Application of Hybrid Metaheuristic Technique to Study Influence of Core Material and Core Trench on Performance of Surface Inset PMSM

Lightweight electric motor topologies used in electric transport applications require high-power density structures. Selection of suitable core material with appropriate physical geometry is important at design stage to reduce the core losses and the weight of the motor. This paper presents a novel hybrid metaheuristic optimization technique termed as Aquila-Grasshopper-Optimization (AGO), inspired by the hunting behavior of Aquila and swarming behavior of grasshoppers in search of the food source. This technique merges the merits of both Aquila optimizer (AO) and Grasshopper optimization algorithm (GOA) to create a balance between exploration and exploitation stages. By introducing the concept of normalization of the distances between the solutions (swarms) of the optimization problem at both narrowed and expanded exploration and exploitation stages of AO, the population is searched thoroughly to avoid the possibility of converging to any local optimum and to increase the convergence rate to reach the global optima. The performance of the proposed technique is first tested for various benchmark functions and thereafter implemented to the real-world problem to optimize the motor geometry of Surface Inset Permanent Magnet Synchronous Motor (SI-PMSM) to reduce the overall core losses. Two different core materials are employed in the core of the motor to study the influence of core material on the performance of the motor. In addition, the study is expanded by providing suitable trench cuts in motor core so that weight of the motor can be reduced substantially for lightweight motor applications. It is established that the variation in properties of core material can increase the power density and AGO technique can minimize core losses to obtain optimized motor core geometry thereby improving the overall electromagnetic performance of the motor.