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).

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