Noise reduction of axial‐flux motors by combining various pole‐arc coefficients and circumferential shifting of permanent magnets: analytical approach

Abstract

The electromagnetic noise of an axial-flux in-wheel motor (AFWM) is reduced by combining various pole-arc coefficients and circumferential shifting of permanent magnets (PMs) here. First, the analytical model of the air-gap magnetic field for the AFWM with various pole-arc coefficients and circumferential shifting of PMs is established. The influence of edging effect and stator slotting is taken into account through the radial correction function and complex relative permeance function, respectively. Subsequently, the torque and electromagnetic noise of the AFWM are derived analytically. A multi-objective optimisation of the AFWM is then implemented by the NSGA-II (non-dominated sorting genetic algorithm) based on the proposed analytical approach. Finally, the electromagnetic performance and vibroacoustic characteristics of the initial and optimised motors are compared and analysed. The results show that the proposed method by combining various pole-arc coefficients and circumferential shifting of PMs can reduce the noise of axial-flux motors effectively, without sacrificing the output torque. This study is of great significance for the vibration and noise reduction of axial-flux motors.

A Review of Axial-Flux Permanent-Magnet Motors: Topological Structures, Design, Optimization and Control Techniques

“…The optimization design combination was verified in the 3D-FEA model, and the results showed that the efficiency of AFPM motor was up to 91.5 %. Additionally, some studies used hybrid algorithms with GA to optimize the design of AFPM motors, such as a hybrid genetic algorithm (HGA) combining simulated annealing and father-offspring selection [77], the elitist genetic algorithm (EGA) [87], the non-dominated sorting genetic algorithm (NSGA-II) [88], etc.…”

“…The optimization design combination was verified in the 3D-FEA model, and the results showed that the efficiency of AFPM motor was up to 91.5%. Additionally, some studies used hybrid algorithms with GA to optimize the design of AFPM motors, such as a hybrid genetic algorithm (HGA) combining simulated annealing and father-offspring selection [77], the elitist genetic algorithm (EGA) [87], the non-dominated sorting genetic algorithm (NSGA-II) [88], etc. With the further development of efficient computer system and optimization theory, new multi-objective optimization algorithms such as particle swarm optimization (PSO) [89,90], bat optimization (BO) [91] and the Taguchi algorithm [92] have also been applied to the optimization design of AFPM motors.…”

Vibration reduction of switched reluctance motor under static eccentricity with optimised current harmonic

“…6. A multiphysics modal is established according to [27]. Combining the electromagnetic force of the stator teeth calculated by JMAG with the modal results of the SRM enclosure obtained by ANSYS, the enclosure vibration response is calculated in LMS by the modal superposition method.…”

An integrated approach of a field‐circuit coupling model and multi‐physics finite‐element simulation for analysing transient electromagnetic vibration of pump motors

“…Moreover, the analytical model facilitates the analysis of the magnetic field under fault operating conditions, for example, misalignment and eccentricity [19,20]. It is also timesaving and efficient for the analytical model to calculate the EM forces over a wide operation range at design stage of a low vibration motor [21]. As for the structural dynamic response solution, the 3D entire structural FE model is commonly adopted to compute the vibration; because the motor structure can be roughly regarded as a cylinder, it has both circumferential and axial modes [22].…”