article · Mathematics
Designing switched reluctance motors is challenging because of the numerous interdependent physical dimensions that must be balanced simultaneously. A computational design procedure uses the non-dominated sorting genetic algorithm, known as NSGA-II, combined with finite element analysis to identify optimal machine dimensions. The optimisation routine adjusts multiple geometrical parameters, such as stator diameter, bore diameter, axial length, shaft diameter, air gap length, and pole dimensions. These factors are evaluated against three competing design objectives: maximising average torque, maximising motor efficiency, and minimising total iron weight. The approach was tested on two common switched reluctance motor configurations, specifically 8/6 and 6/4 setups. Simulation results indicate that the integrated algorithm and finite element approach produces motor designs with improved torque and efficiency alongside reduced iron material requirements.
Electric motors often require trade-offs between physical weight, power output, and energy efficiency. By automating the design process through advanced multi-objective optimisation and electromagnetic simulations, motor designers can quickly find configurations that consume less material while delivering higher performance, helping to create more efficient and lighter electrical machinery.
This methodology can assist electric machine designers and manufacturing engineers looking to develop lighter and more efficient switched reluctance motors. Because the findings rely entirely on finite element simulations across standard configurations rather than physical prototype testing, the work sits at an early computational stage of development. Translating these designs into products would require experimental validation and physical prototyping.
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The design of switched reluctance motor (SRM) is considered a complex problem to be solved using conventional design techniques. This is due to the large number of design parameters that should be considered during the design process. Therefore, optimization techniques are necessary to obtain an optimal design of SRM. This paper presents an optimal design methodology for SRM using the non-dominated sorting genetic algorithm (NSGA-II) optimization technique. Several dimensions of SRM are considered in the proposed design procedure including stator diameter, bore diameter, axial length, pole arcs and pole lengths, back iron length, shaft diameter as well as the air gap length. The multi-objective design scheme includes three objective functions to be achieved, that is, maximum average torque, maximum efficiency and minimum iron weight of the machine. Meanwhile, finite element analysis (FEA) is used during the optimization process to calculate the values of the objective functions. In this paper, two designs for SRMs with 8/6 and 6/4 configurations are presented. Simulation results show that the obtained SRM design parameters allow better average torque and efficiency with lower iron weight. Eventually, the integration of NSGA-II and FEA provides an effective approach to obtain the optimal design of SRM.
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DOI: 10.3390/math9050576
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