Legislative requirements for emissions and fuel consumption reduction are driving OEMs to develop innovative powertrain and vehicle technologies. In addition to continued development of new technologies with conventional internal combustion engines (ICE), there is an increasing trend toward electrification. These trends make it essential to develop relevant means of assessing the NVH performance of electric drive tunits (EDUs). These components do not generate the amplitudes of noise and vibration observed from internal combustion engines (ICE). As such, the methods used for NVH assessment and target development of IC engines are not sufficient for electric machines: While the objectives of ICE-based NVH development are reduction and refinement of source excitations, EDU-based NVH development focuses on the elimination of potential objectionable noise behavior in the context of ever-changing or missing masking noise content. For example, there is a reduced background noise for masking tonal noise in the absence of a running internal combustion engine.
The expectation for interior noise content from ICE-based vehicles (i.e., “powertrain presence”) depends highly on the vehicle class and target demographic; while luxury cars target low interior noise content, performance vehicles demand some level of powertrain noise feedback (with an emphasis on development of the desired “brand character”). Conversely, the tonal noise typically associated with EDUs is universally considered annoying; hence the goal is to minimize perception of this content in the vehicle. This becomes challenging, given that the reduced overall noise content available to hide (mask) this tonal noise content is lower on electric vehicles than ICE-based vehicles. Figure 1 below illustrates the difference in typical noise levels observed in ICE-powered vs. electric vehicles (EV) in the form of FEV scatterbands. Clearly, significant reduction in overall noise levels on EV are evident, especially at low-to-mid vehicle speeds.
To predict the perceptibility of tonal noise content in-vehicle, masking band analysis can be used. As shown in the figure below, the order content can be compared to surrounding 3rd octave levels to determine how much noise is available in adjacent frequencies to mask the tonal noise. If the order level (of whine noise) is high relative to the corresponding 3rd octave band noise levels, this is an indication that there is insufficient background noise to mask the order, resulting in a perceptible, and hence, objectionable whine noise. Also shown below is a masking surface which illustrates the masking content for various orders over the operating range of an example vehicle. At higher vehicle speeds, wind noise is more prominent; this results in more masking content and an associated reduction in perception of whine noise.
NVH issue root-cause analysis & mitigation
Increased trends in electrification and associated technologies have posed new challenges in NVH development. In addition to minimizing tonal noise content in the vehicle’s interior, there are multiple potential NVH issues related to transient instabilities (e.g., gear rattle or other driveline issues). FEV utilizes a structured approach, with extensive experience in 8D analysis and Design-of-Experiments (DoE) to address such problems. As part of this root-cause analysis, FEV utilizes a combination of industry-standard methods (e.g., Ishikawa diagrams), as well as FEV developed tools and processes. FEV’s Vehicle Interior Noise Simulation (VINS) is an example of a unique methodology that can be effectively utilized in the support of root cause analysis with complex noise issues. The VINS process is a unique time-domain transfer path analysis which provides insights into noise sources and transfer paths which contribute to sound quality issues under steady-state or transient conditions. Any noise issues identified at the vehicle’s interior can be broken down to identify contributions of various structureborne and airborne noise paths. The critical noise paths can be further decomposed to identify any potential opportunities for improvement (mount isolation, attachment point stiffness, vibroacoustic sensitivity, acoustic attenuation, etc.). Because the results generated are in the time-domain, advanced analysis methods or subjective evaluations (listening studies) can be used for assessment of the overall simulated noise or individual path contributions. Figure 2 schematically shows the integration of the VINS methodology in a structured 8D root-cause analysis process.
Component-level EDU NVH assessments
FEV has established standard testing procedures for quantifying radiated noise, sound power, and vibration at the component-level to facilitate assessment of source-level inputs to support electric vehicle NVH development. Analogous to ICE-based powertrain NVH testing, overall EDU radiated noise levels are typically assessed based on average radiated noise, measured at a distance of 1 m from the EDU (e.g., using SAE J1074 standard). Additionally, it is common practice for electric machines and EDUs to augment these assessments with measurement of sound power, utilizing a hemispherical or parallelepiped array (e.g., IS0 3744 or 3745 standards). Structureborne excitations can be assessed by measurement of vibration at the EDU mounting locations (i.e., interface points between the EDU and vehicle).
Comparison of average overall sound pressure levels between ICE-based powertrains and EDUs in the figure below illustrates that noise levels radiated from EDUs are significantly lower than those observed from ICE powertrains. As such, assessment of individual orders excited by the electric machines and/or gear meshing frequencies is more relevant (than overall noise levels) for quantification of EDU NVH performance. An example of order content relative to overall radiated noise levels is illustrated below. This comparison provides information regarding the contribution of orders to overall noise levels. Additional investigation of the frequency content of the component noise levels can provide insights into perceptibility of this noise in a test cell environment. However, component level data analysis alone does little to predict the perceptibility of these orders by the customer in-vehicle. For this, a vehicle-centric data analysis approach is required, as described below.
Vehicle-centric EDU NVH target development
Derived from the VINS methodology, FEV has developed an additional process for interior noise prediction, called dBVINS. Unlike VINS (which utilizes vehicle-specific noise transfer functions), the dBVINS process predicts interior noise based on a combination of source data (noise and vibration, as measured in the test cell) and standardized vehicle noise transfer functions. These “standardized” noise transfer functions are based on median vehicle noise sensitivity performance, derived from the extensive database of vehicles assessed by FEV. By standardizing the transfer functions, the interior noise relevant NVH performance of a given component (e.g., EDU) can be judged based on component-level tests from a NVH test bench. This allows for direct comparison of the expected interior noise performance of different EDUs or design variants of a development EDU. Specific to EDU development, this process allows for prediction of relevant order content at the vehicle interior. Comparison of this order content to the masking noise levels discussed above provides insights into the potential perceptibility of tonal noise issues by the customer. Appropriate design changes using a combination of CAE (e.g., MBS/FEA) and test-based approaches (e.g., calibration changes, NVH countermeasure development) can be employed to improve the component-level NVH performance of the EDU, utilizing such a vehicle-centric approach.