-duty vehicle fleets to reduce the related emissions. We introduce a simulation-based assessment of e-retrofitting strategies for heavy-duty vehicles. Our simulation tool, an electric vehicle fleet simulation toolbox, comprises three modules, namely driving cycles, vehicle dynamics, and vehicle profiles. The first allows for the creation of realistic driving cycles based on GPS data from real routes. The vehicle dynamics and vehicle profiles incorporate, e.g., the modelling of the powertrain and driving conditions. Ten realistic driving cycles were created and used for investigating and comparing three different powertrain alternatives, including the original diesel powertrain, electric with a single-speed transmission and electric with a multi-speed transmission. The vehicles were simulated in two different heavy-load scenarios. First, driving with a cargo load represented by the maximum vehicle weight and second, driving with snow ploughing. We found that the multi-speed transmission in an electric heavy-duty truck significantly improved its traction performance and gradeability. On the other hand, the effect on the electric powertrain efficiency, and thereby on the energy consumption, remained rather minor. Considering the given workload scenarios, our results advocate employing rather than omitting the gearbox in the e-retrofit truck process.

This paper presents a hybrid thermal modeling methodology to analyze the temperature performance of radial flux electrical machines. For this purpose, the 2D finite element model of the active part of the machine is coupled with a lumped parameters thermal circuit of the end-winding region. A synchronous reluctance machine is used to validate the proposed approach. The results from the proposed method are compared with the experimental ones, which are obtained from a prototype machine. The computations show that the 2D FE model underestimates the temperature rise in the machine as it does not account for the power losses in the end-windings. The hybrid model accounts for these losses as well as for the heat dissipation in the end-winding region.

Motor-drive systems have the most significant share in industrial energy consumption, which requires a deep study in every aspect of the field. This paper presents a synchronous reluctance motor (SynRM) drive system based on Plecs RT box 1. The system’s design provides the opportunity for the open-loop and closed-loop control of the motor and a characteristic performance analysis of the motor. This paper focuses on the hardware implementation of a research laboratory setup and the precise vector control of the SynRM in real-time. The application of the digital controller and inverter to drive SynRM is examined. The voltage, current, and speed transducers were employed for monitoring the protective measures and to control the motor in the closed-loop. The design of the signal conditioning and the intermediary cards for isolation and data acquisition are described in detail. An algorithm is proposed to measure the whole system parameters, including motor, inverter, and cables. Thanks to the RT box 1, the principle of real-time simulation of control algorithms is investigated, and the rapid control prototyping of field-oriented control (FOC) of SynRM was implemented. The simulation of the system was carried out in the Plecs platform, and the results are presented. The experimental results of the implemented control algorithms validate the setup’s performance and the control algorithm. Finally, as a study of the motor’s performance, the efficiency map of the motor is drawn in different speed and torque ranges.

Finite element analysis of transformers with stranded conductors can be computationally heavy due to the fine mesh required for winding domains. In this paper, a domain decomposition method with subdomain pre-processing is proposed to overcome with this problem. In order to solve the field-circuit problem with finite element analysis, the winding domain is modelled initially. Next, the reduced matrix system is obtained from the winding domain formulation. The solution of the entire problem is obtained by solving the winding domain formulation and the reduced matrix system separately. The method is tested on a transformer model in no load operation. Accurate solutions are obtained in both frequency and time domains 7-9 times faster than when using brute-force method.

Random-wound electrical machines often suffer from high circulating current losses. These losses vary from machine to machine in a stochastic fashion. This study proposes an improved sampling algorithm for quantifying the uncertainty inherent in random windings. The algorithm is then combined with a circuit model to perform Monte Carlo analysis on the losses. The results are compared to measurements, and a good agreement is observed.

In this study, the authors presented a systematic design procedure of outer rotor bearingless synchronous reluctance motor (SynRM). The proposed motor is compared with an already prototyped inner rotor bearingless SynRM at different operation points. It has been shown that a properly designed outer rotor SynRM can produce higher levitation forces than the inner rotor counterpart. The inner rotor motor produces higher torque and eccentric forces than the outer rotor motor.

This paper proposes a new surrogate optimization routine for optimal design of a direct on line (DOL) squirrel cage induction motor. The geometry of the motor is optimized to maximize its electromagnetic efficiency while respecting the constraints, such as output power and power factor. The routine uses the methodologies of Latin-hypercube sampling, a clustering technique and a Box–Behnken design for improving the accuracy of the surrogate model while efficiently utilizing the computational resources. The global search-based particle swarm optimization (PSO) algorithm is used for optimizing the surrogate model and the pattern search algorithm is used for fine-tuning the surrogate optimal solution. The proposed surrogate optimization routine achieved an optimal design with an electromagnetic efficiency of 93.90%, for a 7.5 kW motor. To benchmark the performance of the surrogate optimization routine, a comparative analysis was carried out with a direct optimization routine that uses a finite element method (FEM)-based machine model as a cost function.

