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The Environmental Protection Agency (EPA) requirement for 54.5mpg by 2025 to reduce greenhouse gases has pushed the industry to look for alternative fuels to run vehicles. Electricity is of those green energies that can help auto industry to achieve those strict requirements. However, the electric or hybrid-electric vehicles brought new challenges into science and engineering world including the Noise and Vibration issues which are usually tied up with both airborne and structural noises. The electromagnetic force plays a significant role in acoustic noise radiation in the electric motor which is an air-gap radial Maxwell force. This paper describes an innovative approach to model the physics of noise radiated by the electric motor.

Hypoid gears transmission error (TE) is a metric that is usually used to evaluate their NVH performance in component level. The test is usually done at nominal position as well as out of positions where the pinion and gear are moved along their own axis and also along offset direction to evaluate sensitivity of the measured TE to positional errors. Such practice is crucial in practical applications where the gear sets are inevitably exposed to off position conditions due to a) housing machining and building errors, b) deflections of housing, bearings, etc. under load and c) thermal expansions or contractions of housing due to ambient temperature variations. From initial design to development stage, efforts should be made to design the gear sets to be robust enough to all combinations of misalignments emanated from all three mentioned categories.

This paper discusses approaches to properly design aluminum axles for optimized NVH characteristics. By effectively using well established and validated FEA and other CAE tools, key factors that are particularly associated with aluminum axles are analyzed and discussed. These key factors include carrier geometry optimization, bearing optimization, gear design and development, and driveline system dynamics design and integration. Examples are provided to illustrate the level of contribution from each main factor as well as their design space and limitations. Results show that an aluminum axle can be properly engineered to achieve robust NVH performances in terms of operating temperature and axle loads.

Alternative powertrains, in particular electric and plug-in hybrids, create a wide range of unique and challenging NVH (noise, vibration & harshness) issues in today's automotive industry. Among the emerging engineering challenges from these powertrains, their acoustic performances become more complicated, partially due to reduced ambient masking noise level and light weight structure. In addition, the move away from conventional displacement engines to electrical drive units (EDU) has created a new array of NVH concerns and dynamics, which are relatively unknown as compared to the aforementioned traditional setups. In this paper, an NVH optimization study will be presented, focusing on four distinct factors in electric drive unit gear mesh source generation and radiation: EDU housing and bearing dynamics, gear geometry, EDU shafting torsional dynamics, and EDU housing structure. The study involves intensive FEA modeling/analyses jointly with physical validation tests.

An application of variation simulation for predicting vehicle interior driveline vibration is presented. The model, based on a “Monte Carlo”-style approach, predicts the noise, vibration and harshness (NVH) response of the vehicle driveline based on distributions of imbalance and runout derived from manufacturing production variation (the forcing function) and the vehicle's sensitivity to the forcing function. The model is used to illustrate the change in vehicle interior vibration that results when changes are made to production variation for runout and imbalance of driveline components, and how those same changes result in different responses based on vehicle sensitivity.

Today's emerging 4-wheel-drive and all-wheel-drive vehicle architectures have presented new challenges to engineers in achieving low driveline system noise. In the meantime there's also a constant pressure from increasingly stringent noise level requirements. A driveline system's NVH (noise, vibration and harshness) performance is controlled by various noise sources and mechanisms. The common noise issues include the axle gear whine, driveline imbalance/run-out, 2nd order kinematics, engine torque fluctuation, engine idle shake etc. Unfortunately various design alternatives may improve some NVH performance attributes while degrading others. It is important to balance the requirements for these noise sources to achieve an optimized driveline system NVH. However, very little literature is found on this topic. In this paper, discussions on methodologies in balancing these different driveline NVH requirements are presented.

This paper presents a study of axle system NVH (noise, vibration and harshness) performance using DFSS (Design for Six Sigma) methods with the focus on the system robustness to typical product variations (tolerances / manufacturing based). Instead of using finite element as the simulation tool, a lumped parameter system dynamics model developed in Matlab/Simulink is used in the study, which provides an efficient way in conducting large size analytical DOE (Design of Experiment) and stochastic studies. The model's capability to predict both nominal and variance performance is validated with vehicle test data using statistical hypothesis test methods. Major driveline system variables that contribute to axle gear noise are identified and their variation distributions in production are obtained through sampling techniques.

Ideally, the calculation of driveline component imbalance sensitivity is a straightforward operation of normalizing the changes in dynamic responses that occur when a known imbalance is added to a rotating component. In practice, however, overlapping driveline component orders (and wheel order harmonics) often prohibit the measurement repeatability required to distinguish these changes. A solution to the measurement repeatability issue is presented for chassis dynamometer testing, based on prescribing minor adjustments to the roll speeds for different wheels in order to separate the orders of various rotating components.

Driveline components are designed to meet customer component-level NVH requirements as measured on a dynamometer in a laboratory environment. It is desired to evaluate the NVH characteristics of driveline components at the end of the manufacturing process and predict how this performance will compare to the component-level specification as measured on the dynamometer in the laboratory environment. A test method is presented for establishing the correlation of the NVH performance of light duty truck axles measured at the end of the manufacturing process on the plant floor to the NVH performance of the same axles measured on a dynamometer in a laboratory environment.

The role of NVH testing has evolved from a firefighting role and a period of exploration to a well defined standard test role in the product development and validation process. Integral to this process is robust engineering, which drives the need to execute many tests quickly, efficiently and accurately. This allows the NVH specialist to concentrate on interpretation of results and spend less time on the acquisition of data. As the volume of data grows, this creates the opportunity to data mine an NVH database to compare results from large sample sizes and focus on product variation. Today's NVH laboratory is accountable for producing high quality, consistent, timely, and cost effective test reports. The basic core of the test has to be easy to set up and execute for a novice, yet still allow for exploratory tests by specialists as necessary. The NVH laboratory is now subject to the same budgetary pressures and quality audits as other testing operations.

Axle gear whine originates at the hypoid gearset, and can be amplified by the driveline system dynamics as well as the transfer paths into the vehicle. In addition to tremendous efforts in improving the gear quality, it is of some importance that optimized system dynamics be achieved so that the system sensitivity to the gear excitation can be greatly reduced. This methodology has been extensively utilized in American Axle through a combined numerical simulation (FEA) and testing approach. This paper presents the FEA modeling techniques in studying axle system dynamics and the level of accuracy of the model that can be achieved in predicting forced system responses. The use of a driveline system model in the development of a design optimization for total system NVH performance is discussed. Examples are provided to demonstrate the effects of modal alignments and appropriate system tuning.