Search Results

Longevity is one of the great achievements of the twentieth century. This paper will explore ways that elderly people can employ technology to enhance their independence, loosely termed “ElderTech.” ElderTech is designed to establish a sustained, long-term investment in research and development (R&D) for technologies that can provide the largest growing population, Americans over the age of 65, with the tools to ensure active aging (maintaining independence, self-reliance, and an enhanced quality of life). It will also promote aging in place (in the home); and will address and ease Medicare's financial burden on the federal government. ElderTech is aimed to establish a technology framework that will ensure that the United States (U.S.) is ready to meet the needs of its older Americans.

This paper presents a simple method of using Voronoi partitions for estimating vehicle fuel economy from a limited set of engine operating conditions. While one of the overarching goals of engine research is to continually improve vehicle fuel economy, evaluating the impact of a change in engine operating efficiency on the resulting fuel economy is a non-trivial task and typically requires drive cycle simulations with experimental data or engine model predictions and a full suite of engine controllers over a wide range of engine speeds and loads. To avoid the cost of collecting such extensive data, proprietary methods exist to estimate fuel economy from a limited set of engine operating conditions. This study demonstrates the use of Voronoi partitions to cluster and quantize the fuel consumed along a complex trajectory in speed and load to generate fuel consumption estimates based on limited simulation or experimental results.

A GT-SUITE vehicle-aftertreatment model has been developed to examine the cold-start emissions reduction capabilities of a Vacuum Insulated Catalytic Converter (VICC). This converter features a thermal management system to maintain the catalyst monolith above its light-off temperature between trips so that most of a vehicle’s cold-start exhaust emissions are avoided. The VICC thermal management system uses vacuum insulation around the monoliths. To further boost its heat retention capacity, a metal phase-change material (PCM) is packaged between the monoliths and vacuum insulation. To prevent overheating of the converter during periods of long, heavy engine use, a few grams of metal hydride charged with hydrogen are attached to the hot side of the vacuum insulation. The GT-SUITE model successfully incorporated the transient heat transfer effects of the PCM using the effective heat capacity method.

Mcity at the University of Michigan in Ann Arbor provides a realistic off-roadway environment in which to test vehicles and drivers in complex traffic situations. It is intended for testing of various levels of vehicle automation, from advanced driver assistance systems (ADAS) to fully self-driving vehicles. In a recent human factors study of interfaces for teen drivers, we performed parallel experiments in a driving simulator and Mcity. We implemented driving scenarios of moderate complexity (e.g., passing a vehicle parked on the right side of the road just before a pedestrian crosswalk, with the parked vehicle partially blocking the view of the crosswalk) in both the simulator and at Mcity.

To date, model validation metric is prominently designed for non-dynamic model responses. Though metrics for dynamic responses are also available, they are specifically designed for the vehicle impact application and uncertainties are not considered in the metric. This paper proposes the validation metric for general dynamic system responses under uncertainty. The metric makes use of the popular U-pooling approach and extends it for dynamic responses. Furthermore, shape deviation metric was proposed to be included in the validation metric with the capability of considering multiple dynamic test data. One vehicle impact model is presented to demonstrate the proposed validation metric.

As GHG and fuel economy regulations of light-duty vehicles have become more stringent, advanced emissions reduction technology has extensively penetrated the US light-duty vehicle fleet. This new technology includes not only advanced conventional engines and transmissions, but also greater adoption of electrified powertrains. In 2022, electrified vehicles – including mild hybrids, strong hybrids, plug-ins, and battery electric vehicles – made up nearly 17% of the US fleet and are on track to further increase their proportion in subsequent years. The Environmental Protection Agency (EPA) has previously used its Advanced Light-Duty Powertrain and Hybrid Analysis (ALPHA) full vehicle simulation tool to evaluate the greenhouse gas (GHG) emissions of light-duty vehicles. ALPHA contains a library of benchmarked powertrain components that can be matched to specific vehicles to explore GHG emissions performance.

This SAE EDGE™ Research Report explores how the deployment of automated vehicles (AVs) will affect the insurance industry and the principles of liability that underly the structure of insurance in the US. As we trade human drivers for suites of sensors and computers, who (or what) is responsible when there is a crash? The owner of the vehicle? The automaker that built it? The programmer that wrote the code? Insurers have over 100 years of experience and data covering human drivers, but with only a few years’ worth of information on AVs – how can they properly predict the true risks associated with their deployment? Without an understanding of the nature and risks of AVs, how can the government agencies that regulate the insurance industry provide proper oversight? Do the challenges AVs present require a total reworking of our insurance and liability systems, or can our current structures be adapted to fit them with minor modifications?

