基于RBF-CMGA的电机低温运行特性优化

Optimizing motor cool-operating performance based on RBF-CMGA

为了探讨电机的低温运行特性,依据径向基函数网络(RBFNN)和压缩映射遗传算法(CM-GA)融合理论,对电机低温运行特性(起动功率、起动转矩、转速)进行了优化分析,预测了低温起动电机的结构参数;对比分析了电机在0℃、-5℃、-15℃和-25℃下的运行特性;在-25℃下,进行了电机起动电压、起动电流、起动转矩及起动转速等性能试验。结果表明:优化后的电机最大起动功率为2.89 kW,平均起动转矩为42 N.m,最大转速为2 475 r/m in,保证了发动机快速、可靠地低温起动。证实了利用RBF-CMGA融合算法对电机结构参数和运行特性进行优化,是完全可行的。

In order to discuss the motor cool-operating performance, based on the synergetic theory of the radial basal function neural network （RBFNN） and the contractive mapping genetic arithmetic （CMGA）, the motor cool - operating performance, such as, electromagnetic power, electromagnetic torque and start rotational speed, were optimized by RBF-CMGA and the motor structural parameters were optimally forecasted. The motor cool-operating performances were contrastively analyzed at 0℃, - 5℃, - 15℃ and -25℃ by RBF-CMGA. Several performance tests about starting voltage, starting electric current, starting torque and starting rotate speed of the motor were made out at 25℃ on the engine cool-starting system. The test results indicated that the maximal eleetromagnetic power was 2.89 kW, the average electro- magnetic torque was 42 N·m, the maximal starting rotate speed was 2 475 r/min, which completely satisfy the engine cool-starting performance requirements. It is feasible that the motor cool-starting performance and structural parameters are optimized by RBF-CMGA.