Optimal shape design of a class of permanent magnet motors in a multiple-objectives context

Abstract

Purpose This paper aims to deal with the optimal shape design of a class of permanent magnet motors by minimizing multiple objectives according to an original interpretation of Pareto optimality. The proposed method solves a many-objective problems characterized by five objective functions and five design variables with evolution strategy algorithms, classically used for single- and multi-objective (two objective functions) optimization problems. Design/methodology/approach Two approaches are proposed in the paper: the All-Objectives (AO) and the Many-Objectives (MO) optimization approach. The former is based on a single-objective optimization of a preference function, i.e. a normalized weighted sum. In contrast, in the MO a multi-objective optimization algorithm is applied to the minimization of a weight-free preference function and simultaneously to a maximization of the distance of the current solution from the prototype. The optimizations are based on an equivalent circuit model of the Permanent Magnet (PM) motor, but the results are assessed by means of finite element analyses (FEAs). Findings An extensive study of the solutions obtained by means of the different optimization approaches is provided by means of post-processing analyses. Both the approaches find non-dominated solutions with respect to the prototype that are substantially improving the initial solution. The points of strength along with the weakness points of each solution with respect to the prototype are analysed in depth. Practical implications The paper gives a good guide to the designers of electric motors, focussed on a shape design optimization. Originality/value Considering simultaneously five objective functions in an automated optimal design procedure is challenging. The proposed approach, based on a well-known and established optimization algorithm, but exploiting a new concept of degree of conflict, can lead to new results in the field of automated optimal design in a many-objective context.

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“…Various bioinspired algorithms, including swarm intelligence and evolutionary algorithms, have been used in various optimization problems involving engineering problems (Houssein et al , 2021; Premkumar et al , 2021) and electromagnetic devices (Ayala et al , 2016; Di Barba, 2010; Di Barba et al , 2018, 2022; Kumar et al , 2022).…”

Topology optimization and parameter optimization hybridized by mesh smoothing for IPMSM design

“…Interior permanent magnet (PM) synchronous machines (IPMSMs) play important roles in many industrial applications and household appliances because of their excellent torque density, high efficiency and high power factor (Li et al , 2018; Di Barba et al , 2022). The rotor design of an IPMSM should be careful work as the performance of the IPMSM can be significantly altered by modifying the location and shape of the PM and the air barriers (Di Barba et al , 2022).…”

“…Interior permanent magnet (PM) synchronous machines (IPMSMs) play important roles in many industrial applications and household appliances because of their excellent torque density, high efficiency and high power factor (Li et al , 2018; Di Barba et al , 2022). The rotor design of an IPMSM should be careful work as the performance of the IPMSM can be significantly altered by modifying the location and shape of the PM and the air barriers (Di Barba et al , 2022). Usually, in the design flow of IPMSM, first, experienced engineers build the finite element model of the initial IPMSM based on the desired specifications, then, finite element models of the various feasible designs with modified geometry parameters (such as layout parameters of magnet and barrier) are created artificially or semiautomatically, and finally, the design that meets the expectations may be found based on the finite element solution results (Nagarajan et al , 2017; Yamazaki and Togashi, 2018; Zhu et al , 2021).…”