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A control algorithm is developed for active/semi-active suspensions which can provide more comfort and better handling simultaneously. A weighting parameter is tuned online which is derived from two components - slow and fast adaptation to assign weights to comfort and handling. After establishing through simulations that the proposed adaptive control algorithm can demonstrate a performance better than some controllers in prior-art, it is implemented on an actual vehicle (Cadillac STS) which is equipped with MR dampers and several sensors. The vehicle is tested on smooth and rough roads and over speed bumps.

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).

Due to the high power and energy density and also relative safety, lithium ion batteries are receiving increasing acceptability in industrial applications especially in transportation systems with electric traction such as electric vehicles and hybrid electric vehicles. In this regard, to ensure performance reliability, accurate modeling of calendar life of such batteries is a necessity. In fact, potential failure of Li-ion battery packs remains a barrier to commercialization. Battery pack life is a critical feature to warranty and maintenance planning for hybrid vehicles, and will require adaptive control systems to account for the loss in vehicle range, and loss in battery charge and discharge efficiency. Failure not only results in large replacement costs, but also potential safety concerns such as overheating or short circuiting which may lead to fires.

The detection and tracking of the damage process between surfaces in contact, together with an estimation of the remaining service life, are significant contributions to the efficient operation of hydraulic components. The commonly used approach of analyzing vibration signals in terms of spectral distributions, while being very effective, has some shortcomings. For example, the results are sensitive to both load and speed variations. The approach presented in this paper is based on the fact that the asperity distribution of surfaces in good condition have a near normal probability distribution. Deviation from this can be tracked using statistical moments. The Beta probability distribution provides a number of shapes, including normal, under the control of two positive numbers, α and β. Unlike the normal distribution, which indicates defects by kurtosis values higher than 3.0, the Beta distribution provides more flexibility.

A three dimensional IR-TRACC (Infrared Telescope Rod for Assessment of Chest Compression) was designed for the Test Device for Human Occupant Restraint (THOR) in recent years to measure chest deflections. Due to the design intricateness, the deflection calculation from the measurements is sophisticated. An algorithm was developed in this paper to calculate the three dimensional deflections of the chest. The algorithm calculates the compression and also converts the results to the local spine coordinate system so that it can correlate with the Post Mortem Human Subject (PMHS) measurements for injury calculation. The method was also verified by a finite element calculation for accuracy, comparing the calculation from the corresponding model output and the direct point to point measurements. In addition, the IR-TRACC calibration methods are discussed in this paper.

Machine learning (ML) plays an ever-increasing role in advanced automotive functionality for driver assistance and autonomous operation; however, its adequacy from the perspective of safety certification remains controversial. In this paper, we analyze the impacts that the use of ML within software has on the ISO 26262 safety lifecycle and ask what could be done to address them. We then provide a set of recommendations on how to adapt the standard to better accommodate ML.

Significant improvement in fuel consumption, torque delivery and emission could be achieved through flexible control of the valve timings, duration and lift. In most existing electro-hydraulic variable valve actuation systems, the desired valve lift within every engine cycle is achieved by accurately controlling of the solenoid-valve opening interval; however, due to slow response time, precision control of these valves is difficult particularly during higher engine speeds. In this paper a new lift control strategy is proposed based on the hydraulic supply pressure and flow control. In this method, in order to control the peak valve lift, the hydraulic pump speed is precisely controlled using a two-input gearbox mechanism. This eliminates the need for precision control of the solenoid valves opening interval within every cycle.

This paper examines the application of damage models in tube bending and subsequent hydroforming of AlMg3.5Mn aluminum alloy tubes. An in-house Gurson-based damage model, incorporated within LS-DYNA, has been used for the simulations. The applied damage model contains several void nucleation and growth parameters that must be determined for each material. A simpler straight tube hydroforming process was considered first to check the damage parameters and predicted ductility. Then the model was applied to a sequence of bending and hydroforming. The damage history from pre-bending was mapped to the hydroforming stage, to allow prediction of the overall ductility. The applied forming parameters in the simulation were based on data extracted during the experimental tests. Finally, the numerical results were compared to the experimental data.

Life Cycle Assessment (LCA) is commonly used to measure the environmental and economic impacts of engineering projects and/or products. However, there is some uncertainty associated with any LCA study. The LCA inventory analysis generally relies on imperfect data in addition to further uncertainties created by the assessment process itself. It is necessary to measure the effects that data and process uncertainty have on the LCA result and to communicate the level of uncertainty to those making decisions based on the LCA. To accomplish this, a systematic and rigorous means to assess the overall uncertainty in LCA results is required. This paper demonstrates the use of Monte Carlo Analysis to track and measure the propagation of uncertainty in LCA studies. The Monte Carlo technique basically consists of running repeated assessments using random input values chosen from a specified probable range.

Physical rig testing of a vehicle is often undertaken to obtain experimental data that can be used to ensure a mathematical model is an accurate representation of the vehicle under study. Kinematics and Compliance (K&C) testing is often used for this purpose. The relationship between the hard point locations and compliance parameters, and K&C characteristics of a suspension system is complex, and so automating the process to correlate the model to the test data can make the exercise easier, faster and more accurate than hand tuning the model. In this work, such a process is developed. First, the model parameters are adjusted, next a simulation is run, before the results are read and post processed. This automation processed is used in conjunction with an optimization procedure to carry out the K&C correlation.