Additive manufacturing of ferromagnetic materials for electrical machine applications is maturing. In this work, a full E-type transformer core is printed, characterized, and compared in terms of performance with a conventional Goss textured core. For facilitating a modular winding and eddy current loss reduction, the 3D printed core is assembled from four novel interlocking components, which structurally imitate the E-type core laminations. Both cores are compared at approximately their respective optimal working conditions, at identical magnetizing currents. Due to the superior magnetic properties of the Goss sheet conventional transformer core, 10% reduced efficiency (from 80.5% to 70.1%) and 34% lower power density (from 59 VA/kg to 39 VA/kg) of the printed transformer are identified at operating temperature. The first prototype transformer core demonstrates the state of the art and initial optimization step for further development of additively manufactured soft ferromagnetic components. Further optimization of both the 3D printed material and core design are proposed for obtaining higher electrical performance for AC applications.

This paper presents the analytical calculation of the heat transfer coefficient of a complex housing shape of a Totally Enclosed Fan-Cooled (TEFC) industrial machine when it works below 20% of its nominal speed or close to stall. Therefore, passive cooling is dominant, and most of the heat is extracted by the combination of natural convection and radiation phenomena. Under these conditions, the area-based composite approach was used for the development of the analytical calculation method. A test rig using a TEFC Synchronous Reluctance Motor (SynRM) was constructed, and the collected experimental data was used to validate the proposed analytical method successfully.

This paper presents the modeling and the broken rotor bar fault diagnostics by time–frequency analysis of the motor current under an extended startup transient time. The transient current-based nonstationary signal is retrieved and investigated for its time–frequency response to segregate the rotor faults and spatial harmonics. For studying the effect of reduced voltage on various parameters and the theoretical definition of the fault patterns, the winding function analysis (WFA)-based model is presented first. Moreover, an algorithm to improve the spectrum legibility is proposed. It is shown that by efficient utilization of the attenuation filter and consideration of the area containing the maximum power spectral density, the diagnostic algorithm gives promising results. The results are based on the machine’s analytical model and the measurements taken from the laboratory setup.

Additively manufactured soft magnetic Fe-3.7%w.t.Si toroidal samples with solid and novel partitioned cross-sectional geometries are characterized through magnetic measurements. This study focuses on the effect of air gaps and annealing temperature on AC core losses at the 50 Hz frequency. In addition, DC electromagnetic material properties are presented, showing comparable results to conventional and other 3D-printed, high-grade, soft magnetic materials. The magnetization of 1.5 T was achieved at 1800 A/m, exhibiting a maximum relative permeability of 28,900 and hysteresis losses of 0.61 (1 T) and 1.7 (1.5 T) W/kg. A clear trend of total core loss reduction at 50 Hz was observed in relation to the segregation of the specimen cross-sectional topology. The lowest 50 Hz total core losses were measured for the toroidal specimen with four internal air gaps annealed at 1200 °C, exhibiting a total core loss of 1.2 (1 T) and 5.5 (1.5 T) W/kg. This is equal to an 860% total core loss reduction at 1 T and a 510% loss reduction at 1.5 T magnetization compared to solid bulk-printed material. Based on the findings, the advantages and disadvantages of printed air-gapped material internal structures are discussed in detail.

In the above article [1], Figs. 3 and 19 are modified versions of Fig. 18 from the article. Although this source was cited in our article as [6], the captions for these figures are missing the appropriate citation/reference.

This paper presents an algorithm to remove the DC drift from the B-H curve of an additively manufactured soft ferromagnetic material. The removal of DC drift from the magnetization curve is crucial for the accurate estimation of iron losses. The algorithm is based on the sliding mean value subtraction from each cycle of calculated magnetic flux density (B) signal. The sliding mean values (SMVs) are calculated using the convolution theorem, where a DC kernel with a length equal to the size of one cycle is convolved with B to recover the drifting signal. The results are based on the toroid measurements made by selective laser melting (SLM)-based 3D printing mechanism. The measurements taken at different flux density values show the effectiveness of the method.

In this paper, the bearing faults analysis of the brushless DC motor is presented. The research method is based on the analysis of the vibration signal of healthy as well as faulty bearings by the identification of specific frequencies on the vibration spectrum. For the experiment, the most common faults were inflicted on the bearings. As the used motor is intended for electric scooter applications, seven different damages were chosen, which are highly likely to occur during the scooter operation. The main bearing faults and the possibility of fault monitoring are addressed. The vibration data are gathered by the acceleration sensors placed on the motor at different locations and the spectrum analysis is performed using the fast Fourier transform. The variation in the amplitude of the frequency harmonics particularly the fundamental component is presented as a fault indicator.

