Search Results

Small crack growth from notches under variable amplitude loading requires that crack opening stress be followed on a cycle by cycle basis and taken into account in making fatigue life predictions. The use of constant amplitude fatigue life data that ignores changes in crack opening stress due to high stress overloads in variable amplitude fatigue leads to non-conservative fatigue life predictions. Similarly fatigue life predictions based on small crack growth calculations for cracks growing from flaws in notches are non-conservative when constant amplitude crack growth data are used. These non-conservative predictions have, in both cases, been shown to be due to severe reductions in fatigue crack closure arising from large (overload or underload) cycles in a typical service load history.

The analysis of nickel metal hydride (Ni-MH) battery performance is very important for automotive researchers and manufacturers. The performance of a battery can be described as a direct consequence of various chemical and physical phenomena taking place inside the container. In this paper, a physics-based model of a Ni-MH battery will be presented. To analyze its performance, the efficiency of the battery is chosen as the performance measure, which is defined as the ratio of the energy output from the battery and the energy input to the battery while charging. Parametric sensitivity analysis will be used to generate sensitivity information for the state variables of the model. The generated information will be used to showcase how sensitivity information can be used to identify unique model behavior and how it can be used to optimize the capacity of the battery. The results will be validated using a finite difference formulation.

The performance, life cycle cost, and safety of electric and hybrid electric vehicles (EVs and HEVs) depend strongly on their energy storage system. Advanced batteries such as lithium-ion (Li-ion) polymer batteries are quite viable options for storing energy in EVs and HEVs. In addition, thermal management is essential for achieving the desired performance and life cycle from a particular battery. Therefore, to design a thermal management system, a designer must study the thermal characteristics of batteries. The thermal characteristics that are needed include the surface temperature distribution, heat flux, and the heat generation from batteries under various charge/discharge profiles. Therefore, in the first part of the research, surface temperature distribution from a lithium-ion pouch cell (20Ah capacity) is studied under different discharge rates of 1C, 2C, 3C, and 4C.

The development and calibration of stress state-dependent failure criteria for advanced high-strength steel (AHSS) and aluminum alloys requires characterization under proportional loading conditions. Traditional tests to construct a forming limit diagram (FLD), such as Marciniak or Nakazima tests, are based upon identifying the onset of strain localization or a tensile instability (neck). However, the onset of localization is strongly dependent on the through-thickness strain gradient that can delay or suppress the formation of a tensile instability so that cracking may occur before localization. As a result, the material fracture limit becomes the effective forming limit in deformation modes with severe through-thickness strain gradients, and this is not considered in the traditional FLD. In this study, a novel bending test apparatus was developed based upon the VDA 238-100 specification to characterize fracture in plane strain bending using digital image correlation (DIC).

Forecasting the motion of the leading vehicle is a critical task for connected autonomous vehicles as it provides an efficient way to model the leading-following vehicle behavior and analyze the interactions. In this study, a personalized time-series modeling approach for leading vehicle trajectory prediction considering different driving styles is proposed. The method enables a precise, personalized trajectory prediction for leading vehicles with limited inter-vehicle communication signals, such as vehicle speed, acceleration, space headway, and time headway of the front vehicles. Based on the learning nature of human beings that a human always tries to solve problems based on grouping and similar experience, three different driving styles are first recognized based on an unsupervised clustering with a Gaussian Mixture Model (GMM).

The vision for the future automotive chassis is to interconnect the lateral, longitudinal, and vertical dynamics by separately controlling driving, braking, steering, and damping of each individual wheel. A major advantage of all wheel drive electric vehicles with four in-wheel motors is the possibility to control the torque and speed at each wheel independently. This paper proposes a traction controller for such a vehicle. It estimates the road's adhesion potential at each wheel and adjusts each motor voltage, such that the longitudinal slip is kept in an optimal range. For development and validation, a full vehicle model is designed in ADAMS/View software, in co-simulation with motor and control elements, modeled in MATLAB/Simulink.

Residual stress prediction arising from manufacturing processes provides paramount information for the fatigue performance assessment of components subjected to cyclic loading. The determination of the material model to be applied in the numerical model should be taken carefully. This study focuses on the estimation of residual stresses generated after deep rolling of cast iron crankshafts. The researched literature on the field employs the available commercial material codes without closer consideration on their reverse loading capacities. To mitigate this gap, a single element model was used to compare potential material models with tensile-compression experiments. The best fit model was then applied to a previously developed crankshaft deep rolling numerical model. In order to confront the simulation outcomes, residual stresses were measured in two directions on real crankshaft specimens that passed through the same modeled deep rolling process.

