Showing papers by "Ford Motor Company published in 2010"

High capacity hydrogen storage materials: attributes for automotive applications and techniques for materials discovery

Abstract: Widespread adoption of hydrogen as a vehicular fuel depends critically upon the ability to store hydrogen on-board at high volumetric and gravimetric densities, as well as on the ability to extract/insert it at sufficiently rapid rates As current storage methods based on physical means—high-pressure gas or (cryogenic) liquefaction—are unlikely to satisfy targets for performance and cost, a global research effort focusing on the development of chemical means for storing hydrogen in condensed phases has recently emerged At present, no known material exhibits a combination of properties that would enable high-volume automotive applications Thus new materials with improved performance, or new approaches to the synthesis and/or processing of existing materials, are highly desirable In this critical review we provide a practical introduction to the field of hydrogen storage materials research, with an emphasis on (i) the properties necessary for a viable storage material, (ii) the computational and experimental techniques commonly employed in determining these attributes, and (iii) the classes of materials being pursued as candidate storage compounds Starting from the general requirements of a fuel cell vehicle, we summarize how these requirements translate into desired characteristics for the hydrogen storage material Key amongst these are: (a) high gravimetric and volumetric hydrogen density, (b) thermodynamics that allow for reversible hydrogen uptake/release under near-ambient conditions, and (c) fast reaction kinetics To further illustrate these attributes, the four major classes of candidate storage materials—conventional metal hydrides, chemical hydrides, complex hydrides, and sorbent systems—are introduced and their respective performance and prospects for improvement in each of these areas is discussed Finally, we review the most valuable experimental and computational techniques for determining these attributes, highlighting how an approach that couples computational modeling with experiments can significantly accelerate the discovery of novel storage materials (155 references)

Community-based participatory research: a capacity-building approach for policy advocacy aimed at eliminating health disparities.

Abstract: There have been increasing calls for community–academic partnerships to enhance the capacity of partners to engage in policy advocacy aimed at eliminating health disparities. Community-based participatory research (CBPR) is a partnership approach that can facilitate capacity building and policy change through equitable engagement of diverse partners. Toward this end, the Detroit Community–Academic Urban Research Center, a long-standing CBPR partnership, has conducted a policy training project. We describe CBPR and its relevance to health disparities; the interface between CBPR, policy advocacy, and health disparities; the rationale for capacity building to foster policy advocacy; and the process and outcomes of our policy advocacy training. We discuss lessons learned and implications for CBPR and policy advocacy to eliminate health disparities.

Adaptive optics via pupil segmentation for high-resolution imaging in biological tissues

Abstract: Biological specimens are rife with optical inhomogeneities that seriously degrade imaging performance under all but the most ideal conditions. Measuring and then correcting for these inhomogeneities is the province of adaptive optics. Here we introduce an approach to adaptive optics in microscopy wherein the rear pupil of an objective lens is segmented into subregions, and light is directed individually to each subregion to measure, by image shift, the deflection faced by each group of rays as they emerge from the objective and travel through the specimen toward the focus. Applying our method to two-photon microscopy, we could recover near-diffraction-limited performance from a variety of biological and nonbiological samples exhibiting aberrations large or small and smoothly varying or abruptly changing. In particular, results from fixed mouse cortical slices illustrate our ability to improve signal and resolution to depths of 400 microm.

Evaluated kinetic and photochemical data for atmospheric chemistry: Volume V – heterogeneous reactions on solid substrates

Abstract: . This article, the fifth in the ACP journal series, presents data evaluated by the IUPAC Subcommittee on Gas Kinetic Data Evaluation for Atmospheric Chemistry. It covers the heterogeneous processes on surfaces of solid particles present in the atmosphere, for which uptake coefficients and adsorption parameters have been presented on the IUPAC website in 2010. The article consists of an introduction and guide to the evaluation, giving a unifying framework for parameterisation of atmospheric heterogeneous processes. We provide summary sheets containing the recommended uptake parameters for the evaluated processes. Four substantial appendices contain detailed data sheets for each process considered for ice, mineral dust, sulfuric acid hydrate and nitric acid hydrate surfaces, which provide information upon which the recommendations are made.

An optimal-control-based framework for trajectory planning, threat assessment, and semi-autonomous control of passenger vehicles in hazard avoidance scenarios

Abstract: This paper formulates the vehicle navigation task as a constrained optimal control problem with constraints bounding a traversable region of the environment A model predictive controller iteratively plans an optimal vehicle trajectory through the constrained corridor and uses this trajectory to establish the minimum threat posed to the vehicle given its current state and driver inputs Based on this threat assessment, the level of controller intervention required to prevent departure from the traversable corridor is calculated and driver/controller inputs are scaled accordingly Simulated and experimental results are presented to demonstrate multiple threat metrics and configurable intervention laws

Methods and systems for assisted direct start control

Abstract: Method and systems are provided for controlling a vehicle system including an engine that is selectively deactivated during engine idle-stop conditions. One example method comprises, adjusting a brake torque applied to a deactivated rotating engine after an engine restart request, the brake torque applied to slow the engine to at least a predetermined threshold speed without stopping the engine, and engaging a starter to the still rotating engine to increase the engine speed and restart the engine.

