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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.

Battery packs of electric vehicles are typically composed of lithium-ion batteries with aluminum and copper acting as cell terminals. These terminals are joined together in series by means of connector tabs to produce sufficient power and energy output. Such critical electrical and structural cell terminal connections involve several challenges when joining thin, highly reflective and dissimilar materials with widely differing thermo-mechanical properties. This may involve potential deformation during the joining process and the formation of brittle intermetallic compounds that reduce conductivity and deteriorate mechanical properties. Among various joining techniques, laser welding has demonstrated significant advantages, including the capability to produce joints with low electrical contact resistance and high mechanical strength, along with high precision required for delicate materials like aluminum and copper.

Driving Automation Systems (DAS) are subject to complex road environments and vehicle behaviors and increasingly rely on sophisticated sensors and Artificial Intelligence (AI). These properties give rise to unique safety faults stemming from specification insufficiencies and technological performance limitations, where sensors and AI introduce errors that vary in magnitude and temporal patterns, posing potential safety risks. The Safety of the Intended Functionality (SOTIF) standard emerges as a promising framework for addressing these concerns, focusing on scenario-based analysis to identify hazardous behaviors and their causes. Although the current standard provides a basic cause-and-effect model and high-level process guidance, it lacks concepts required to identify and evaluate hazardous errors, especially within the context of AI. This paper introduces two key contributions to bridge this gap.

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.

Decision-making of lane-change for autonomous vehicles faces challenges due to the behavioral differences among human drivers in dynamic traffic environments. To enhance the performances of autonomous vehicles, this paper proposes a game theoretic decision-making method that considers the diverse Social Value Orientations (SVO) of drivers. To begin with, trajectory features are extracted from the NGSIM dataset, followed by the application of Inverse Reinforcement Learning (IRL) to determine the reward preferences exhibited by drivers with distinct Social Value Orientation (SVO) during their decision-making process. Subsequently, a reward function is formulated, considering the factors of safety, efficiency, and comfort. To tackle the challenges associated with interaction, a Stackelberg game model is employed.

Modern electrified vehicles rely on drivers to manually adjust control parameters to modify the vehicle's powertrain, such as regenerative braking strength selection or drive mode selection. However, this reliance on infrequent driver input may lead to a mismatch between the selected powertrain control modifiers and the true driving environment. It is therefore advantageous for an electric vehicle's powertrain controller to make online identifications of the current driving conditions. This paper proposes an online driving condition identification scheme that labels drive cycle intervals collected in real-time based on a clustering model, with the objective of informing adaptive powertrain control strategies. HDBSCAN and K-means clustering models are fitted to a data set of drive cycle intervals representing a full range of characteristic driving conditions.

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.

The Self-Piercing Rivet (SPR) is an effective method for joining aluminum sheets and dissimilar materials. The durability assessment of SPR joints is essential for the optimum design of the automotive body-in-white structure. Fatigue analysis is required for any structural system subject to cyclic loading where durability assessment is required. While there is no established fatigue life prediction model for SPR joints, Rupp’s model is a well-established fatigue life prediction method intended for resistance spot welds. Rupp’s model has been the automotive industry’s choice for fatigue life estimation due to its computational efficiency and ability to capture various loading conditions. The purpose of this study is to investigate the compatibility of Rupp’s model with SPR joints. Load-control fatigue testing was conducted on cross-tension SPR joints of aluminum sheets (Al 6016) with dissimilar thicknesses and SPR joints of dissimilar materials (Al 6016 to DX54D steel).

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.

Internal combustion engines will continue to be the leading power-train in the heavy-duty, on-highway sector as technologies like hydrogen, fuel cells, and electrification face challenges. Natural gas (NG) engines offer several advantages over diesel engines including near zero particle matter (PM) emissions, lower NOx emissions, lower capital and operating costs, availability of vast domestic NG resources, and lower CO2 emissions being the cleanest burning of all hydrocarbons (HC). The main limitation of this type of engine is the lower efficiency compared to diesel counterparts. Addressing the limitations (knock and misfire) for achieving diesel-like efficiencies is key to accomplishing widespread adoption, especially for the US market. With the aim to achieve high brake thermal efficiency (BTE), three (3) computational fluid dynamics (CFD) optimized pistons with three different compression ratios (CR) have been tested.

