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Modelling and controlling heat transfer in rotating electrical machines is very important as it enables the design of assemblies (e.g., motors) that are efficient and durable under multiple operational scenarios. To address the challenge of deriving accurate data-driven estimators of key motor temperatures, we propose a multi-objective strategy for creating Linear Regression (LR) models that integrate optimised synthetic features. The main strength of our approach is that it provides decision makers with a clear overview of the optimal trade-offs between data collection costs, the expected modelling errors and the overall explainability of the generated thermal models. Moreover, as parsimonious models are required for both microcontroller deployment and domain expert interpretation, our modelling strategy contains a simple but effective step-wise regularisation technique that can be applied to outline domain-relevant mappings between LR variables and thermal profiling capabilities. Results indicate that our approach can generate accurate LR-based dynamic thermal models when training on data associated with a limited set of load points within the safe operating area of the electrical machine under study.

In this paper, a new system-level sequential Taguchi method (SLSTM) is proposed to achieve the optimal solution with high robustness for switched reluctance motor (SRM) drive systems. An SRM drive system consisting of a segmented-rotor SRM and the angle position controller is investigated as a case study. In the implementation, the optimization function contains torque, loss, and torque ripple. The control factors of the system are selected according to the sensitivity analysis results. Manufacturing tolerances are considered to guarantee that the optimal solution features low sensitiveness to uncertainties. The process of defining the design levels of all the control factors and noise factor is illustrated and the orthogonal array is established. The optimization of the SLSTM is carried out sequentially until the certain convergence condition is satisfied. Finally, the component-level sequential Taguchi method (CLSTM) is carried out for comparison. It appears that the proposed SLSTM is efficient in searching for the robust optimal solution for the SRM drive system. Besides, it can achieve better output performance, such as higher average torque and lower torque ripple, and a higher level of robustness compared with the initial design and CLSTM.

This paper investigates and compares two popular combined winding configurations for bearingless machines, the multi-phase (MP) and dual-purpose no-voltage (DPNV) windings. Each of these winding configurations has the ability to independently create suspension force and drive torque using the same stator coils. The paper develops analogous bearingless motor force and torque models, space vector equivalent circuits, and a current regulation architecture for each combined winding approach. A comprehensive comparison is made between the two combined winding topologies by using these analogous models and through an experimental study. It is shown that, from a motor design perspective, these combined winding topologies are equivalent for certain design conditions, but that each winding places very different demands upon the drive power electronics. The paper concludes that the optimal choice of winding configuration depends on the machine parameters and the expected loading and drive cycle. When compared in terms of total current stress exerted on the bearingless drive, the MP winding is preferred when over half of the coil current is used to produce torque rather than force. In terms of the drive's volt-ampere rating, the DPNV winding is shown to be advantageous for high operating speeds and low torque values.

This article discusses a method to consider the effects of the manufacturing uncertainties on the additional cogging torque harmonics. With this method, the sensitivity analysis can be incorporated into the design stage instead of just checking it afterwards. With the authors’ previous work of the worst-uncertain-combination-analysis (WUCA) method, several design parameters and their effects on the additional cogging torque harmonics under uncertainties are discussed. It is demonstrated that a design with inherent robustness can be obtained through the proper selection of pole-arc coefficient and slot-opening width. In addition, a sensitivity index is proposed to estimate the effects of parameter uncertainties on torque harmonics. Some suggestions on improving the design robustness are consequently provided from the aspects of tolerances control and parameter design. The deductions are verified through finite element analysis (FEA) on different pole/slot configurations.

This article introduces the design and optimization of line-start synchronous machines in fractional horsepower industry applications. The proposed optimized design will be considered as a replacement for the induction machine driven from an ac drive. The optimized design will be achieved using a 2-D finite-element transient analysis and multi-objective optimization algorithm. For the optimization, a multi-objective self-organizing migrating algorithm was chosen. The optimization results are compared with the more popular nondominated sorting genetic algorithm (NSGA-II) algorithm, to prove algorithm sufficiency with three objectives and five design parameters in the area of the electromagnetic design application.

This article presents a new method for multi-objective robust design optimization of electrical machines and provides a detailed comparison with so far introduced techniques. First, two robust design approaches, worst-case design and design for six-sigma, are compared with the conventional deterministic approach for multi-objective optimization. Through a case study on a permanent magnet motor, it is found that the reliabilities of motors produced based on robust designs are 100% under the investigated constraints, while the reliabilities of deterministic designs can be lower than 30%. A major disadvantage of robust optimization is the huge computation cost, especially for high-dimensional problems. To attempt this problem, a new multi-objective sequential optimization method (MSOM) with an orthogonal design technique and hypervolume indicator (as a measure of convergence) is proposed for both deterministic and robust design optimization of electrical machines. Through another case study, it is found that the new MSOM can improve motor performance and greatly reduce the computational cost. For the robust optimization, the number of required finite element simulations can be reduced by more than 40%, compared with that required by the conventional approach. The proposed method can be applied to many-objective (robust) design optimization of electrical machines.

