Multi-Objective Optimization of Noise Control Treatments for Road Noise and Weight Reduction in Electrical Vehicles Using Reduced Order Models and Response Surface Methods

In modern automotive design, acoustic comfort is a crucial factor influencing passenger experience, which drives engineers to develop Noise Control Treatments (NCTs) to reduce the noise experienced by passengers. These NCTs also add weight to the car, making it an engineering challenge to balance the noise reduction and weight increase. This study investigates the application of acoustic treatments in electric cars and evaluates their effectiveness through Statistical Energy Analysis (SEA), focusing mainly on road noise. To aid on the acoustic predictions, Reduced Order Models (ROMs) and Response Surface Methods (RSMs) are used to reduce the computational cost involved in simulation. An optimization is then conducted with the main objectives of minimizing noise and weight. The results demonstrate a trade-off between road noise reduction and sound package weight, and also demonstrate that ROM-based simulations can provide fast and accurate approximations of vehicle acoustics, enabling efficient design iterations and optimization of acoustic treatments.