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Fracture characterization of automotive metals under simple shear deformation is critical for the calibration of advanced fracture models employed in forming and crash simulations. In-plane shear fracture tests of high ductility materials have proved challenging since the sample edge fails first in uniaxial tension before the fracture limit in shear is reached at the center of the gage region. Although through-thickness machining is undesirable, it appears required to promote higher strains within the shear zone. The present study seeks to adapt existing in-plane shear geometries, which have otherwise been successful for many automotive materials, to have a local shear zone with a reduced thickness. It is demonstrated that a novel shear zone with a pocket resembling a “peanut” can promote shear fracture within the shear zone while reducing the risk for edge fracture. An emphasis was placed upon machinability and surface quality for the design of the pocket in the shear zone.

Accurately predicting the future trajectories of surrounding traffic agents is important for ensuring the safety of autonomous vehicles. To address the scenario of frequent interactions among traffic agents in the highway merging area, this paper proposes a trajectory prediction method based on interactive graph attention mechanism. Our approach integrates an interactive graph model to capture the complex interactions among traffic agents as well as the interactions between these agents and the contextual map of the highway merging area. By leveraging this interactive graph model, we establish an agent-agent interactive graph and an agent-map interactive graph. Moreover, we employ Graph Attention Network (GAT) to extract spatial interactions among trajectories, enhancing our predictions. To capture temporal dependencies within trajectories, we employ a Transformer-based multi-head self-attention mechanism.

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.

Electrical steel, also known as silicon steel, is a ferromagnetic material that is often used in electric vehicles (EVs) for stator and rotor applications. Since the design and manufacturing of rotors require the use of laminated thin electrical steel sheets, the fatigue characterization of these single sheets is of interest. In this study, a 0.27mm thick non-oriented electrical steel sheet was tested under cyclic loading in the load-controlled mode with the load ratio R = 0.1 at room temperature. The specimens were prepared using the Computer Numerical Control (CNC) machining method. The Smith-Watson-Topper mean stress correction was used to find the equivalent fully reversed stress-life (S-N) curve. The Basquin equation was used to describe the fatigue strength of the electrical steel and the fatigue parameters were extracted. Furthermore, a design curve with a reliability of 90% and a confidence level of 90% was generated using Owen’s Tolerance Limit method.

Electrical steels are silicon alloyed steels that possess great magnetic properties, making them the ideal material choice for the stator and rotor cores of electric motors. They are typically comprised of laminated stacks of thin electrical steel sheets. An electric motor can reach high temperatures under a heavy load, and it is important to understand the combined effect of temperature and load on the electrical steel’s performance to ensure the long life and safety of electric vehicles. This study investigated the fatigue strength and failure behavior of a 0.27mm thick electrical steel sheet, where the samples were prepared by a stamping process. Stress-control fatigue tests were performed at both room temperature and 150°C. The S-N curve indicated a decrease in the fatigue strength of the samples at the elevated temperature compared to the room temperature by 15-25 MPa in the LCF and HCF regimes, respectively.

Strict environmental regulations are driving the automotive industry toward electric vehicles as they offer zero emissions. A key component in electric vehicles is the electric motor, where the stator and rotor are manufactured from stacks of thin electrical steel sheets. The electrical steel sheets can be cut in different ways, and the cutting methods may significantly affect the fatigue strength of the component. It is important to understand the effect of the cutting processes on the fatigue properties of electrical steel to ensure there is no premature failure of the electric motor resulting from an improper cutting process. This investigation compared the effect of three different edge preparation methods (stamping, CNC machining, and waterjet cutting) on the fatigue performance of 0.27mm thick electrical steel sheets. To investigate the effect of the edge finish on fatigue behavior, surface roughness was measured for these different samples.

Plane strain tension is the critical stress state for sheet metal forming because it represents the extremum of the yield function and minima of the forming limit curve and fracture locus. Despite its important role, the stress response in plane strain deformation is routinely overlooked in the calibration of anisotropic plasticity models due to challenges and uncertainty in its characterization. Plane strain tension test specimens used for constitutive characterization typically employ large gage width-to-thickness ratios to promote a homogeneous plane strain stress state. Unfortunately, the specimens are limited to small strain levels due to fracture initiating at the edges in uniaxial tension. In contrast, notched plane strain tension coupons designed for fracture characterization have become common in the automotive industry to calibrate stress-state dependent fracture models. These coupons have significant stress and strain gradients across the gage width to avoid edge fracture.

The presence of residual stresses affects the fatigue response of welded components. In the present study of thick welded cantilever specimens, residual stresses were measured in two A36 steel samples, one in the as-welded condition, and one subjected to a short history of bending loads where substantial local plasticity is expected at the fatigue hot-spot weld toe. Extensive X-Ray Diffraction (XRD) measurements describe the residual stress state in a large region above the weld toe both in an untested as-welded sample and in a sample subjected to a short load history that generated an estimated 0.01 strain amplitude at the stress concentration zone at the weld toe. The results show that such a test will significantly alter the welding-induced residual stresses. Fatigue life prediction methods need to be aware that such alterations are possible and incorporate the effects of such cyclic stress relaxation in life computations.

