A Computationally Efficient Multi-Objective Design Optimization of SRM Using K-Means Clustering and Artificial Neural Networks

This paper presents a novel methodology for realizing a computationally efficient multi-objective design optimization (CE-MODO) of switched reluctance motors (SRMs). Existing MODO approaches rely heavily on static and dynamic finite element analysis (FEA) to evaluate the electromagnetic performance parameters, which, despite their accuracy, incur significant computational costs. While analytical models reduce the dependence on static FEA, their limited accuracy constrains their applicability. Similarly, existing machine learning methods are inadequate for mapping multiple geometric parameters (GPs) to static characteristics across varied SRM designs. To overcome these limitations, this study proposes a novel integrated approach that combines K-means clustering with an artificial neural network (ANN) to enable accurate and efficient prediction of static characteristics. This technique led to a 52.16% decrease in the total computation time for determining the static characteristics of the SRM designs. Further, dynamic performance is evaluated using a MATLAB/Simulink-based SRM drive model, offering a computationally lightweight alternative to dynamic FEA. The proposed CE-MODO framework is applied to a four-phase 8/6 SRM topology designed for an electric three-wheeler, with average torque and electromagnetic losses as optimization objectives. Optimization is carried out by coupling the nondominated sorting genetic algorithm II (NSGA-II) with Kriging surrogate models, significantly reducing the computational load. The proposed methodology achieved an 11.01% improvement in average torque and a 4.56% reduction in electromagnetic losses compared to the initial design. The FEA models corresponding to both static and dynamic analyses employed in this study are further validated through experimental testing on a fabricated SRM prototype.