This paper presents soft computing-based optimization techniques for the cogging torque reduction and thermal characterization by finite element analysis in a permanent magnet brushless DC motor (BLDC). Stator and rotor structure of BLDC motor are optimized to reduce the cogging torque, noise, and vibration by using the design parameters namely: length of magnet, length of air gap and opening in the stator slot which are selected by performing variance-based sensitivity analysis. The proposed method is suitable in the preliminary design phase of the motor to determine the optimal structure to improve the efficiency. The comparison of results obtained using firefly algorithm , ant colony optimization algorithm and Bat algorithm indicate that Firefly-based optimization algorithm is capable of giving improved design parameter output. Cogging torque is created due to the interaction of magnets in the rotor and the stator slot of the motor. Thorough thermal analysis is also conceded out to investigate the thermal characteristics at dissimilar portions of the motor namely: stator core, stator slot, rotor core and permanent magnet at different operating environments in the continuous operation mode. Thermal investigation is required for the various high speed e-vehicle applications. The usefulness of the designed machine simulation is compared with the results obtained from hardware analysis. The outcomes attained from software simulation studies are validated through experimental hardware setup.

本文介绍了基于软计算的优化技术，用于通过永磁无刷直流电机 (BLDC) 的有限元分析来降低齿槽转矩和热特性。通过基于方差的灵敏度分析选择磁体长度、气隙长度和定子槽开口等设计参数，优化 BLDC 电机的定子和转子结构，以降低齿槽转矩、噪声和振动。该方法适用于电机的初步设计阶段确定最优结构以提高效率。使用萤火虫算法、蚁群优化算法和蝙蝠算法获得的结果的比较表明，基于萤火虫的优化算法能够提供改进的设计参数输出。齿槽转矩是由于电机转子和定子槽中的磁铁的相互作用而产生的。还进行了彻底的热分析，以研究电机不同部分（即：定子铁芯、定子槽、转子铁芯和永磁体）在连续运行模式下不同运行环境下的热特性。各种高速电动汽车应用都需要进行热研究。将设计的机器仿真的有用性与硬件分析获得的结果进行比较。软件模拟研究获得的结果通过实验硬件设置进行验证。