Search Results

Android applications have historically faced vulnerabilities to man-in-the-middle attacks due to insecure custom SSL/TLS certificate validation implementations. In response, Google introduced the Network Security Configuration (NSC) as a configuration-based solution to improve the security of certificate validation practices. NSC was initially developed to enhance the security of Android applications by providing developers with a framework to customize network security settings. However, recent studies have shown that it is often not being leveraged appropriately to enhance security. Motivated by the surge in vehicular connectivity and the corresponding impact on user security and data privacy, our research pivots to the domain of mobile applications for vehicles. As vehicles increasingly become repositories of personal data and integral nodes in the Internet of Things (IoT) ecosystem, ensuring their security moves beyond traditional issues to one of public safety and trust.

Multiple experimental studies were performed on galling intiation for variety of tooling materials, coatings and surface treatments, sheet materials with various surface textures and lubrication. Majority of studies were performed for small number of samples in laboratory conditions. In this paper, the methodology of screening experiment using different combinations of tooling configurations and sheet material in the lab followed by the high volume small scale U-bend performed in the progressive die on the mechanical press is discussed. The experimental study was performed to understand the effect of the interface between the sheet metal and the die surface on sheet metal flow during stamping operations. Aluminum sheet AA5754 2.5mm thick was used in this experimentation. The sheet was tested in laboratory conditions by pulling between two flat insert with controllable clamping force and through the drawbead system with variable radii of the female bead.

The US EPA and the California Air Resources Board (CARB) require electric vehicle range to be determined according to the Society of Automotive Engineers (SAE) surface vehicle recommended practice J1634 - Battery Electric Vehicle Energy Consumption and Range Test Procedure. In the 2021 revision of the SAE J1634, the Short Multi-Cycle Test (SMCT) was introduced. The proposed testing protocol eases the chassis dynamometer test burden by performing a 2.1-hour drive cycle on the dynamometer, followed by discharging the remaining battery energy into a battery cycler to determine the Useable Battery Energy (UBE). Opting for a cycler-based discharge is financially advantageous due to the extended operating time required to fully deplete a 70-100kWh battery commonly found in Battery Electric Vehicles (BEVs).

It is recognized that the heavier vehicles, the more emissions, thus the more imperative to electrify. In this study, long haul heavy-duty trucks are referred as HDTs, which are recognized as one of the hard-to-electrify vehicle segments, though the automotive industry has gained trending advantages of electrifying both light-duty cars and SUVs. Since big rigs such as Class 8 HDTs have significant road-block challenges for electrification due to the demanding long-hour work cycles in all weathers, this study focuses on quantifying those electrification challenges by taking advantage of the public data of Class 8 tractors & trailers. Tesla Semi is the research target though its vehicle spec data is sorted out with fragmentary information in the public domain. The key task is to analyze the battery capacity requirements due to environmental temperature and inherent aging over the lifespan.

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.

Level 2 (L2) partial driving automation systems are rapidly emerging in the marketplace. L2 systems provide sustained automatic longitudinal and lateral vehicle motion control, reducing the need for drivers to continuously brake, accelerate and steer. Drivers, however, remain critically responsible for safely detecting and responding to objects and events. This paper summarizes variations of L2 systems (hands-on and/or hands-free) and considers human drivers’ roles when using L2 systems and for designing Human-Machine Interfaces (HMIs), including Driver Monitoring Systems (DMSs). In addition, approaches for examining potential unintended consequences of L2 usage and evaluating L2 HMIs, including field safety effect examination, are reviewed. The aim of this paper is to guide L2 system HMI development and L2 system evaluations, especially in the field, to support safe L2 deployment, promote L2 system improvements, and ensure well-informed L2 policy decision-making.

The transition from combustion engines to electric propulsion is accelerating in every coordinate of the globe. The engineers had strived hard to augment the engine performance for more than eight decades, and a similar challenge had emerged again for electric vehicles. To analyze the performance of the engine, the vector engine operating point (EOP) is defined, which is common industry practice, and the performance vector electric vehicle motor operating point (EVMOP) is not explored in the existing literature. In an analogous sense, electric vehicles are embedded with three primary components, e.g., Battery, Inverter, Motor, and in this article, the EVMOP is defined using the parameters [motor torque, motor speed, motor current]. As a second aspect of this research, deep learning models are developed to predict the EVMOP by mapping the parameters representing the dynamic state of the system in real-time.