Discrete-Time Current Regulator Design for AC Machine Drives

Abstract: This paper analyzes the behavior of discrete-time current regulators for ac machines operating at high ratios of fundamental-to-sampling frequencies, a situation common for high-speed automotive drives and large-traction drives. At high ratios of fundamental-to-sampling frequencies, highly oscillatory, or unstable, response can occur if the current regulator design does not properly incorporate the effects of the discrete nature of the controller, including delays between the sampling of signals and the application of the voltage commands through pulse-width modulation (PWM). This paper investigates these issues for different design methods and current regulator topologies. As part of this investigation, a simple discrete-time domain ac machine model is developed that includes the delays associated with PWM. This model is then used to design a discrete-time domain version of the complex vector PI current regulator that demonstrates improved response compared with the other regulators studied. Simulation and experimental results are provided to compare the performance, stability, and robustness of the current regulators analyzed.

Nonlinear Regularization for Per Voxel Estimation of Magnetic Susceptibility Distributions From MRI Field Maps

Abstract: Magnetic susceptibility is an important physical property of tissues, and can be used as a contrast mechanism in magnetic resonance imaging (MRI). Recently, targeting contrast agents by conjugation with signaling molecules and labeling stem cells with contrast agents have become feasible. These contrast agents are strongly paramagnetic, and the ability to quantify magnetic susceptibility could allow accurate measurement of signaling and cell localization. Presented here is a technique to estimate arbitrary magnetic susceptibility distributions by solving an ill-posed inversion problem from field maps obtained in an MRI scanner. Two regularization strategies are considered: conventional Tikhonov regularization and a sparsity promoting nonlinear regularization using the l 1 norm. Proof of concept is demonstrated using numerical simulations, phantoms, and in a stroke model in a rat. Initial experience indicates that the nonlinear regularization better suppresses noise and streaking artifacts common in susceptibility estimation.

A stochastic model predictive control approach for series hybrid electric vehicle power management

Abstract: This paper illustrates the use of stochastic model predictive control (SMPC) for power management in vehicles equipped with advanced hybrid powertrains Hybrid vehicles use two or more distinct power sources for propulsion, and their complex powertrain architecture requires the coordination of all the subsystems to achieve target performances in terms of fuel consumption, driveability, component life-time, exhaust emissions Many control strategies have been presented and successfully applied, mainly based on heuristics or rules and tuned on certain reference drive cycles To take into account that cycles are not exactly known a priori in driving routine, this paper proposes a stochastic approach for the power management problem We focus on a series hybrid electric vehicle (HEV), which combines an internal combustion engine and an electric motor The power demand from the driver is modeled as a Markov chain estimated on several driving cycles and used to generate scenarios in the SMPC law Simulation results over a standard driving cycle are presented to demonstrate the effectiveness of the proposed stochastic approach and compared with other deterministic approaches

Predictive Control of Autonomous Ground Vehicles With Obstacle Avoidance on Slippery Roads

Abstract: Two frameworks based on Model Predictive Control (MPC) for obstacle avoidance with autonomous vehicles are presented. A given trajectory represents the driver intent. An MPC has to safely avoid obstacles on the road while trying to track the desired trajectory by controlling front steering angle and differential braking. We present two different approaches to this problem. The first approach solves a single nonlinear MPC problem. The second approach uses a hierarchical scheme. At the high-level, a trajectory is computed on-line, in a receding horizon fashion, based on a simplified point-mass vehicle model in order to avoid an obstacle. At the low-level an MPC controller computes the vehicle inputs in order to best follow the high level trajectory based on a nonlinear vehicle model. This article presents the design and comparison of both approaches, the method for implementing them, and successful experimental results on icy roads.Copyright © 2010 by ASME

African Ancestry and Higher Prevalence of Triple-Negative Breast Cancer: Findings From an International Study

Abstract: BACKGROUND The study of breast cancer in women with African ancestry offers the promise of identifying markers for risk assessment and treatment of triple-negative disease.

Method and system for monitoring an energy storage system for a vehicle for trip planning

Abstract: One or more embodiments may include a trip planning system for planning a trip based on the charge of an electric vehicle battery. The trip planning system may include one or more computers located remotely from an electric vehicle which may have one or more battery packs for powering the electric vehicle. The computer(s) may be configured to receive geographic parameters defining a trip and a battery charge status of the one or more electric vehicle battery packs. The computer(s) may be further configured to determine that the trip cannot be completed based on the battery charge status. The computer(s) may additionally be configured to present a battery charge requirement for completing the trip.

