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Software tools for simulations of vehicle fuel economy/energy efficiency play an important role strategic decision-making in advanced powertrains. In general, there is a trade-off between the level of detail in a numerical model of a vehicle (higher detail provides better simulation accuracy), and the computational time resources to run the model. However, even with detailed models of a vehicle, there remains some uncertainty about how the vehicle performs in the real-world. Calibration of simulation models versus real-world data is a challenging task due to variations in vehicle usage by different owners. This work utilizes datasets of real-world driving in vehicles that have been equipped with OBD/GPS loggers. The loggers record at fairly high frequency the vehicle speed, road slope, cabin heating/air-conditioning loads, as well as energy/fuel consumption.

The finite element method (FEM) is used daily in the automotive industry for such purposes as reducing the time of product development and improving the design based on analysis results, followed by later validation by tests in the laboratory and on the proving ground. This paper will present some of the methodology used to develop automotive components by finite element analysis, including procedures to specialize FEM models to obtain quantitative and qualitative results for systems such as body, chassis, and suspension components, as well as validation of the models by experimental data.

A detailed study of the effects of shim stack assemblies on performance of hydraulic mono-tube vehicle shock absorbers is presented. Currently, shim stacks are modeled as blow-off valves in hydraulic models of shock absorbers. Using this simplification, important material and geometrical properties of shim stacks cannot be studied and their effects cannot be understood on overall damper performance. In this paper, shim stack deflection is investigated and a mathematical model is presented for shim stack deflection. This model is then incorporated into the mathematical model of a hydraulic damper and various properties of shim stack and their effects on damper characteristics are studied. Energy and variational methods were used to develop the mathematical model of the shim stack. The mathematical model also takes into account the sliding effects of the shims on each other when the shim stack is deflected.

Structural Health Monitoring (SHM), especially in the field of rotary machinery diagnosis, plays a crucial role in determining the defect category as well as its intensity in a machine element. This paper proposes a new framework for real-time classification of structural defects in a roller bearing test rig using time domain-based classification algorithms. Along with the bearing defects, the effect of eccentric shaft loading has also been analyzed. The entire system comprises of three modules: sensor module – using accelerometers for data collection, data processing module – using time-domain based signal processing algorithms for feature extraction, and classification module – comprising of deep learning algorithms for classifying between different structural defects occurring within the inner and outer race of the bearing.

The advent of low emission regulations on fuel systems has made conventional sealing materials such as acry-lonitrile butadiene rubber (NBR) unfit for sealing most fuel systems. Therefore, it is imperative to look beyond conventional rubbers and towards more exotic materials to seal such applications. In this study, the permeation characteristics and the change in physical properties of several elastomeric materials (NBR, hydrogenated NBR (HNBR), and fluorocarbon elastomers (FKM)) as well as various poly(tetrafluoroethylene) (PTFE) composites were evaluated with four different fuel mixtures. The sealing materials were tested using vaporimeter cups. The results are discussed as a function of the materials' nature, composition, and filler content.