Whole-body vibration (WBV) is associated with several adverse health and safety outcomes including low-back pain (LBP) and driver fatigue. The objective of this study was to evaluate the efficacy of three commercially-available air-suspension truck seats for reducing truck drivers’ exposures to WBV. Seventeen truck drivers operating over a standardized route were recruited for this study and three commercially-available air suspension seats were evaluated. The predominant, z-axis average weighted vibration (Aw) and Vibration Dose Values (VDV) were calculated and normalized to represent eight hours of truck operation. In addition, the Seat Effective Amplitude Transmissibility (SEAT), the ratio of the seat-measured vibration divided by the floor-measured vibration, was compared across the three seats. One seat had significantly higher on-road WBV exposures whereas there were no differences across seats in off-road WBV exposures.

1 Crack initiation and propagation test data gathered during tests on Keyhole notched samples is used to evaluate a fatigue life prediction technique. Materials tested include a lower strength ManTen steel and a higher strength Boron steel, RQC-100, both tested with constant and variable amplitude histories. Initiation fatigue life is predicted using the usual method of plasticity correction at the notch followed by a Palmgren-Miner summation of damage with mean stress correction. The emphasis of the study is on simulating the crack propagation results. For that phase discretetize da/dN vs ΔK lines and thresholds for negative R ratios, are used specifically to help predict the propagation for one of the VA histories that had a significant negative mean. The open source crack propagation simulation program applies a material memory model to determine the crack advance on a reversal by reversal basis.

The interactions between automatic controls, physics, and driver is an important step towards highly automated driving. This study investigates the dynamical interactions between human-selected driving modes, vehicle controller and physical plant parameters, to determine how to optimally adapt powertrain control to different human-like driving requirements. A cyber-physical system (CPS) based framework is proposed for co-design optimization of the physical plant parameters and controller variables for an electric powertrain, in view of vehicle’s dynamic performance, ride comfort, and energy efficiency under different driving modes. System structure, performance requirements and constraints, optimization goals and methodology are investigated. Intelligent powertrain control algorithms are synthesized for three driving modes, namely sport, eco, and normal modes, with appropriate protocol selections. The performance exploration methodology is presented.

Tessellation methods have been applied to characterize second phase particle fields and the degree of clustering present in AA 5754 and 5182 automotive sheet alloys. A model of damage development within these materials has been developed using a damage percolation approach based on measured particle distributions. The model accepts tessellated particle fields in order to capture the spatial distributions of particles, as well as nearest neighbour and cluster parameter data. The model demonstrates how damage initiates and percolates within particle clusters in a stable fashion for the majority of the deformation history. Macro-cracking leading to final failure occurs as a chain reaction with catastrophic void linkage triggered once linkage beyond three or more clusters of voids takes place.

Using standard tensile testing methods, the material properties of AKDQ and DP600 steels tubes along the axial direction were determined. A novel in-situ optical strain mapping system ARAMIS® was utilized to evaluate the strain distribution during tensile testing along the axial direction. Microstructural and damage characterization was carried out using microscopy and image analysis techniques to compare the damage evolution and formability of both materials. Failure in both steels was observed to occur via a ductile failure mode. AKDQ was found to be the more formable material as it can achieve higher strains, total elongations and thinning prior to failure than the higher strength DP600.

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 characterization of sheet metals under in-plane uniaxial bending is challenging due to the aspect ratios involved that can cause buckling. Anti-buckling plates can be employed but require compensation for contact pressure and friction effects. Recently, a novel in-plane bending fixture was developed to allow for unconstrained sample rotation that does not require an anti-buckling device. The objective of the present study is to design the sample geometry for sheared edge fracture characterization under in-plane bending along with a methodology to resolve the strains exactly at the edge. A series of virtual experiments were conducted for a 1.0 mm thick model material with different hardening rates to identify the influence of gage section length, height, and the radius of the transition region on the bend ratio and potential for buckling. Two specimen geometries are proposed with one suited for constitutive characterization and the other for sheared edge fracture.

One of the major concerns preventing wider utilization of high strength steels (HSS) and advanced high strength steels (AHSS) in hydroforming is their inherent lower formability, compared to conventional mild steels. The application of the axial forces on the tube ends during a hydroforming operation is often referred to as end-feed, and can facilitate deformation of the tube by postponing failure. This research examines the effect of end-feed on the formability of HSS and AHSS tubes during hydroforming. Through simulation, straight and pre-bent tubes are hydroformed at different levels of end-feed for three materials: DDQ, HSLA350 and DP600.

This research examines the effect of endfeed on the thickness and strains during bending of steel tubes. The tubes were bent using an instrumented rotary draw tube bender and subsequently hydroformed into a diamond-profile outside corner fill die. DQAK tubes with an OD of 76.2 mm and a thickness of 1.55 mm were investigated. Endfeed during bending was found to have a significant effect on the thickness and strains within the tube after bending, and numerical models that were generated showed good agreement with the experimental data. It is shown how slight changes in thickness can cause localized failure during hydroforming, and how excessive die clearances can cause large strains in undesired areas.

The effects of bead surface roughness on friction, die pickup, and sheet surface damage in the drawbead test were investigated. Beads of HRC 58 hardness were prepared from centerless-ground rod by circumferential honing to 0.05 μm roughness, followed by finishing with 100, 400, or 600 grit SiC paper in the axial direction. Paraffinic base oils with viscosities of 4.5, 30, and 285 mm2/s were used neat and in conjunction with stearic acid. The effects of bead roughness depended on the nature of metal transfer, especially its distribution and firmness of attachment. The presence of a boundary additive increased, decreased, or had no effect on friction depending on the particular coating and bead finish.