Vapor Pressures of Alcohol−Gasoline Blends

Abstract: Reid vapor pressures (RVPs) were determined for alcohol−gasoline blends containing 5−85% by volume of methanol, ethanol, 1-propanol, 2-propanol, 1-butanol, 2-butanol, i-butanol (2-methyl-1-propanol), and t-butanol (2-methyl-2-propanol). The results are compared to literature data. Most alcohols form near-azeotropic mixtures with hydrocarbons in gasoline that affect the vapor pressure of the blend in a non-ideal manner. In addition, vapor pressures of dual-alcohol blends with gasoline containing a range of ethanol and 1-butanol volume percentages were determined. We demonstrate a simple method to prepare dual-alcohol−gasoline blends with RVPs indistinguishable from that of the base gasoline. It may be advantageous to use alcohols in addition to ethanol in the form of dual- or multi-alcohol blends as these become available in larger scale in the future.

Magnet Temperature Estimation in Surface PM Machines Using High-Frequency Signal Injection

Abstract: This paper proposes a method to estimate the magnet temperature in surface permanent-magnet machines using high-frequency carrier signal injection. The injection of a high-frequency signal, superimposed on the fundamental excitation, allows the estimation of the stator high-frequency impedance, which is a function of both the stator and rotor impedances. The temperature of the magnets is shown to have a significant weight on the overall stator high-frequency impedance, from which it can be estimated. The high-frequency carrier signal is injected intermittently in order to minimize potential adverse effects on the normal operation of the machine. This paper first explains the physics behind the magnet temperature dependence. Then, the principles of the method, as well as its practical implementation, are discussed. Experimental verification of the method is provided.

Inhalation anaesthetics and climate change

Abstract: Background Although the increasing abundance of CO2 in our atmosphere is the main driver of the observed climate change, it is the cumulative effect of all forcing agents that dictate the direction and magnitude of the change, and many smaller contributors are also at play Isoflurane, desflurane, and sevoflurane are widely used inhalation anaesthetics Emissions of these compounds contribute to radiative forcing of climate change To quantitatively assess the impact of the anaesthetics on the forcing of climate, detailed information on their properties of heat (infrared, IR) absorption and atmospheric lifetimes are required Methods We have measured the IR spectra of these anaesthetics and conducted calculations of their contribution to radiative forcing of climate change recognizing the important fact that radiative forcing is strongly dependent on the wavelength of the absorption features Results Radiative efficiencies of 0453, 0469, and 0351 W m−2 ppb−1 and global warming potentials (GWPs) of 510, 1620, and 210 (100 yr time horizon) were established for isoflurane, desflurane, and sevoflurane, respectively Conclusions On the basis of the derived 100 yr GWPs, the average climate impact per anaesthetic procedure at the University of Michigan is the same as the emission of ∼22 kg CO2 We estimate that the global emissions of inhalation anaesthetics have a climate impact which is comparable with that from the CO2 emissions from one coal-fired power plant or 1 million passenger cars

Distillation Curves for Alcohol−Gasoline Blends

Abstract: Distillation curves are presented for single-alcohol blends in gasoline, containing 5−85% by volume of methanol, ethanol, 1-propanol, 2-propanol, 1-butanol, 2-butanol, i-butanol (2-methyl-1-propanol), and t-butanol (2-methyl-2-propanol). Most alcohols are shown to form mixtures with gasoline exhibiting near-azeotropic behavior that significantly affect the shape of the distillation curves. The results are compared to literature data available for some alcohols. In addition, distillation curves for a variety of dual-alcohol blends are presented, containing 10% of each of two alcohols. We show that such dual-alcohol blends have distillation curves closer to that of the base gasoline than single-alcohol blends with 20% of either alcohol individually. At present, ethanol is the only biofuel alcohol available in scale. As other alcohol biofuels become available in the future, it may be advantageous to use them in dual- or multialcohol blends to minimize their impact on fuel volatility.

Intentional networking: opportunistic exploitation of mobile network diversity

Abstract: Mobile devices face a diverse and dynamic set of networking options. Using those options to the fullest requires knowledge of application intent. This paper describes Intentional Networking, a simple but powerful mechanism for handling network diversity. Applications supply a declarative label for network transmissions, and the system matches transmissions to the most appropriate network. The system may also defer and re-order opportunistic transmissions subject to application-supplied mutual exclusion and ordering constraints. We have modified three applications to use Intentional Networking: BlueFS, a distributed file system for pervasive computing, Mozilla's Thunderbird e-mail client, and a vehicular participatory sensing application. We evaluated the performance of these applications using measurements obtained by driving a vehicle through WiFi and cellular 3G network coverage. Compared to an idealized solution that makes optimal use of all aggregated available networks but that lacks knowledge of application intent, Intentional Networking improves the latency of interactive messages from 48% to 13x, while adding no more than 7% throughput overhead.

