AI-Based Optimization Method of Motor Design Parameters for Enhanced NVH Performance in Electric Vehicles 2024-01-2927

The high-frequency whining noise produced by motors in modern electric vehicles causes a significant issue, leading to annoyance among passengers. This noise becomes even more noticeable due to the quiet nature of electric vehicles, which lack other noises to mask the high-frequency whining noise. To improve the noise caused by motors, it is essential to optimize various motor design parameters. However, this task requires expert knowledge and a considerable time investment. In this study, we explored the application of artificial intelligence to optimize the NVH performance of motors during the design phase. Firstly, we selected and modeled three benchmark motor types using Motor-CAD. Machine learning models were trained using Design of Experiment methods to simulate batch runs of Motor-CAD inputs and outputs. By applying AI, we developed a CatBoost-based regression model to estimate motor performance, including NVH and torque based on motor design parameters, achieving an impressive R-squared value of approximately 0.99. Additionally, we further analyzed key design predictors through SHAP (SHapley Additive exPlanations). Subsequently, we investigated various optimization algorithms, including Particle Swarm Optimization, Genetic Algorithm, and Reinforcement Learning, to determine the optimal adjustments of motor design parameters for improved NVH performance. Throughout this process, we achieved improvements in NVH performance while applying constraints to maintain torque levels. Finally, we integrated the AI model and optimization algorithms into a user interface dashboard, enabling motor design engineers to efficiently predict motor NVH performance by selecting input parameters, applying attribute balance constraints, and executing optimizations.