微特电机 ›› 2021, Vol. 49 ›› Issue (9): 16-20.

• 设计分析 • 上一篇    下一篇

基于响应面算法的车载电机成本优化设计

靳冶1, 皇甫倩2   

  1. 1.上海大学 机电工程与自动化学院,上海 200072;
    2.中国电子科技集团公司第三十二研究所,上海 201808
  • 收稿日期:2021-03-03 出版日期:2021-09-28 发布日期:2021-09-29
  • 作者简介:靳冶(1984—),男,本科,工程经理,研究方向为电机控制。

Cost Optimization of Vehicle Driving Motor Based on Response Surface Model

JIN Ye1, HUANGFU Qian2   

  1. 1. School of Mechatronic Engineering and Automation,Shanghai University, Shanghai 200072, China;
    2. The 32nd Research Institution of China Electronics Technology Corporation,Shanghai 201808, China
  • Received:2021-03-03 Online:2021-09-28 Published:2021-09-29

摘要: 针对A00级微型电动车驱动电机,如何在满足应用需求的同时尽量降低成本的问题进行了研究。选用定转子内外径之比、永磁体槽中心线底部距转子内圆距离、磁钢厚度和磁钢长度4个决定永磁体排布的转子结构变量作为优化参数和自变量,建立这4个变量和材料成本之间的响应面模型。再用遗传算法对成本作优化。通过有限元仿真验证降低电机材料成本的同时,电机优化前后的扭矩、效率等性能基本不变,说明了该优化算法的有效性。

关键词: 车载电机, 降成本, 响应面模型, 遗传算法, 磁钢形状优化

Abstract: In this paper, how to meet the application requirements and reduce the cost of A00 micro electric vehicle drive motor was studied. Four structural variables of the rotor which determine the shape of the permanent magnet, i.e. the ratio of the inner and outer diameters of the stator and rotor, the distance between the bottom of the center line of the permanent magnet slot and the inner circle of the rotor, the thickness and length of the magnetic steel, were selected as the optimization parameters and independent variables to establish the response surface model between the four variables and the material cost. Genetic algorithm was used to optimize the cost. Through the finite element simulation, the torque and efficiency of the motor before and after optimization were basically unchanged, which showed the effectiveness of the optimization algorithm.

Key words: vehicle motor, cost reduction, response surface model (RSM), genetic algorithm, shape optimization of magnet steel

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