As part of its midterm evaluation of the 2022-2025 light-duty greenhouse gas (GHG) standards, the Environmental Protection Agency (EPA) has been acquiring fuel efficiency data from testing of recent engines and vehicles. The benchmarking data are used as inputs to EPA’s Advanced Light Duty Powertrain and Hybrid Analysis (ALPHA) vehicle simulation model created to estimate GHG emissions from light-duty vehicles. For complete powertrain modeling, ALPHA needs both detailed engine fuel consumption maps and transmission efficiency maps. EPA’s National Vehicle and Fuels Emissions Laboratory has previously relied on contractors to provide full characterization of transmission efficiency maps. To add to its benchmarking resources, EPA developed a streamlined more cost-effective in-house method of transmission testing, capable of gathering a dataset sufficient to broadly characterize transmissions within ALPHA.

Tanker trucks are commonly used for transporting liquid material including chemical and petroleum products. On the one hand, tanker trucks are susceptible to rollover accidents due to the high center of gravity when they are loaded and due to the liquid sloshing effects when the tank is partially filled. On the other hand, tanker truck rollover accidents are among the most dangerous vehicle crashes, frequently resulting in serious to fatal driver injuries and significant property damage, because the liquid cargo is often hazardous and flammable. Therefore, effective schemes for tanker truck rollover avoidance are highly desirable and can bring a considerable amount of societal benefit. Yet, the development of such schemes is challenging, as tanker trucks can operate in various environments and be affected by manufacturing variability, aging, degradation, etc. This paper considers the use of Learning Reference Governor (LRG) for tanker truck rollover avoidance.

In-vehicle infotainment (IVI) platforms are getting increasingly connected. Besides OEM apps and services, the next generation of IVI platforms are expected to offer integration of third-party apps. Under this anticipated business model, vehicular sensor and event data can be collected and shared with selected third-party apps. To accommodate this trend, Google has been pushing towards standardization among proprietary IVI operating systems with their Android Automotive platform which runs natively on the vehicle’s IVI platform. Unlike Android Auto’s limited functionality of display-projecting certain smartphone apps to the IVI screen, Android Automotive will have access to the in-vehicle network (IVN), and will be able to read and share various vehicular sensor data with third-party apps. This increased connectivity opens new business opportunities for both the car manufacturer as well as third-party businesses, but also introduces a new attack surface on the vehicle.

In this paper, residual stress distributions in rectangular bars due to rolling or burnishing at very high rolling or burnishing loads are investigated by roll burnishing experiments and three-dimensional finite element analyses using ABAQUS. First, roll burnishing experiments on rectangular bars at two roller burnishing loads are presented. The results indicate the higher burnishing load induces lower residual stresses and the higher burnishing load does not improve fatigue lives. Next, in the corresponding finite element analyses, the roller is modeled as rigid and the roller rolls on the flat surface of the bar with a low coefficient of friction. The bar material is modeled as an elastic-plastic strain hardening material with a nonlinear kinematic hardening rule for loading and unloading.

In order to improve the steering stability of the dual-motor drive electric vehicle, Taking the yaw rate and the sideslip angle as the control variables, Using the improved two degree of freedom linear dynamic model and seven degree of freedom nonlinear vehicle dynamics model, The hierarchical structure is used to establish the dual-motor drive electric vehicle steering stability control strategy which consist of the upper direct yaw moment decision-making layer based on the sliding mode controller and the lower additional yaw moment distribution layer based on the optimization theory. The Matlab/Simulink-Carsim joint simulation platform was built. The control strategy proposed in this paper was simulated and verified under the snake test condition and double-line shift test condition.

As part of an ongoing assessment of the potential for reducing greenhouse gas (GHG) emissions of light-duty vehicles, the U.S. Environmental Protection Agency (EPA) has implemented an updated methodology for applying the results of full vehicle simulations to the range of vehicles across the entire fleet. The key elements of the updated methodology explored for this article, responsive to stakeholder input on the EPA’s fleet compliance modeling, include (1) greater transparency in the process used to determine technology effectiveness and (2) a more direct incorporation of full vehicle simulation results. This article begins with a summary of the methodology for representing existing technology implementations in the baseline fleet using EPA’s Advanced Light-duty Powertrain and Hybrid Analysis (ALPHA) full vehicle simulation. To characterize future technologies, a full factorial ALPHA simulation of every conventional technology combination to be considered was conducted.

