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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]

Here, the authors present a new constrained multi‐objective optimization method for maximizing output power and efficiency of switched reluctance motor (SRM). The constraints play a significant role in this process. Artificial neural network‐based models are created to represent the SRM behaviour which is a difficult task. For this reason, a single neural network for each objective is built and the number of training data has been increased to reach a good accuracy. The authors propose a neural network‐based adjoint Lp metric technique to combine several objective functions and constraints with different weight factors to a single function which should be minimized. To apply the constraints, an adjoint term is added to the original Lp function including only objective functions. The flux densities of the magnetic materials are selected as the optimization constraints. When approaching as close as possible to the saturation level, the SRM has a better performance. Adding an adjoint term to the original Lp leads to better optimization results which were proved by comparing with a conventional optimization. Also, all obtained results were finally validated by the Ansoft Maxwell software. [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]