Wang ZeYu¹, Wang XianYun¹, Wang Zhen^1,

Electric vehicles (EVs) have emerged as a cornerstone technology in sustainable transportation, where noise, vibration, and harshness (NVH) challenges predominantly stem from high-frequency electromagnetic vibrations in motors, battery fatigue under mechanical oscillations, component interactions, and amplified tire-road contact effects. The multi-parameter coupling phenomena result in merely 40% parameter consistency, positioning this as a cutting-edge international research priority in transportation engineering. Focusing on "multi-parameter coupling mechanisms and data-driven optimization for EV NVH", this study synthesizes existing research through six critical dimensions:System linkage integration addressing powertrain-chassis-battery synergy; Dynamic variations in tire structural parameters with FEA-validated geometric deviations; Modal characteristics and high-frequency coupling incorporating flexible ring models; Vibration transfer path analysis enhanced by neural network-based uncertainty quantification; Threshold optimization of tire-road interaction effects on cabin response using psychoacoustic metrics; Innovative control strategies leveraging AI-driven multi-physics simulations. Through multidisciplinary approaches combining digital twins, operational modal analysis (OMA), and machine learning, we establish quantifiable mechanisms and engineering solutions. Persistent gaps in nonlinear dynamics under transient conditions are identified, proposing data-driven methodologies as pivotal for EV-specific NVH advancements. The research further highlights intelligent control systems and sustainable material applications, providing both theoretical foundations for comfort enhancement and practical guidelines for sustainable mobility engineering.

Electric Vehicles, NVH, Multi-Parameter Coupling, Data-Driven Optimization, Tire-Road Interaction