Often, when assessing the distraction or ease of use of an in-vehicle task (such as entering a destination using the street address method), the first question is “How long does the task take on average?” Engineers routinely resolve this question using computational models. For in-vehicle tasks, “how long” is estimated by summing times for the included task elements (e.g., decide what to do, press a button) from SAE Recommended Practice J2365 or now using new static (while parked) data presented here. Times for the occlusion conditions in J2365 and the NHTSA Distraction Guidelines can be determined using static data and Pettitt’s Method or Purucker’s Method. These first approximations are reasonable and can be determined quickly. The next question usually is “How likely is it that the task will exceed some limit?”

With the development of vehicles equipped with automated driving systems, the need for systematic evaluation of AV performance has grown increasingly imperative. According to ISO 34502, one of the safety test objectives is to learn the minimum performance levels required for diverse scenarios. To address this need, this paper combines two essential methodologies - scenario-based testing procedures and scoring systems - to systematically evaluate the behavioral competence of AVs. In this study, we conduct comprehensive testing across diverse scenarios within a simulator environment following Mcity AV Driver Licensing Test procedure. These scenarios span several common real-world driving situations, including BV Cut-in, BV Lane Departure into VUT Path from Opposite Direction, BV Left Turn Across VUT Path, and BV Right Turn into VUT Path scenarios.

Automated vehicles, in the form we see today, started off-road. Ideas, technologies, and engineers came from agriculture, aerospace, and other off-road domains. While there are cases when only on-road experience will provide the necessary learning to advance automated driving systems, there is much relevant activity in off-road domains that receives less attention. Implications of Off-road Automation for On-road Automated Driving Systems argues that one way to accelerate on-road ADS development is to look at similar experiences off-road. There are plenty of people who see this connection, but there is no formalized system for exchanging knowledge. Click here to access the full SAE EDGETM Research Report portfolio.

Motor vehicle crashes involving child Vulnerable Road Users (VRUs) remain a critical public health concern in the United States. While previous studies successfully utilized the crash scenario typology to examine traffic crashes, these studies focus on all types of motor vehicle crashes thus the method might not apply to VRU crashes. Therefore, to better understand the context and causes of child VRU crashes on the U.S. road, this paper proposes a multi-step framework to define crash scenario typology based on the Fatality Analysis Reporting System (FARS) and the Crash Report Sampling System (CRSS). A comprehensive examination of the data elements in FARS and CRSS was first conducted to determine elements that could facilitate crash scenario identification from a systematic perspective. A follow-up context description depicts the typical behavioral, environmental, and vehicular conditions associated with an identified crash scenario.

A multiple-injection combustion strategy has been developed for heated gasoline direct injection compression ignition (HGCI). Gasoline was injected into a 0.4L single cylinder engine at a fuel pressure of 300bar. Fuel temperature was increased from 25degC to a temperature of 280degC by means of electric injector heater. This approach has the potential of improving fuel efficiency, reducing harmful CO and UHC as well as particulate emissions, and reducing pressure rise rates. Moreover, the approach has the potential of reducing fuel system cost compared to high pressure (>500bar) gasoline direct injection fuel systems available in the market for GDI SI engines that are used to reduce particulate matter. In this study, a multiple injection strategy was developed using electric heating of the fuel prior to direct fuel injection at engine speed of 1500rpm and load of 12.3bar IMEP.

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.

Time-domain and frequency domain methods are two common methods for fatigue damage and life assessment. The frequency domain fatigue assessment methods are becoming increasingly popular recently because of their unique advantages over the traditional time-domain methods. Recently, a series of moment of load path based multiaxial fatigue life assessment approaches have been developed. Among them, the most recently developed effective second moment of load path (ESMLP) approach demonstrates its potentials of conducting fatigue damage and life assessment accurately and efficiently. ESMLP can be used for fatigue analysis even without resorting to cycle counting because of its unique mathematical and physical properties, such as quadratic form in the kernel of the moment integral, rotationally invariant, and being proportional to damage. Developing a better parameter for frequency-domain analysis is the driving force behind the development of ESMLP as a new fatigue damage parameter.

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.

The aim of this study is to develop a pathway towards Hydrogen combustoin on an opposed-piston four stroke engine (OP4S) by using 1D simulation code from Gamma Technologies. By its configuration, the OP4S engine has significant thermal efficiency benefits versus conventional ICE. The benefit of the OP4S is reduced heat losses due to elimination of the cylinder head, which increase the brake thermal efficiency. A hydrogen-fueled (H2) opposed-piston four stroke (OP4S) engine was modeled using GTPower to determine the potential on performance, thermal efficiency and emissions targets. The 1D model was first validated on E10 gasoline using experimental data and was used to explore changes to fuel type in NG and H2, fueling location (TPI and DI), fuel mixture strength (stoichiometric and lean), for an optimized plenum volume and turbocharger selection.