A framework for statistical design of a brushless DC motor considering efficiency maximisation

Abstract

In this study, a framework for statistical design of a brushless DC (BLDC) motor is proposed in order to maximise yield while keeping efficiency as high as possible and the non-linear constraints, that is, limitations of flux density, current density and physical dimensions, satisfied. The proposed yield maximisation method has two major steps: polyhedral approximation of the constraint region and yield maximisation. By implementing the proposed method, the optimum design parameters, that is, embrace, thickness and length of magnet, are obtained with maximum immunity to variations in magnet dimensions. Also, highly accurate artificial neural network-based models are used to estimate the objective function and constraints. It is also shown that by adjusting performance constraints (efficiency and flux density), different design goals can be achieved which help to choose the appropriate design according to the designer's goals.