Robust Design of BLDC Motor Considering Driving Cycle

With growing environmental concerns and the limitations of fuel cell resources, replacing petrol-based engines with electric machines as the traction motor for electric vehicles (EVs) is gaining attention. Among the different electric machines topology, for lower power applications, such as three-wheel motorcycle, the brushless direct current (BLDC) motors are the most favorable due to their simple structure, simple control algorithm, and so on. One of the key aspects of developing BLDC motors in EV applications is utilizing the fast and efficient optimization algorithm over driving cycles. Another aspect is considering manufacturing tolerances to eliminate the failure product rate in mass production. The combination of optimization algorithms with consideration of manufacturing tolerances leads to a complex and time-consuming optimization procedure. To overcome this problem, the robust design by utilizing the design of experiment (DOE) method can be utilized. In this study, first, a developed systematic multiphysics multilevel optimization algorithm with consideration of manufacturing tolerances and driving cycles is proposed. Then, to evaluate the accuracy of the optimization process, a prototype of the optimized motor is manufactured. The efficiency and accuracy of the optimization algorithm are compared through finite element method (FEM) and experimental results, showing the effectiveness of the proposed algorithm.