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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. [ABSTRACT FROM AUTHOR]

This study presents a new method for parametric modelling and optimisation of a permanent‐magnet brushless DC (BLDC) motor. We proposed an artificial neural network (ANN)‐based LP metric technique to combine and optimise different objective functions of a BLDC motor using ANN‐based models and compared with conventional optimisation methods with analytical models. To proceed with this optimisation problem, the LP function should be minimised. For applying constraints to this problem, a simple method called penalty factor is proposed, in which a penalty term was added to the LP function when the constraints are violated. We considered three goals in this optimisation: efficiency maximisation, speed maximisation and material cost minimisation. Since the load is constant torque in our case, more speed means more powerful motor, and to achieve the minimum material cost goal the volume of the magnet is set as an objective function. To find the optimum geometric parameters, we used gradient‐based method subject to non‐linear magnetic constraints. All the obtained results were validated by Ansoft Maxwell. Optimising using the proposed method including ANN‐based models does not require knowledge about complicated electric/magnetic equations. Also, ANN‐based BLDC motor model is more accurate than analytical models and faster than existing models in simulation tools. [ABSTRACT FROM AUTHOR]