基于改进遗传算法的汽车永磁起动机优化设计

针对电机的优化设计是一个复杂的、有约束、多变量优化问题，为了提高优化效率和收敛速度，使电机成本降低，结构更紧凑，采用了改进的遗传算法，将永磁起动机的原始方案直接加在初始种群中，并对交叉概率和变异概率采用了随着适应值变化进行自适应调整的方法．在适应值的计算过程中，为了提高计算准确度，2D有限元模型被用来计算永磁起动机的参数和性能．根据优化结果研制了新的样机，实验结果表明：通过优化，在满足各项性能要求和约束的前提下，降低了电机的成本．

Optimization design of automobile permanent magnet starting motor based on improved genetic algorithm

Genetic algorithm (GA) is employed to optimize permanent magnet DC motor used as automobile starting motor. The aim is to make the motor more compact and the cost much lower. Because the optimization of motor is a very complex, restricted, multivariable problem, improved genetic algorithm is adopted in order to enhance optimization efficiency and convergence rate. The original parameters of the optimized motor is directly put into initial population, adaptive crossover and mutation is introduced. In the calculation process of fitness value, in order to obtain the needed accuracy, 2D finite element model is used to calculate the motor parameters and performances. According to the optimization results, a prototype motor is produced, experimental results show that much lower cost of the motor is obtained, GA is a very efficient technique for the motor optimization design.