Multiobjective Electric Machine Optimization for Highest Reliability Demands

This article is about illustrating a workflow for incorporating reliability measures to typical electric machine design optimization scenarios.Such measures facilitate comparing designs not only for rated conditions,but also allow to analyze their performance in the presence of unevitable tolerances.Consequently,by additionally considering reliability or robustness as objectives compared to conventional optimization scenarios,designs featuring low parameter sensitiveness can be obtained.The analysis of the design’s reliability as part of solving optimization problems involves a significant increase in required numerical evaluations.To minimize the associated prolongation of the runtime,an approach featuring a design of experiments based reduction of required computations and a consequent surrogate modeling technique is presented here.After successful training,the metamodel can be applied for fast evaluating lots of different parameter combinations.A test problem is defined and analyzed.Based on the observed findings,the necessity of incorporating robustness evaluations to machine design optimization becomes evident.In addition,the derived models allow for studying the impact of any tolerance-affected parameter on the machine performance in detail.This facilitates further beneficial studies,as for instance the analysis of selected changes of tolerance levels rather than a general minimization of the respective ranges which usually is associated with high production cost.

Cogging Torque Sensitivity considering Imperfect Magnet Positioning for Permanent Magnet Machines of Different Slot and Pole Count

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.

Robustness Criteria for Concurrent Evaluation of the Impact of Tolerances in Multiobjective Electric Machine Design Optimization

This article is about a comparison of different measures for determining the robustness or reliability of electric machine designs in the presence of inevitable tolerances.The selected criteria shall be suitable for concurrent evaluation in the course of solving state-of-the-art large scale multi-objective opti-mization problems.In the past,besides particularly customized criteria,mainly gradient based measures,worst case information,or standard deviation based quantities were considered.In this work,the quantile measure is introduced for electric machine design optimization and compared with the existing solutions.The evaluation of a design’s robustness is typically examined based on finite element simulations.As for most measures a signif-icant number of parameter combinations and thus computations are required,a surrogate model assisted approach is presented to minimize computational effort and runtime.A test problem is defined and analyzed to illustrate the differences of selected robustness measures.Results reveal the importance of considering robustness in the optimization process.Moreover,a careful choice of appropriate measures has to be taken.Selected designs are compared and conclusions and an outlook on future activities are presented.