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A sophisticated finite element analysis (FEA) method for modeling interior permanent magnet (IPM) electric motors is presented. Based on this method, a coupled structural-acoustic analysis procedure was developed to simulate the motor dyno vibroacoustic responses with improved accuracy and reliability for NVH (noise, vibration, and harshness) behavior prediction over a wide range of torques and frequencies under the operational electromagnetic forces. The proposed motor modeling and analysis method is detail-oriented with high fidelity in modeling the structure and complex material representation. To effectively deal with the motor stator core constructed with large numbers of electromagnetic laminae, the unit-cell approach was employed to derive the core material properties by homogenizing the laminated core as an equivalent orthotropic material. Meanwhile, the windings were modeled by capturing the precise geometry for accuracy improvement.

Noise and Vibration (NVH) characteristic of an electric machine (e-Machine) is the outcome of complex interaction between source level disturbances and the surrounding structure to which the e-Machine is attached. Key e-Machine metrics that objectively quantify source level disturbance include torque ripple and radial electro-magnetic forces. These disturbances can radiate directly from the e-Machine housing (air-borne component) and also can be transmitted through the structural attachments like stator bolts, stator ring, powertrain mounts etc. (structure-borne component). In the e-machine driven by PWM switching inverter, current is not perfectly sinusoidal but contain different level of harmonics. Current harmonics impact Torque ripple, which in turn would translate into undesirable noise and vibration. There is very limited literature referencing the influence of current harmonics on torque ripple and e-machine NVH.

The current of an electric machine driven by PWM switching inverter is not ideal sinusoidal, containing different levels of harmonics. The current harmonics have important impact on the electrical machine torque ripple which could translate into transmission and vehicle level Noise Vibration and Harshness (NVH). In this work, the current waveforms were measured from dyno test at prescribed torque and speed levels, and the electric machine torque ripple was computed with the measured current. This paper will focus on the investigation of the current harmonics behaviors and features at various torque and speed conditions, the impact on torque ripple, and the possible mitigation method to reduce torque ripple.

Finite Element Analysis (FEA) models are routinely being adopted as a means of up-front design for automotive body structure design. FEA models play two important functions: first as a means of assessing design versus an absolute target; secondly they are used to assess the performance of design alternatives required to meet targets. Means of assessing model capability versus task is required to feed appropriate information into the design process. Being able to document model capability improves the credibility of the FEA model information. A prior paper addressed assessing the absolute performance of model technology using a metric based on a statistical hypotheses test that determines membership in a reference set. This paper extends the use of quality technology to determining the capability of the FEA model to span the design space using Designed Experiments.

The problem of NVH CAE model correlation in light of test and product variation has been addressed. An objective metric based on statistical hypothesis testing has been proposed and evaluated. This technique has been shown to work for frequency response functions. The hypothesis test answers the question ‘Are the involved frequency response functions statistically different than those in a reference set?’ This paper demonstrates that vehicles are uniquely identifiable by their frequency response functions. Under certain restrictive assumptions, the average gross error normalized by the ensemble variance is chi-squared distributed. Using a chi-squared test, the probability that a NVH CAE prediction is a member of a reference (test) set can be estimated. Within the context of a reference (test) set, this metric represents the limit to predictability. The metric was applied to examples including two midsize car NVH CAE models.