Battery Electric Vehicles (BEVs) are driving increasingly advanced noise, vibration, and harshness (NVH) performance analysis due to the expansion of the frequency range of interest. Electric components (such as the powertrain) excite high frequent, tonal noise fingerprints which can be disturbing to the driver and thus, decreases the NVH performance of the vehicle.
Rubber mounts are used to isolate these components and minimize their noise contribution. However, mounts may display their own dynamic behavior which can lead to decreasing isolation performance. For this reason, a detailed analysis of the isolation performance of the mounts, their dynamic (stiffness) behavior, must be studied into the kHz range.
At VIBES we have categorized our solutions as seen in the figure below. In this training, we will focus on mount stiffness identification, a workflow that belongs to our test-based modelling solution(s).
The workflow is typically used to identify the dynamic stiffness of (rubber) mounts into the kHz range, which is relevant to the EV market as mentioned above.
Conventionally, mounts are characterized using hydro pulse machines. However, these machines are relatively expensive and limited to measuring dynamic stiffness only in translational directions and at relatively low frequencies.
As an alternative, VIBES has developed a workflow to capture dynamic stiffness using two Dynamic Substructuring based methods. These accurate and cost-effective methods can acquire a mount’s dynamic stiffness data into the kHz range and include rotational degrees of freedom into the model. The first method is called Inverse Substructuring and the second Substructure Decoupling.
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