基于响应面法和粒子群算法的电机轴多目标优化

Multi-objective optimization of motor shaft based on response-surface method and particle-swarm algorithm

为满足新能源汽车电机轴的轻量化设计需求,文中提出了一种基于响应面法和粒子群算法相结合的多目标优化方法。以电机轴的内径为设计变量,在满足强度约束的条件下,将电机轴质量、多危险截面处等效应力和1阶临界转速作为设计目标构建了多目标优化模型。建立Box-Behnken试验设计得到4因素3水平的有限元分析试验方案,采用最小二乘法拟合了各子目标的高精度响应面方程,并根据各子目标的重要程度引入影响因子,建立了统一的全局优化目标函数。最后,结合惯性权重线性递减的粒子群算法对全局目标函数进行了寻优。优化结果表明:电机轴的质量减小了4.8%,1阶临界转速提升了8%,且两处危险截面的等效应力分别减小了2.9%,2.5%。

In this article,a multi-objective optimization method based on the response-surface method and the particle-swarm algorithm is proposed for the lightweight design of the new-energy vehicle’s motor shaft.With the motor shaft’s inner diameter as the design variable and the strength constraints taken into account,the multi-objective optimization model is set up by taking the motor shaft’s mass,the equivalent stress at multiple dangerous sections and the first-order critical speed as the design objectives.The Box–Behnken test is conducted to obtain the finite-element analytical scheme of 4 factors and 3 levels,and the least-square method is used to fit the high-precision response-surface equation of each sub-objective.Besides,according to the importance of each sub-objective,the influence factor is introduced,and the unified global optimization objective function is worked out.Finally,the global optimization objective function is optimized by means of the particle-swarm optimization algorithm with the linear-decreasing inertia weight.The optimization results show that the motor shaft’s mass decreases by 4.8%,the first-order critical speed increases by 8%,and the equivalent stress at two dangerous sections decreases by 2.9%and 2.5%respectively.