In this study, a finite element software application (SORPAS®) is used to simulate the effect of pulsing on the expected weld thermal cycle during resistance spot welding (RSW). The predicted local cooling rates are used in combination with experimental observation to study the effect pulsing has on the microstructure and mechanical properties of Zn-coated DP600 AHSS (1.2mm thick) spot welds. Experimental observation of the weld microstructure was obtained by metallographic procedures and mechanical properties were determined by tensile shear testing. Microstructural changes in the weld metal and heat affect zone (HAZ) were characterized with respect to process parameters.

In this work, a new air hybrid engine is introduced in which two throttles are used to manage the engine load in three modes of operation i.e. braking, air motor, and conventional mode. The concept includes an air tank to store pressurized air during braking and rather than a fully variable valve timing (VVT) system, two throttles are utilized. Use of throttles can significantly reduce the complexity of air hybrid engines. The valves need three fixed timing schedules for the three modes of operation. To study this concept, for each mode, the results of engine simulations using GT-Power software are used to generate the operating maps. These maps show the maximum braking torque as well as maximum air motor torque in terms of air tank pressure and engine speed. Moreover, the resulting maps indicate the operating conditions under which each mode is more effective. Based on these maps, a power management strategy is developed to achieve improved fuel economy.

This study investigates the fundamental stiffness and damping properties of a self-damped pneumatic suspension system, based on both the experimental and analytical analyses. The pneumatic suspension system consists of a pneumatic cylinder and an accumulator that are connected by an orifice, where damping is realized by the gas flow resistance through the orifice. The nonlinear suspension system model is derived and also linearized for facilitating the properties characterization. An experimental setup is also developed for validating both the formulated nonlinear and linearized models. The comparisons between the measured data and simulation results demonstrate the validity of the models under the operating conditions considered. Two suspension property measures, namely equivalent stiffness coefficient and loss factor, are further formulated.

Many design problems are discovered often late in the development process, when design flexibility is limited. It is the art of the refinement engineers to find a solution to any unpredicted issues at this stage. The refinement process contains many hours of testing and requires many prototypes. Having an accurate experimental model of the system in this phase could reduce refinement time significantly. One of the areas that usually require refinement and tuning late in the design process is engine and body mounting systems. In this paper, we introduce a technique to optimize the mounting system of a vehicle for a given objective function using experimental/numerical analysis. To obtain an accurate model of the vehicle, we introduce an experimental procedure based upon the substructuring method. The method eliminates the need for any accurate finite element method of the vehicle. Experimental results of the implementation of this approach to a real vehicle are presented.

The paper describes the fabrication and testing of thin sheet metal uniaxial fatigue specimens that have been laminated to prevent buckling. When hot or cold rolled metal thicknesses are below 5 mm, the usual fatigue specimens, having a uniform gauge length of 7.5 mm or more, buckle in the short life region (∼10000 cycles) of strain-life testing. For thinner materials, non-standard specimen designs or anti-buckling guides have been used, but each of these solutions requires additional instrumentation. The results presented in this paper show that laminating multiple sheets of material together to increase the specimen's effective thickness raises the strain level for the onset of buckling of the standard uniaxial specimen. Constant and variable amplitude fatigue tests extending into the high-strain short-life region were performed. Fatigue life data for multiple layer specimens were in good agreement with those obtained for single layer specimens.

A ferritic-pearlitic nodular iron automobile suspension knuckle was fatigue tested in the laboratory using a constant amplitude load level that simulated a severe service condition. It was found that cracks always initiated from surface casting defects and that the fatigue life could be extended significantly by machining away the as-cast surface in the fatigue sensitive locations. Both local strain and fracture mechanics approaches were used successfully to predict the fatigue life of the component.

Details of the development of metal transfer and friction were studied by drawing cold-rolled bare, galvannealed, electrogalvanized, and hot-dip galvanized strips with a mineral-oil lubricant of 30 cSt viscosity at 40 C, over a total distance of 2500 mm by three methods. An initial high friction peak was associated with metal transfer to the beads and was largest with pure zinc and smallest with Fe-Zn coatings. Insertion of a new strip disturbed the coating and led to the development of secondary peaks. Long-term trends were governed by the stability of the coating. Stearic acid added to mineral oil delayed stabilization of the coating and increased contact area and thus friction with pure zinc surfaces. The usual practice of reporting average friction values can hide valuable information on lubrication mechanisms and metal transfer.

