Development and Optimization of Vehicle Systems for Improved Road Noise and Prediction Using Vehicle System Model in an Autonomous Vehicle Application

As autonomous vehicles with electric propulsion technology are introduced to the market, the customer expectations may change significantly compared to the conventional ownership models. It is expected that the customers' focus while commuting will shift from driver related to non-driver related tasks and, as a result, more isolation from the environment may be required. Among the many challenges in noise, vibration and harshness (NVH) for an autonomous-electric vehicle, the expectation is that road noise and component noise will require the most amount of development and refinement efforts. As such, a major focus on structure-borne road noise must be considered early in the vehicle development phase to avoid either costly changes late in the program or dissatisfied customers in the market. In order to minimize the amount of structure-borne noise transmission through the body and chassis systems, a vehicle system model for virtual road noise development has been developed at Zoox with close collaboration across vehicle integration, computer-aided engineering (CAE), and tire supplier teams. Vehicle system models for road noise development are generally limited by the tire models used, which are often too simplified and limiting with respect to virtual efforts for system optimization. This paper will review the application of spindle forces and their estimation, an advanced tire-wheel model in a vehicle system model, the proceeding vehicle system analysis, and synthesis of road noise. The tire-wheel model includes the structural dynamic properties of both the tire and wheel with the centrifugal, gyroscopic, fluid-structure coupling and preload effects while the vehicle system model includes vibro-acoustics properties of chassis, powertrain, body and interior systems. A review of other work in the area of road noise excitation methods and application in a vehicle system model will be given, a description of the advanced tire-wheel system model and its application will be introduced, and system optimization will be highlighted. The inclusion of the advanced tire-wheel system model and close collaboration across teams enabled a high level of correlation with experimental tests from 20-400 Hz.