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Finite element (FE) based simulations for fully trimmed bodies are a key tool in the automotive industry to predict and understand the Noise, Vibration and Harshness (NVH) behavior of a complete car. While structural and acoustic transfer functions are nowadays straight-forward to obtain from such models, the comprehensive understanding of the intrinsic behavior of the complete car is more complex to achieve, in particular when it comes to the contribution of each sub-part to the global response. This paper proposes a complete target cascading process, which first assesses which sub-part of the car is the most contributing to the interior noise, then decomposes the total structure-borne acoustic transfer function into several intermediate transfer functions, allowing to better understand the effect of local design changes.

This study deals with the fatigue life prediction methodology of welding simulation components involving arc welding. First, a method for deriving the cyclic deformation and fatigue properties of the weld metal (that is also called ER70S-3 in AWS, American Welding Standard) is explained using solid bar specimens. Then, welded tube specimens were used with two symmetric welds and subjected to axial, torsion, and combined in-phase and out-of-phase axial-torsion loads. In most previous studies the weld bead’s start/stop were arbitrarily removed by overlapping the starting and stop point. Because it can reduce fatigue data scatter. However, in this study make the two symmetric weld’s start/stops exposed to applying load. Because the shape of the weld bead generated after the welding process can act as a notch (Ex. root notch at weld start / Crater at weld stop) to an applied stress. Accordingly, they were intentionally designed to cause stress concentrations on start/stops.

A special spot weld element (SWE) is presented for simplified representation of spot joints in complex structures for structural durability evaluation using the mesh-insensitive structural stress method. The SWE is formulated using rigorous linear four-node Mindlin shell elements with consideration of weld region kinematic constraints and force/moments equilibrium conditions. The SWEs are capable of capturing all major deformation modes around weld region such that rather coarse finite element mesh can be used in durability modeling of complex vehicle structures without losing any accuracy. With the SWEs, all relevant traction structural stress components around a spot weld nugget can be fully captured in a mesh-insensitive manner for evaluation of multiaxial fatigue failure.

This paper presents deep learning-based prognostics and health management (PHM) for predicting fractures of an electric propulsion (eP) drivetrain system using real-time CAN signals. The deep learning algorithm, based on autoencoders, resamples time-series signals and converts them into 2D images using recurrence plots (RP). Subsequently, through unsupervised learning of DeepSVDD, it detects anomalies in the converted 2D images and predicts the failure of the system in real-time. Also, reliability analysis based on fracture mechanics was performed using the detected signals and big data. In particular, the severity of the eP drivetrain system is proportional to the maximum shear stress (τmax) in terms of linear elastic fracture mechanics (LEFM) and can be calculated by summarizing the relationship between cracks (a) and the stress intensity factor (KIII).

This paper analyzes the leakage magnetic field generated by the Bi-Directional wireless charging system of Electric Vehicle(EV) and confirms the effect of the shielding coil in the Bi-Directional wireless charging system. In particular, in EV using the Inductive Power Transfer(IPT) method, the effective shielding coil position is proposed by analyzing the contribution of the leakage magnetic field of the Ground Assembly(GA) coil and the Vehicle Assembly(VA) coil according to the power transfer direction. Simulations were conducted using the WPT3/Z2 model of the standard SAE J2954, and it was confirmed that the GA coil contributed more to the leakage magnetic field due to the relatively large size compared to the VA coil regardless of power transfer direction.

Owing to their weight saving potential and improved flexural stiffness, metal-polymer-metal sandwich laminates are finding increasing applications in recent years. Increased use of such laminates for automotive body panels and structures requires not only a better understanding of their mechanical behavior, but also their formability characteristics. This study focuses on the formability of a metal–polymer-metal sandwich laminate that consists of AA5182 aluminum alloy as the outer skin layers and polypropylene (PP) as the inner core. The forming limit curves of Al/PP/Al sandwich laminates are determined using finite element simulations of Nakazima test specimens. The numerical model is validated by comparing the simulated results with published experimental results. Strain paths for different specimen widths are recorded.