Stochastic model predictive control with driver behavior learning for improved powertrain control

Abstract: In this paper we advocate the use of stochastic model predictive control (SMPC) for improving the performance of powertrain control algorithms, by optimally controlling the complex system composed of driver and vehicle While the powertrain is modeled as the deterministic component of the dynamics, the driver behavior is represented as a stochastic system which affects the vehicle dynamics Since stochastic MPC is based on online numerical optimization, the driver model can be learned online, hence allowing the control algorithm to adapt to different drivers and drivers' behaviors The proposed technique is evaluated in two applications: adaptive cruise control, where the driver behavioral model is used to predict the leading vehicle dynamics, and series hybrid electric vehicle (SHEV) energy management, where the driver model is used to predict the future power requests

Provisioning of data to a vehicle infotainment computing system

Abstract: Various embodiments include a software provisioning system and method for a vehicle infotainment computer. Software provisioning of the vehicle infotainment computer may occur during vehicle assembly. A software provisioning request may be received for custom installing software to the vehicle infotainment computer. The custom install may be based on a customization schedule which may include a location identifier (such as uniform resource identifiers or file paths) for locating the software. In response to the request, the software may be located on a provisioning server or a portable memory device based on the customization schedule. The software may be transmitted to memory of the vehicle infotainment computer and custom installed on the vehicle infotainment computer.

Pedestrian alert system and method

Abstract: A pedestrian warning or alert system and method are disclosed The warning system is mounted on an electric vehicle The warning system includes a front speaker system, a rear speaker system, a front pedestrian detector, a rear pedestrian detector, and an electronic control unit (ECU) electronically coupled to the front speaker system, the rear speaker system, the front pedestrian detector, and the rear pedestrian detector The ECU commands the front speaker system to emit a warning sound based on the front pedestrian detector detecting a pedestrian-shaped object and commands the rear speaker system to emit a warning sound based on the rear pedestrian detector detecting a pedestrian-shaped object The ECU is coupled to a microphone, the signal of which is used to estimate an ambient noise level The intensity and frequency of the warning sound commanded from the front and/or rear speaker system is based on the ambient noise level

Method and system for controlling fuel usage

Abstract: Methods and systems are provided for improving fuel usage while addressing knock by adjusting the use of spark retard and direct injection of a knock control fluid based on engine operating conditions and the composition of the injected fluid. One or more engine parameters, such as EGR, VCT, boost, throttle position, and CMCV, are coordinated with the direct injection to reduce torque and EGR transients.

Method And System For Charging An Auxilary Battery In A Plug-In Electric Vehicle

Abstract: A system and method is provided for charging an auxiliary battery in a plug-in electric vehicle with an onboard charging system. The auxiliary battery has a predetermined operating range of charge levels. The auxiliary battery is charged to an upper charge level using an external power source thereby reducing energy used from the onboard charging system to charge the auxiliary battery during a drive mode of the vehicle. The upper charge level may be substantially similar to an upper level of the predetermined operating range of charge levels or greater than the predetermined operating range of charge levels. The auxiliary battery may be allowed to discharge to a lower charge level, which may be substantially similar to a lower level of the predetermined operating range of charge levels or less than the predetermined operating range of charge levels.

Comparing time histories for validation of simulation models : Error measures and metrics

Abstract: Computer modeling and simulation are the cornerstones of product design and development in the automotive industry. Computer-aided engineering tools have improved to the extent that virtual testing may lead to significant reduction in prototype building and testing of vehicle designs. In order to make this a reality, we need to assess our confidence in the predictive capabilities of simulation models. As a first step in this direction, this paper deals with developing measures and a metric to compare time histories obtained from simulation model outputs and experimental tests. The focus of the work is on vehicle safety applications. We restrict attention to quantifying discrepancy between time histories as the latter constitute the predominant form of responses of interest in vehicle safety considerations. First, we evaluate popular measures used to quantify discrepancy between time histories in fields such as statistics, computational mechanics, signal processing, and data mining. Three independent error measures are proposed for vehicle safety applications, associated with three physically meaningful characteristics (phase, magnitude, and slope), which utilize norms, cross-correlation measures, and algorithms such as dynamic time warping to quantify discrepancies. A combined use of these three measures can serve as a metric that encapsulates the important aspects of time history comparison. It is also shown how these measures can be used in conjunction with ratings from subject matter experts to build regression-based validation metrics.