Customized Optimization for Vehicle Acoustic Statistical Energy Analysis  

整车声学统计能量分析定制优化

作　　者：Huang Yi Feng Qiuhan Liu Jingqi Li Xueliang Liu Lin Yang Shaobo 黄义;冯秋翰;刘经奇;李学亮;刘林;杨少波(重庆长安汽车股份有限公司,重庆400023;汽车智能仿真重庆重点实验室,重庆400000)

机构地区：[1]Chongqing Changan Automobile Co.,Ltd,Chongqing 400023 [2]Chongqing Key Laboratory of Vehicle Intelligent Simulation,Chongqing 400000

出　　处：《汽车文摘》2024年第11期1-10,共10页Automotive Digest

基　　金：重庆市技术创新与应用发展专项(CSTB2023TIAD-STX0039)。

摘　　要：Statistical Energy Analysis(SEA) is one of the conventional tools for predicting vehicle high-frequency acoustic responses.This study proposes a new method that can provide customized optimization solutions to meet NVH targets based on the specific needs of different project teams during the initial project stages.This approach innovatively integrates dynamic optimization,Radial Basis Function(RBF),and Fuzzy Design Variables Genetic Algorithm(FDVGA) into the optimization process of Statistical Energy Analysis(SEA),and also takes vehicle sheet metal into account in the optimization of sound packages.In the implementation process,a correlation model is established through Python scripts to link material density with acoustic parameters,weight,and cost.By combining Optimus and VaOne software,an optimization design workflow is constructed and the optimization design process is successfully executed.Under various constraints related to acoustic performance,weight and cost,a globally optimal design is achieved.This technology has been effectively applied in the field of Battery Electric Vehicle(BEV).统计能量分析(SEA)是预测整车高频声学响应的常规工具之一。为了在项目初期阶段依据不同项目组的具体需求,提供满足NVH目标的定制优化方案,提出了一种创新性方法,结合了动态优化、径向基函数(RBF)以及基于模糊决策变量的遗传算法(FDVGA)进行SEA优化,并将车身钣金纳入声学包优化的考虑范围。在实施过程中,利用Python脚本建立了材料密度与声学参数、质量及成本之间的关联模型。通过整合Optimus与VaOne软件,构建了优化设计工作流,并成功执行了优化设计流程,可以在不同的声学性能、质量及成本约束条件下,实现了全局最优设计。该技术已在电动汽车研发领域得到有效应用。

关 键 词：Statistical Energy Analysis(SEA) Dynamic optimization Radial Basis Function(RBF) Vehicle sheet metal Sound package Battery Electric Vehicle(BEV) 

分 类 号：U461.4[机械工程—车辆工程]