Diesel oxidation catalyst (DOC) is one of the critical catalyst components in modern diesel aftertreatment systems. It mainly converts unburned hydrocarbon (HC) and CO to CO2 and H2O before they are released to the environment. In addition, it also oxidizes a portion of NO to NO2, which improves the NOx conversion efficiency via fast SCR over the downstream selective catalytic reduction (SCR) catalyst. HC light-off tests, with or without the presence of NOx, has been typically used for DOC evaluation in laboratory. In this work, we aim to understand the influences of DOC light-off experimental conditions, such as initial temperature, initial holding time, HC species, with or without the presence of NOx, on the DOC HC light-off behavior. The results indicate that light-off test with lower initial temperature and longer initial holding time (at its initial temperature) leads to higher DOC light-off temperature.

The prediction accuracy of a three-way catalyst (TWC) model is highly associated with the ability of the model to incorporate the reaction kinetics of the emission process as a lambda function. In this study, we investigated the O2 and H2 concentration profiles of TWC reactions and used them as critical inputs for the development of a global TWC model. We presented the experimental data and global kinetic model showing the impact of thermal degradation on the performance of the TWC. The performance metrics investigated in this study included CH4, NOx, and CO conversions under lean, rich, and dithering light-off conditions to determine the kinetics of oxidation reactions and reduction/reforming/water-gas shift reactions as a function of thermal aging. The O2 and H2 concentrations were measured using mass spectrometry to track the change in the oxidation state of the catalyst and to determine the mechanism of the reactions under these light-off conditions.

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.

Safety, fuel economy and uptime are key requirements for the operation of heavy-duty line-haul trucks within a fleet. With the penetration of connectivity and automation technologies, energy optimal and safe operation of the trucks are further improved through Advanced Driver Assistance System (ADAS) features and automated technologies as in truck platooning. Understanding the braking capability of the vehicle is very important for optimal ADAS and platooning control system design and integration. In this paper, the importance of tire connectivity and tire conditions on truck stopping distance are demonstrated through testing. The test data is further utilized to develop tire models for integration in an optimal vehicle automation for platooning. New ways to produce and use the tire related information in real-time optimal control of platooning trucks are proposed and the contribution of tire information in fuel economy is quantified through simulations.

Safety assurance is a central concern for the development and societal acceptance of automated driving (AD) systems. Perception is a key aspect of AD that relies heavily on Machine Learning (ML). Despite the known challenges with the safety assurance of ML-based components, proposals have recently emerged for unit-level safety cases addressing these components. Unfortunately, AD safety cases express safety requirements at the system level and these efforts are missing the critical linking argument needed to integrate safety requirements at the system level with component performance requirements at the unit level. In this paper, we propose the Integration Safety Case for Perception (ISCaP), a generic template for such a linking safety argument specifically tailored for perception components. The template takes a deductive and formal approach to define strong traceability between levels.

For better fuel economy and reduced emissions; fuel system plays a very important role. There are some major challenges related to development of suitable fuel system due to high static (~2000 bar) and fluctuating pressures in high pressure (HP) fuel lines. This enforces to design leak proof joints as they directly affect engine operation and can cause customer inconvenience. It is also critical from safety standpoint. Sealing capability of a joint is generally evaluated by sealing pressure, length of the sealing width and retaining capability of joint preload over time. Theoretically, it is known that preload loss at a joint is a combination of several factors such as; thread pitch, nut stiffness and friction at threads. In our current work the cause of leakage in HP fuel line joints is explored. Using fish bone diagram for RCA (Root Cause Analysis), probable causes are narrowed down and design parameters responsible for preload loss are identified.

Valve seat inserts (VSI) are installed in cylinder heads to provide a seating surface for poppet valves. Insert material is more heat and wear resistant than the base cylinder head material and hence it makes them better suited for valve seating and improved engine durability. Also use of inserts permits easier repair or rebuild of cylinder heads as only the wear surfaces need to be replaced. Desirable performance characteristics are appropriate sealing, heat-transfer and minimizing valve’s seating face to VSI wear and undesired outputs include valve seat dropping and cracking. With the downsizing trend of diesel engines, it leads to increasing power density and therefore higher cylinder pressure and temperatures. Hence the engine components are getting exposed to more severe loadings and hence to damage modes, which were heretofore not experienced. Among such possible damage modes are insert’s yielding and corresponding press-fit loss leading to either it’s cracking or drop-out.