The present work is aimed at investigating the tribological factors influencing the LDH test. The material used was AKDQ cold-rolled bare steel, 0.82mm thick. The investigated factors included: test speed (0.833, 4.167, 6.667, and 8.333 mm/s), lubricant viscosity (4.5, 7.0, and 12.5 mm2/s), punch roughness (0.033 and 0.144 μm Ra), and test temperature (25 and 50 °C). Test speed and lubricant viscosity form a variation of the numerator of the Stribeck curve's x-axis (ηV). With ηV increasing from 4 to 120 mm3/s2 friction decreased, resulting in a 0.5 mm higher LDH. Increasing the punch roughness decreased friction producing an increase of 0.25 mm in the LDH. There appears to be an optimum roughness -- at which the roughness features act as lubricant reservoirs but the asperities do not break through the lubricant film -- resulting in minimum friction, therefore, maximum LDH.

Robust lane marking detection remains a challenge, particularly in temperate climates where markings degrade rapidly due to winter conditions and snow removal efforts. In previous work, dynamic Bayesian networks with heuristic features were used with the feature distributions trained using semi-supervised expectation maximization, which greatly reduced sensitivity to initialization. This work has been extended in three important respects. First, the tracking formulation used in previous work has been corrected to prevent false positives in situations where only poor RANSAC hypotheses were generated. Second, the null hypothesis is reformulated to guarantee that detected hypotheses satisfy a minimum likelihood. Third, the computational requirements have been greatly reduced by computing an upper bound on the marginal likelihood of all part hypotheses upon generation and rejecting parts with an upper bound less likely than the null hypothesis.

Recently, the development of braking assistance system has largely benefit the safety of both driver and pedestrians. A robust prediction and detection of driver braking intention will enable driving assistance system response to traffic situation correctly and improve the driving experience of intelligent vehicles. In this paper, two types unsupervised clustering methods are used to build a driver braking intention predictor. Unsupervised machine learning algorithms has been widely used in clustering and pattern mining in previous researches. The proposed unsupervised learning algorithms can accurately recognize the braking maneuver based on vehicle data captured with CAN bus. The braking maneuver along with other driving maneuvers such as normal driving will be clustered and the results from different algorithms which are K-means and Gaussian mixture model (GMM) will be compared.

The automobile industry has been undergoing a transition from fossil fuels to a low emission platform due to stricter environmental policies and energy security considerations. Electric vehicles, powered by lithium-ion batteries, have started to attain a noticeable market share recently due to their stable performance and maturity as a technology. However, electric vehicles continue to suffer from two disadvantages that have limited widespread adoption: charging time and energy density. To mitigate these challenges, vehicle Original Equipment Manufacturers (OEMs) have developed different vehicle architectures to extend the vehicle range. This work seeks to compare various powertrains, including: combined power battery electric vehicles (BEV) (zinc-air and lithium-ion battery), zero emission fuel cell vehicles (FCV)), conventional gasoline powered vehicles (baseline internal combustion vehicle), and ICE engine extended range hybrid electric vehicle.

Lithium-ion batteries, which are nowadays common in laptops, cell phones, toys, and other portable electronic devices, are also viewed as a most promising advanced technology for electric and hybrid electric vehicles (EVs and HEVs), but battery manufacturers and automakers must understand the performance of these batteries when they are scaled up to the large sizes needed for the propulsion of the vehicle. In addition, accurate thermo-physical property input is crucial to thermal modeling. Therefore, a designer must study the thermal characteristics of batteries for improvement in the design of a thermal management system and also for thermal modeling. This work presents a purely experimental thermal characterization in terms of measurement of the temperature gradient and temperature response of a lithium-ion battery utilizing a promising electrode material, LiFePO4, in a prismatic pouch configuration.

Hybrid electric vehicles (HEVs) are considered as the most commercial prospects new energy vehicles. Most HEVs have adopted Atkinson cycle engine as the main drive power. Atkinson cycle engine uses late intake valve closing (LIVC) to reduce pumping losses and compression work in part load operation. It can transform more heat energy to mechanical energy, improve engine thermal efficiency and decrease fuel consumption. In this paper, the investigations of Atkinson cycle converted from conventional Otto cycle gasoline engine have been carried out. First of all, high geometry compression ratio (CR) has been optimized through piston redesign from 10.5 to 13 in order to overcome the intrinsic drawback of Atkinson cycle in that combustion performance deteriorates due to the decline in the effective CR. Then, both intake and exhaust cam profile have been redesigned to meet the requirements of Atkinson cycle engine.