This paper presents a hybrid finite element method (FEM)–analytical model of a three-phase squirrel cage induction motor solved using parallel processing for reducing the simulation time. The growing development in artificial intelligence (AI) techniques can lead towards more reliable diagnostic algorithms. The biggest challenge for AI techniques is that they need a big amount of data under various conditions to train them. These data are difficult to obtain from the industries because they contain low numbers of possible faulty cases, as well as from laboratories because a limited number of motors can be broken for testing purposes. The only feasible solution is mathematical models, which in the long run can become part of advanced diagnostic techniques. The benefits of analytical and FEM models for their speed and accuracy respectively can be exploited by making a hybrid model. Moreover, the concept of cloud computing can be utilized to reduce the simulation time of the FEM model. In this paper, a hybrid model being solved on multiple processors in a parallel fashion is presented. The results depict that by dividing the rotor steps among several processors working in parallel, the simulation time reduces considerably. The simulation results under healthy and broken rotor bar cases are compared with those taken from a laboratory setup for validation.

Samples from FeSi4 powder were fabricated with a low power selective laser melting (SLM) system using a laser re-melting strategy. The sample material was characterized through magnetic measurements. The study showed excellent DC magnetic properties, comparable to commercial and other 3D printed soft ferromagnetic materials from the literature at low (1 T) magnetization. Empirical total core losses were segregated into hysteresis, eddy and excessive losses via the subtraction of finite element method (FEM) simulated eddy current losses and hysteresis losses measured at quasi-static conditions. Hysteresis losses were found to decrease from 3.65 to 0.95 W/kg (1 T, 50 Hz) after the annealing. Both empirical and FEM results confirm considerable eddy currents generated in the printed bulk toroidal sample, which increase dramatically at high material saturation after annealing. These losses could potentially be reduced by using partitioned material internal structure realized by printed airgaps. Similarly, with regard to the samples characterized in this study, the substantially increased core losses induced by material oversaturation due to reduced filling factor may present a challenge in realizing 3D printed electrical machines with comparable performance to established 2D laminated designs.

Estimating the additional power losses caused by an inter-laminar short circuit in electromagnetic devices using thermal measurements depends on many parameters such as thermal conductivity, heat capacity, convective heat coefficient, and size of the fault points. This paper presents a method for estimating these parameters using experimental measurement and a numerical model. The surface temperature rise due to inter-laminar short circuit fault was obtained using an infrared camera. Based on the initial temperature rise method, the least square non-linear approximation technique was used to determine the best fitting parameters of the fault region from the numerical model. To validate the results obtained, the fault region temperature rise and the total loss of the experimental sample were compared with the numerical model using the obtained parameters for different current supply conditions. The study shows that surface temperature distribution can be used to estimate the inter-laminar short circuit fault parameters and localized losses.

In this paper, a parallel estimation system of the stator resistance and the rotor speed is proposed in speed sensorless six-phase induction motor (6PIM) drive. First, a full-order observer is presented to provide the stator current and the rotor flux. Then, an adaptive control law is designed using the Lyapunov stability theorem to estimate the rotor speed. In parallel, a stator resistance identification scheme is proposed using more degrees of freedom of the 6PIM, which is also based on the Lyapunov stability theorem. The main advantage of the proposed method is that the stator resistance adaptation is completely decoupled from the rotor speed estimation algorithm. To increase the robustness of the drive system against external disturbances, noises, and parameter uncertainties, an active disturbance rejection controller (ADRC) is introduced in direct torque control (DTC) of the 6PIM. The experimental results clarify the effectiveness of the proposed approaches.

This paper presents a numerical method and computational results for acoustic noise of electromagnetic origin generated by an induction motor. The computation of noise incorporates three levels of numerical calculation steps, combining both the finite element method and boundary element method. The role of magnetic forces in the production of acoustic noise is established in the paper by showing the magneto-mechanical and vibro-acoustic pathway of energy. The conversion of electrical energy into acoustic energy in an electrical motor through electromagnetic, mechanical, or acoustic platforms is illustrated through numerical computations of magnetic forces, mechanical deformation, and acoustic noise. The magnetic forces were computed through 2D electromagnetic finite element simulation, and the deformation of the stator due to these forces was calculated using 3D structural finite element simulation. Finally, boundary element-based computation was employed to calculate the sound pressure and sound power level in decibels. The use of the boundary element method instead of the finite element method in acoustic computation reduces the computational cost because, unlike finite element analysis, the boundary element approach does not require heavy meshing to model the air surrounding the motor.

In this paper, a new vector control strategy is proposed to reduce torque ripples and harmonic currents represented in switching table-based direct torque control (ST-DTC) of a six-phase induction motor (6PIM). For this purpose, a new set of inputs is provided for the switching table (ST). These inputs are based on the decoupled current components in the synchronous reference frame. Indeed, using both field-oriented control (FOC) and direct torque control (DTC) concepts, precise inputs are applied to the ST in order to achieve better steady-state torque response. By applying the duty cycle control strategy, the loss subspace components are eliminated through a suitable selection of virtual voltage vectors. Each virtual voltage vector is based on a combination of a large and a medium vector to make the average volt-seconds in loss subspace near to zero. Therefore, the proposed strategy not only notably reduces the torque ripples, but also suppresses the low frequency current harmonics, simultaneously. Simulation and experimental results clarify the high performance of the proposed scheme.