量子遗传算法在永磁同步轮毂电机优化设计中的应用

量子遗传算法具有种群规模小而不影响算法性能、收敛速度快和全局搜索能力强等优点。为了获得高功率密度和低成本的电动车用轮毂电机,基于量子遗传算法,针对研究设计的一种外转子永磁同步轮毂电机,以电机有效质量、材料成本和功率损耗为优化目标,建立了包含8个设计变量和5个约束的数学模型,对电机进行优化设计。研究结果表明:永磁同步轮毂电机有效质量、材料成本和功率损耗降低,效率特性提升,有限元分析结果与量子遗传算法计算结果接近,能满足电动车对驱动轮毂电机的使用要求,因此,量子遗传算法对于轮毂电机优化设计是有效可行的。

Application of quantum genetic algorithm to the optimum design of permanent magnet synchronous in-wheel motor

Quantum genetic algorithm (QGA) has advantages of small population size with good algorithm performance, fast convergent rate and powerful ability of global search. In order to acquire high power density and low cost in-wheel motor of electric vehicle,based on the quantum genetic algorithm, a designed outer-rotor permanent magnet synchronous in-wheel motor model with 8 designed variables and 5 constraints was built to optimize the effective quality, material cost and power consumption. The results show that the effective quality, material cost and power consumption of the motor are decreased and the efficiency of the motor is improved. The results of finite element analysis are close to those calculated by quantum genetic algorithm,which can satisfy the using requirements of driving in-wheel motor electric vehicle. Therefore, the QGA is an effective and feasible algorithm in optimization design of in-wheel motor.