The objective of this study was to assess the formability of two 3rd generation advanced high strength steels (3rd Gen AHSS) with ultimate strengths of 980 and 1180 MPa and evaluate their applicability to a structural B-Pillar for a mid-sized sport utility vehicle. The constitutive behavior including strain-rate effects and formability were characterized to generate the material models for use within AutoForm R8 software to design the B-pillar tooling and forming process. An extended Bressan-Williams instability model was able to deterministically predict the forming limit curves obtained using Marciniak tests. The tooling for the representative B-pillar was designed and fabricated with Bowman Precision Tooling and forming trials conducted for both 3rd Gen steels that had a thickness of 1.4 mm.

The superior formability and local ductility of the emerging class of third generation of advanced high-strength steels (3rd Gen AHSS) compared to their conventional counterparts of the same strength level offer significant advantages for automotive lightweighting and enhanced crash performance. Nevertheless, studies on the material behavior of 3rd Gen AHSS have been limited and there is some uncertainty surrounding the applicability of developed methodologies for conventional dual-phase (DP) steels to this new class of AHSS. The present paper provides a comprehensive study on the quasi-static and dynamic constitutive behavior, formability characterization and prediction, and the fracture behavior of two commercial 3rd Gen AHSS with an ultimate strength of 1180 MPa that will be contrasted with a conventional DP1180. The hardening response to large strain levels was determined experimentally using tensile and shear tests and then validated with 3-D simulations of tensile tests.

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).

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.

With the widespread development of automated driving systems (ADS), it is imperative that standardized testing methodologies be developed to assure safety and functionality. Scenario testing evaluates the behavior of an ADS-equipped subject vehicle (SV) in predefined driving scenarios. This paper compares four modes of performing such tests: closed-course testing with real actors, closed-course testing with surrogate actors, simulation testing, and closed-course testing with mixed reality. In a collaboration between the Waterloo Intelligent Systems Engineering (WISE) Lab and AAA, six automated driving scenario tests were executed on a closed course, in simulation, and in mixed reality. These tests involved the University of Waterloo’s automated vehicle, dubbed the “UW Moose”, as the SV, as well as pedestrians, other vehicles, and road debris.

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.

In this paper, a new solution method is presented to study the effect of wave propagation in engine manifolds, which includes solving one-dimensional models for compressible flow of air. Velocity, pressure, and density profiles are found by solving a system of non-linear Partial Differential Equations (PDEs) in space and time derived from Euler’s equations. The 1D model includes frictional losses, area change, and heat transfer. The solution is traditionally found by utilizing the Method of Characteristics and applying finite difference solutions to the resulting system of ordinary differential equations (ODEs) over a discretized grid. In this work, orthogonal collocation is used to solve the system of ODEs that is defined along the characteristic curves. Orthogonal polynomials are utilized to approximate velocity, pressure, sound speed, and the characteristic curves along which the system of PDEs reduce to a system of ODEs.

Fatigue crack initiation and propagation models predict the fatigue life of welded "T" specimens tested by the Fatigue Design and Evaluation (FDE) Committee of SAE under constant and variable amplitude load histories. The crack propagation equations stipulated by British Standard BS-7910 have been incorporated in a material memory model for cyclic deformation. The simulations begin with the crack initiation model and show how it is used to account for cyclic mean stress relaxation and the effects of periodic overloads. After the cracks initiate the BS-7910 model is applied to predict the crack advance due to either constant or variable amplitude histories. Simulation results correspond to the experimental results with good accuracy.

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).

This paper discusses modeling of a power-split hybrid electric vehicle and the design of a longitudinal dynamics controller for the University of Waterloo’s self-driving vehicle project. The powertrain of Waterloo’s vehicle platform, a Lincoln MKZ Hybrid, is controlled only by accelerator pedal actuation. The vehicle’s power management strategy cannot be altered, so a novel approach to grey-box modeling of the OEM powertrain control architecture and dynamics was developed. The model uses a system of multiple neural networks to mimic the response of the vehicle’s torque control module and estimate the distribution of torque between the powertrain’s internal combustion engine and electric motors. The vehicle’s power-split drivetrain and longitudinal dynamics were modeled in MapleSim, a modeling and simulation software, using a physics-based analytical approach.

In this paper, gear ratios of a two-speed transmission system are optimized for an electric passenger car. Quasi static system models, including the vehicle model, the motor, the battery, the transmission system, and drive cycles are established in MATLAB/Simulink at first. Specifically, since the regenerative braking capability of the motor is affected by the SoC of battery and motors torque limitation in real time, the dynamical variation of the regenerative brake efficiency is considered in this study. To obtain the optimal gear ratios, iterations are carried out through Nelder-Mead algorithm under constraints in MATLAB/Simulink. During the optimization process, the motor efficiency is observed along with the drive cycle, and the gear shift strategy is determined based on the vehicle velocity and acceleration demand. Simulation results show that the electric motor works in a relative high efficiency range during the whole drive cycle.

The development of a comprehensive battery simulator is essential for future improvements in the durability, performance and service life of lithium-ion batteries. Although simulations can never replace actual experimental data, they can still be used to provide valuable insights into the performance of the battery, especially under different operating conditions. In addition, a single-cell model can be easily extended to the pack level and can be used in the optimization of a battery pack. The first step in building a simulator is to create a model that can effectively capture both the voltage response and thermal behavior of the battery. Since these effects are coupled together, creating a robust simulator requires modeling both components. This paper will develop a battery simulator, where the entire battery model will be composed of four smaller submodels: a heat generation model, a thermal model, a battery parameter model and a voltage response model.