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In engineering terms, it is always desirable to maximise the efficiency of the motor and to look the problem in economic viewpoint, minimisation of the weight and cost of the motor is required. As these two aspects are like two sides of a coin, it is usually aimed to acquire the best efficiency per weight and cost. As a result, the performance improvement of permanent‐magnet synchronous motor (PMSM) has two sides: technical and economic. This study proposes a new approach for design optimisation of an interior PMSM (IPMSM) taking into account both technical performances and economic considerations. The selected rotor configuration is a good candidate for general purpose industrial applications. A multi‐objective optimisation technique based on a new and efficient magnetic equivalent circuit of the motor is applied to obtain optimal motor parameters. A Pareto front composed of possible optimal solutions is provided. The best optimal solution is then selected using a k‐means clustering algorithm. To verify the efficacy of the design optimisation, the performance characteristics of the optimal PMSM are compared by both the finite element simulations and the experiments. In the test setup, an 8‐pole, 200‐Watt, four‐phase IPMSM with an optimised structure is prototyped where the results give agreeable accuracy. [ABSTRACT FROM AUTHOR]

This paper presents a multiobjective optimal design of the parameters of surface‐mounted permanent magnet synchronous motors (PMSMs) considering saturation of the iron core. Multiobjective parameter tuning can further improve the performance characteristics of such motors. Specifically, efficiency and total volume are of great importance in industrial applications. A new formulation is presented for these two objective functions. In this paper, a multiobjective optimization based on a modified nondominated sorting genetic algorithm II (NSGA‐II) is utilized for optimal design of PMSM. Pareto‐optimal solution is achieved, and optimal variables are obtained. The best solutions are then selected by k‐means clustering algorithm for the motor under study. Finally, finite element analysis (FEA) is performed to validate the optimization procedure. The FEA results agree well with the values obtained by the optimization process. [ABSTRACT FROM AUTHOR]