Combatting the modification of automotive control systems is a current and future challenge for OEMs and suppliers. ‘Chip-tuning’ is a manifestation of manipulation of a vehicle's original setup and calibration. With the increase in automotive functions implemented in software and corresponding business models, chip tuning will become a major concern. Recognizing and reporting of tuned control units in a vehicle is required for technical as well as legal reasons. This work approaches the problem by capturing the behavior of relevant control units within a machine learning system called a recognition module. The recognition module continuously monitors vehicle's sensor data. It comprises a set of classifiers that have been trained on the intended behavior of a control unit before the vehicle is delivered. When the vehicle is on the road, the recognition module uses the classifier together with current data to ascertain that the behavior of the vehicle is as intended.

A method has been developed for calculating the internal temperature distribution in an aircraft tire while free rolling under load. The method uses an approximate stress analysis of each point in the tire as it rolls through the contact patch, and from this stress change the mechanical work done on each volume element may be obtained and converted into a heat release rate through a knowledge of material characteristics. The tire cross-section is then considered as a body with internal heat generation, and the diffusion equation is solved numerically with appropriate boundary conditions at the wheel and runway surface. Comparisons with buried thermocouples in actual aircraft tires shows good agreement.

Portable Emission Measurement Systems (PEMS) are used by the U.S. Environmental Protection Agency (EPA) to measure gaseous and particulate mass emissions from vehicles in normal, in-use, on-the-road operation to support many of its programs, including assessing mobile source emissions compliance, emissions factor assessment for in-use fleet modeling, and collection of in-use vehicle operational data to support vehicle simulation modeling programs. This paper discusses EPA’s use of Global Positioning System (GPS) measured altitude data and electronically logged vehicle speed data to provide real-world road grade data for use as an input into the Gamma Technologies GT-DRIVE+ vehicle model. The GPS measured altitudes and the CAN vehicle speed data were filtered and smoothed to calculate the road grades by using open-source Python code and associated packages.

The preliminary design of a single engine business jet is presented. The airplane is intended to fill a market niche surrounded by several types of airplanes: single engine (piston and turboprop) and entry-level twin engine airplanes (turboprop and turbofan). The Williams-Rolls FJ44 turbofan engine, with a takeoff thrust rating of 1900 pounds, is chosen as the powerplant because of its low acquisition and maintenance costs. The airplane is designed to carry four persons and baggage 1500 n.m. with VFR reserves, and is intended to meet FAR 23 standards — including the 61 knot single engine stall speed requirement. A parametric analysis of wing aspect ratio, thickness, and taper is performed to determine the best planform from the standpoint of weight, cruise speed, and cost. Maximum cruise speed is estimated to be 371 knots and the airplane purchase price is estimated to be 1.98 million. These results indicate the airplane will satisfy intended market niche.

Automated clutches for vehicle startup is being increasingly deployed in commercial trucks for benefits, which include driver comfort, gradient performance, improved clutch life, emissions and driveline vibration reduction potential. The process of designing the controller is divided into 2 parts. Firstly, the parameter estimation of previously developed driveline models is carried out. The procedure involves an off-line minimization technique based on measured and estimated speeds. Secondly, the nominal plant model is used to develop LQR based optimal control strategy, which takes into account the slip time, dissipated power and slip acceleration. Mathematical expression of the performance index is clearly developed. A variety of clutch lock up profiles can be incorporated by changing a single tuning parameter, thus providing the driver the ability to select a launch profile based on specific driving objectives.

A novel application in the field of Life Cycle Assessment is presented that investigates optimal vehicle retirement timing and design life. This study integrates Life Cycle Energy Analysis (LCEA) with Dynamic Replacement Modeling and quantifies the energy tradeoffs between operating an older vehicle versus replacing it with a new more energy efficient model. The decision to keep or replace a vehicle to minimizes life cycle energy consumption is influenced by several factors including vehicle production energy, current vehicle's fuel economy and its deterioration with age, the improvement in fuel economy technology of new model vehicles and annual vehicle miles traveled (VMT). Model simulations explore vehicle replacement under incremental improvements in vehicle technology and leapfrog technology improvements such as with the PNGV (Partnership for a New Generation of Vehicles).

In this research, off-road vehicle simulation is performed with tire-soil interaction model. The predictive semi-analytical model, which is originally developed for tire-snow interaction model by Lee [4], is applied as a tire-soil interaction model and is implemented to MSC/ADAMS, commercial multi-body dynamic software. It is applied to simulate the handling maneuver of military vehicle HMMWV. Two cases are simulated with Michigan sandy loam soil property. Each case has two maneuvers, straight-line brake and step steer (J-turn). First, tire-soil interaction model and conventional on-road tire model are simulated on the flat road of the same frictional coefficient. The proposed tire-soil interaction model provided larger force under the same slip. Second, the same maneuvers are performed with real off-road frictional coefficient. The proposed tire-soil model can be validated and the behavior of the off-road vehicle can be identified through two simulation cases.