Finite element (FE) analyses of macroscopic stress-strain relations and failure modes for tensile tests of additively manufactured (AM) AlSi10Mg in different loading directions with respect to the building direction are conducted with consideration of melt pool (MP) microstructures and pores. The material constitutive relations in different orientations of AM AlSi10Mg are first obtained from fitting the experimental tensile engineering stress-strain curves by conducting axisymmetric FE analyses of round bar tensile specimens. Four representative volume elements (RVEs) with MP microstructures with and without pores are identified and selected based on the micrographs of the longitudinal cross-sections of the vertical and horizontal tensile specimens. Two-dimensional plane stress elastic-plastic FE analyses of the RVEs subjected to uniaxial tension are then conducted.

Connected autonomy brings with it the means of significantly increasing vehicle Energy Economy (EE) through optimal Eco-Driving control. Much research has been conducted in the area of autonomous Eco-Driving control via various methods. Generally, proposed algorithms fall into the broad categories of rules-based controls, optimal controls, and meta-heuristics. Proposed algorithms also vary in cost function type with the 2-norm of acceleration being common. In a previous study the authors classified and implemented commonly represented methods from the literature using real-world data. Results from the study showed a tradeoff between EE improvement and run-time and that the best overall performers were meta-heuristics. Results also showed that cost functions sensitive to the 1-norm of acceleration led to better performance than those which directly minimize the 2-norm.

Making manned and remotely-controlled wheeled and tracked vehicles easier to drive, especially off-road, is of great interest to the U.S. Army. If vehicles are easier to drive (especially closed hatch) or if they are driven autonomously, then drivers could perform additional tasks (e.g., operating weapons or communication systems), leading to reduced crew sizes. Further, poorly driven vehicles are more likely to get stuck, roll over, or encounter mines or improvised explosive devices, whereby the vehicle can no longer perform its mission and crew member safety is jeopardized. HMI technology and systems to support human drivers (e.g., autonomous driving systems, in-vehicle monitors or head-mounted displays, various control devices (including game controllers), navigation and route-planning systems) need to be evaluated, which traditionally occurs in mission-specific (and incomparable) evaluations.

The automotive industry widely accepted the launch of electric vehicles in the global market, resulting in the emergence of many new areas, including battery health, inverter design, and motor dynamics. Maintaining the desired thermal stress is required to achieve augmented performance along with the optimal design of these components. The HVAC system controls the coolant and refrigerant fluid pressures to maintain the temperatures of [Battery, Inverter, Motor] in a definite range. However, identifying the prominent factors affecting the thermal stress of electric vehicle components and their effect on temperature variation was not investigated in real-time. Therefore, this article defines the vector electric vehicle thermal operating point (EVTHOP) as the first step with three elements [instantaneous battery temperature, instantaneous inverter temperature, instantaneous stator temperature].

The automotive industry’s journey towards fully autonomous vehicles brings more and more vehicle control systems. Additionally, the reliability and robustness of all these systems must be guaranteed for all road and weather conditions before release into the market. However, the ever-increasing number of such control systems, in combination with the number of road and weather conditions, makes it unfeasible to test all scenarios in real life. Thus, the performance and robustness of these systems needs to be proven virtually, via vehicle simulations. The key challenge for performing such a range of simulations is that the tire performance is significantly affected by the road/weather conditions. An end user must therefore have access to the corresponding tire models. The current solution is to test tires under all road surfaces and operating conditions and then derive a set of model parameters from measurements.

As new technology is added to vehicles and traffic congestion increases, there is a concern that drivers will be overloaded. As a result, there has been considerable interest in measuring driver workload. This can be achieved using many methods, with subjective assessments such as the NASA Task Loading Index (TLX) being most popular. Unfortunately, the TLX is unanchored, so there is no way to compare TLX values between studies, thus limiting the value of those evaluations. In response, a method was created to anchor overall workload ratings. To develop this method, 24 subjects rated the workload of clips of forward scenes collected while driving on rural, urban, and limited-access roads in relation to 2 looped anchor clips. Those clips corresponded to Level of Service (LOS) A and E (light and heavy traffic) and were assigned values of 2 and 6 respectively.

Tubular sections are found in many automotive structural components such as front rails, cross beams, and sub-frames. They are also used in other vehicular structures, such as buses and rails. In many of these components, smaller tubular sections may be joined together using an adhesive to build the required structure. For crash safety applications, it is important that the joined tube sections be able to provide high energy absorption capability and withstand the impact load before the adhesive bond failure occurs. In this study, single lap tubular joints between two aluminum tubes are investigated for their crush performance at both quasi-static and high impact speeds using finite element analysis. A crash optimized adhesive Betamate 1496 is considered. The joint parameters, such as adhesive overlap length, tube diameters and tube lengths, are varied to determine their effects on energy absorption, peak and mean loads, and tube deformation mode.

