Without the masking effect of a combustion engine, noise from the road is much more prominent in electric vehicles (EV) and has become the dominant source of noise for drivers and passengers.
Road noise however is a complex problem. Unlike engine noise, which comes from a single, well-defined source, road noise finds its origins in the road-to-tire contact. This means that there are typically 4 sources (assuming a 4-wheel vehicle) which are influenced by the roughness and profile of the road as well as the compliance of the tires. From an engineering point of view it’s easy to appreciate the added complexity compared to engine noise.
In addition to the engineering complexity, there is also a supplier-OEM relationship that comes into play. Most OEMs do not manufacture their own tires and may even have multiple tire suppliers for the same vehicle. This brings on another set of complications. Firstly, there are multiple types of tires for the same vehicle, each combination having its own noise characteristics. And secondly, there is a need to properly agree with the tire suppliers on what the target performances of the tire need to be and how to determine them.
The proposed solution to these problems is to use defined vehicle architectures and populate them with sub-system and component models based on test and/or simulation data. The sub-systems are both passive systems such as trimmed bodies and sub-frames, but also ‘active’ components that introduce loads into the vehicle. In this case where we consider road noise, these active components are the wheels and the road loads coming through them. The important aspect of these ‘active’ components is that they are defined independent from the actual vehicle application, i.e. they are universally applicable to any vehicle model.
This approach, called Virtual Prototype Assembly, allows for effective prediction of road noise throughout the vehicle development without needing a physical vehicle or prototype and hence can be applied from very early on in the process.
In this paper, this methodology is applied to predict the interior noise in a simulated vehicle, starting from road loads determined from testing on an existing vehicle, transforming them into an invariant load definition and finally applying them onto a simulated vehicle.