In this article, a novel system-level robust design optimization method is presented to improve the performance of switched reluctance motor (SRM) drive systems under multiple operating conditions. Based on typical driving cycles of electric vehicles (EVs), five typical driving modes of the SRM are determined. The optimization objectives in each driving mode are established. The significant parameters of the motor and controller of each driving mode are selected as the optimization variables by using the sensitivity analysis. In order to simplify the optimization process, correlation analysis is performed to determine the coherence of the objective functions of all driving modes. Then, a sequential Taguchi method is applied to find an optimal design which is less sensitive to the noise factors. To verify the effectiveness of the proposed method, an SRM drive system applied in EVs with a 12/10 SRM and angle position control method is investigated. It is found that the proposed method can significantly reduce the torque ripple and improve the comprehensive performance. Finally, a 12/10 SRM is prototyped and tested to validate the simulation results.

This article is about analyzing the change of the optimal design's shape and respective performance measures of permanent magnet synchronous machines considering the manufacturing impact on soft magnetic materials. Typically, the respective material degradation is neglected when optimizing electric machines. However, recent studies reported a significant effect of the manufacturing on soft magnetic materials and, consequently, on the machine's performance. This especially holds for fractional horsepower designs, where the deteriorated zone is relatively wide compared to the overall dimensions. In this work, machine designs are analyzed by both evaluating them with and without considering the manufacturing impact. A case study is defined with typical measures, i.e., efficiency and cogging torque, selected as objectives. The performances and their change due to manufacturing are quantified for all variants investigated. The results allow for comparing the designs’ sensitivities regarding the manufacturing impact. In addition, considering a change of emphasis from best rated performance to a tradeoff regarding rated characteristics versus robustness, the respective change of the optimal design's shape is studied. A conclusion is drawn if and how the manufacturing impact shall be incorporated to future optimization scenarios to find designs with promising rated performance while guaranteeing low sensitivity regarding the manufacturing impact.

Investigating the impact of tolerance-affected parameters on the performance of electric machines gets evermore important. A major reason is that designs are optimized and thus are on or close to boundaries regarding performance constraints. Moreover, highly-utilized designs are obtained due to optimization of cost or specific measures like torque or power density following increased tolerance sensitiveness. To find the best tradeoff regarding rated performance and robustness, appropriate measures should be incorporated to optimization problems. Often, computational effort acts as barrier for succesful implementation, as numerous design parameters feature considerable variation. To minimize this effort, this work is about screening and quantifying the importance of tolerance-affected parameters. By contrast to classical sensitivity analysis, the likelihood of parameters to change must be considered. Different measures are introduced and compared by applying them to an electric machine scenario. The detailedness, accuracy, and fluctuation of the prediction is evaluated. At the best, the analysis does not require specific data point arrangements, e.g, orthogonal sets. The derived results can be utilized for determining most influential parameters and to focus on those during optimization. The overall computational effort can thus be signficantly reduced and tolerance analyses can be more easily incorporated to optimization problems.

This work is about analyzing surface mounted permanent magnet machines regarding their sensitiveness related to erroneous magnet positioning. A finite element analysis based approach is presented and different topologies in terms of slot and pole count are compared. The study further includes the analysis of multiple magnet widths and stator teeth widths. By contrast to most of previous studies, the work is based on evaluating the cumulative distribution function of the cogging torque in case of non-idealities. A Monte Carlo importance sampling based strategy is focused. This approach facilitates studying arbitrary tolerance distributions. Results reveal that topologies with particularly promising rated cogging torque behaviour exhibit the most significant performance degradation in presence of tolerances. A linear relationship is identified for cogging torque performance as function of the accuracy in magnet positioning. Results emphasize the necessity of tolerance analyses for electric machine design to not overrate their performance in the presence of manufacturing uncertainties.

This paper presents an analysis of a permanent magnet synchronous machine (PMSM) with an unequal teeth width. The use of the concentrated nonoverlapping winding, also known as tooth-coil winding, has certain advantages. In addition, asymmetric features can be exploited by adopting this winding configuration to improve the machine performance. The procedure involves increasing the stator tooth width and, to the same extent, reducing the adjacent stator tooth width. In this paper, an analytical method was employed to predict and understand the back-EMF behavior, and a finite element analysis (FEA) was performed to verify the analytical method. Furthermore, a detailed study of the machine performance (electromagnetic torque, flux density, and losses) was carried out by the FEA. The machine was built and tested to validate the performance of the machine.

This article is about investigating the tolerance sensitiveness of electric machines regarding permanent magnet failures. The focus is on established permanent magnet synchronous machine designs in terms of slot and pole count. A finite-element-based approach is presented that allows analyzing single air-gap magnetomotive force (MMF) harmonics and the therefrom caused effects. Their corresponding magnitudes can be estimated based on a relatively fast analytical evaluation of multiple simultaneous magnet failures. Examples of evaluated magnet tolerances are misplaced permanent magnets or differences in the magnets' width, height, or magnetization strength, and direction. In contrast to investigating numerous different combinations of permanent magnet failures, general statements regarding the tolerance sensitiveness of the machines can be derived within reasonable time. A case study is defined, featuring six different topologies and the sensitivity on cogging torque and linked flux harmonics is evaluated. Low-order MMF harmonics are focused, as those typically feature the most significant impact on the machine's performance. Significant differences among the configurations regarding cogging torque increase and additional linked flux harmonics compared to the ideal structure are observed. The presented general technique can thus help engineers to rate the sensitivity of machine designs with regard to tolerance-induced MMF harmonics. The presented approach is thus useful at both the early design stage to select the most suitable machine configuration in terms of the number of slots, poles, and the winding arrangement, and during machine design optimization to compare different geometrical configurations.