Autonomy for multiple trucks to drive in a fixed-headway platoon formation is achieved by adding precision GPS and V2V communications to a conventional adaptive cruise control (ACC) system. The performance of the Cooperative ACC (CACC) system depends heavily on the reliability of the underlying V2V communications network. Using data recorded on precision-instrumented trucks at both ACM and NCAT test tracks, we provide an understanding of various effects on V2V network performance: Occlusions - non-line-of-sight (NLOS) between the Tx and Rx antenna may cause network signal loss. Rain - water droplets in the air may cause network signal degradation. Antenna position - antennas at higher elevation may have less ground clutter to deal with. RF interference - interference may cause network packet loss. GPS outage - outages caused by tree cover, tunnels, etc. may result in degraded performance. Road curvature - curves may affect antenna diversity.

In frontal collision of vehicles, the front bumper system is the first structural member that receives the energy of collision. In low speed impacts, the bumper beam and the crush cans that support the bumper beam are designed to protect the engine and the radiator from being damaged, while at high speed impacts, they are required to transfer the energy of impact as uniformly as possible to the front rails that contributes to the occupant protection. The bumper beam material today is mostly steels and aluminum alloys, but carbon fiber composites have the potential to reduce the bumper weight significantly. In this study, crash performance of bumper beams made of a boron steel, aluminum alloy 5182 and a carbon fiber composite with steel crush cans is examined for their maximum deflection, load transfer to crush cans, total energy absorption and failure modes using finite element analysis.

An EGR system has been significantly used in order to cope with reinforced exhaust gas regulation and enhancement of fuel efficiency. For the well-designed EGR cooler, performance analysis is basically required. Furthermore, boiling prediction of the EGR cooler is especially essential to evaluate durability failure of abnormal operating conditions in DPF. However, due to intrinsic complexity of detailed 3-dimensional heat transfer tubes in the EGR cooler, no precise technique of boiling prediction has been developed. Therefore, this research had been performed in order to fulfill 3 goals: (1) development of 3-dimentional performance prediction technique including boiling occurrence, (2) generation and validation of a new evaluation index for boiling, (3) development of an optimized EGR cooler for suppressing boiling. In order to increase analysis accuracy and reduce analysis efforts at the same time, 3-dimensional single-phase flow analysis was developed.

The new wireless rotor electrical machine has all the coils placed on the stator. The laminated wireless rotor serves as a magnetic field switcher. A wireless rotor is cold, and it facilitates machine cooling. The model of the wireless rotor machine includes electrical and mechanical components. The wireless rotor machines can find an application in practice because they have advantages over existing devices. In modeling, we use a new approach. We consider a motor as a natural control system with torque feedback. Such an approach simplifies analyses and adjustment of the operation of an electrical machine. First, we analyze an open-loop control system without torque feedback. In this case, by changing the rotor speed under external torque, we can receive the torque-speed motor characteristics. After some adjustments to the open-loop system, we can consider the close loop control system with torque feedback. We use a space vector representation for sinusoidal electric components.

The powertrain durability test mode often defines the method by reflecting figures such as frequency of use or severity, but in complex systems, durability is difficult to verify in real life conditions under simple conditions. Therefore, in this session, a new analysis method modeled for each driving unit is presented, rather than analyzing time series data in time to extract representative driving pattern from the perspective of the powertrain load reflecting driving situation and driver’s will by applying machine learning technique, and to develop realistic durability test evaluation mode.

Multiple models to estimate motion sickness (MS) have been proposed in the literature; however, few capture the influence of visual cues, limiting the models’ ability to predict MS that closely matches experimental MS data. This is especially significant in the presence of conflicts between visual and vestibular sensory signals. This paper provides an analysis of the gaps within existing MS estimation models and addresses these gaps by proposing the visual-vestibular motion sickness (VVMS) model. In this paper, the structure of the VVMS model, associated model parameters, and mathematical and physiological justification for selecting these parameters are presented. The VVMS model integrates vestibular sensory dynamics, visual motion perception, and visual-vestibular cue conflict to determine the conflict between the sensed and true vertical orientation of the passenger.