Optimized Lightweight Frame for Intelligent New-energy Vehicles

Peipei Wu
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Abstract

In this paper, a joint optimization method based on multi-objective response surface approximation model and finite element simulation program is proposed to realize the lightweight optimization of new-energy vehicle frames. Under the premise of satisfying the constraints of strength, frequency and vibration, the thickness of different important parts is optimized to achieve the goal of minimizing the quality of intelligent vehicles. In order to obtain the stress distribution of each part and the vibration frequency of the frame, various finite element analyses of the intelligent vehicle frame are analyzed. In order to achieve optimization, this paper adopts the response surface method for multi-objective optimization. Sample data was generated by the central composite design, and the response surface optimization method was used to filter out 5 design variables that had a large impact on the frame. As a result, the weight of the frame was reduced from 25.05 kg to 19.86 kg, a weight reduction of 20.7%, achieving a significant weight reduction effect. This method provides important reference value and guiding significance for the optimization of frame and its lightweight. In this way, the design of the frame can be better optimized to make it lighter, thereby improving the performance of the smart car. At the same time, this method can also be applied to optimization problems in other fields to achieve more efficient and accurate optimization goals.
智能新能源汽车轻量化车架优化
提出了一种基于多目标响应面近似模型和有限元仿真程序的联合优化方法,实现了新能源汽车车架轻量化优化。在满足强度、频率和振动约束的前提下,对不同重要部件的厚度进行优化,以实现智能汽车质量最小化的目标。为了得到车架各部分的应力分布和振动频率,对智能车架进行了各种有限元分析。为了实现优化,本文采用响应面法进行多目标优化。通过中心复合设计生成样本数据,利用响应面优化方法筛选出对车架影响较大的5个设计变量。由此,车架重量从25.05 kg减少到19.86 kg,减重20.7%,达到了显著的减重效果。该方法对车架的优化和轻量化具有重要的参考价值和指导意义。这样,可以更好地优化车架的设计,使其更轻,从而提高智能汽车的性能。同时,该方法也可以应用于其他领域的优化问题,以达到更高效、准确的优化目标。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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