Correlation of NVH Model for Extended Range Generator in Electric Vehicle

As electrification becomes more common in the automotive industry, the need to accurately predict their behavior in the prototyping phase has become a critical topic for manufacturers. Electric motors are naturally much quieter than combustion engines, and as a result there is an increased emphasis on the reduction of noise, vibration, and harshness (NVH). This emphasis carries over into a need for accurate, quantitative prediction of NVH phenomena. This need applies not only when comparing performance between variants, but for confidently driving design decisions to meet customer satisfaction. Furthermore, accurate prediction is critical because NVH performance must be balanced with other areas such as durability and eMotor performance. If the NVH response is not accurate, one cannot take the appropriate countermeasures. In some cases this can lead to a noisy eMotor. In other cases, a poorly performing, very quiet eMotor This paper presents a comprehensive approach to addressing this need for accurate, quantitative prediction of NVH phenomena by focusing on three key areas: validation of electromagnetic excitations (eMag forces), validation of struc-tural modeling, and correlation to testing data. Validation of eMag forces includes an emphasis on temperature de-pendent NVH response due to variation in magnet strength. Validation of structural modeling includes experimental modal analysis where critical assumptions in the modeling are tested and modified to match test data. Finally, an overall correlation is performed with respect to both structure-borne and airborne responses to validate the model and pre-pare it to influence subsequent iterations. The contributions of this paper include developing standards for early design phase modeling with best assumed values, force mapping techniques, and collaborating closely with testing teams to refine simulation correlations. These stand-ards aim to improve the accuracy of NVH predictions and facilitate better decision-making in the design process.