Ford Motor Company

About: Ford Motor Company is a company organization based out in Dearborn, Michigan, United States. It is known for research contribution in the topics: Internal combustion engine & Clutch. The organization has 36123 authors who have published 51450 publications receiving 855200 citations. The organization is also known as: Ford Motor & Ford Motor Corporation.

Emissivity of Dispersed Carbon Particles

Abstract: The emissivity of spherical carbon particles is calculated in both the infrared and visible regions of the spectrum. The scattering and absorption cross sections for individual particles are obtained from the Mie theory of scattering. A suitable dispersion equation is derived which represents the optical properties of carbon at flame temperatures. An expression is obtained for the radiation intensity emitted by a large number of dispersed particles which includes all higher order scattering processes. From these results the emissivity of carbon particles in flames is calculated for particle radii in the range from 50 to 800 A and for 109 to 1015 particles cm−2. In addition the emissivity is obtained for several different particle size distributions which are representative of actual flames. A quantitative explanation is given for the occurrence of the intensity maximum at shorter wavelengths than corresponds to the blackbody maximum at the same temperature.

Intelligent Hybrid Vehicle Power Control—Part I: Machine Learning of Optimal Vehicle Power

Abstract: In this series of two papers, we present our research on intelligent energy management for hybrid electric vehicles (HEVs). These two papers cover the modeling of power flow in HEVs, the mathematical background of optimization in energy management in HEVs, a machine learning framework that combines dynamic programming (DP) with machine learning to learn about roadway-type- and traffic-congestion-level-specific energy optimization, machine learning algorithms, and real-time quasi-optimal control of energy flow in an HEV. This first paper presents our research on machine learning for optimal energy management in HEVs. We will present a machine learning framework ML_EMO_HEV developed for the optimization of energy management in an HEV, machine learning algorithms for predicting driving environments, and the generation of an optimal power split for a given driving environment. Experiments are conducted based on a simulated Ford Escape Hybrid vehicle model provided by Argonne National Laboratory's Powertrain Systems Analysis Toolkit (PSAT). Based on the experimental results on the test data, we can conclude that the neural networks trained under the ML_EMO_HEV framework are effective in predicting roadway type and traffic congestion levels, predicting driving trends, and learning optimal engine speed and optimal battery power from DP.

Fourier transform infrared kinetic studies of the reaction of HONO with HNO3, NO3 and N2O5 at 295 K

Abstract: The kinetics of the reaction of nitrous acid (HONO) with nitric acid (HNO3), nitrate radicals (NO3) and dinitrogen pentoxide (N2O5) have been studied using Fourier transform infrared spectroscopy. Experiments were performed at 700 torr total pressure using synthetic air or argon as diluents. From the observed decay of HONO in the presence of HNO3 a rate constant of k<7×10-19 cm3 molecule-1 s-1 was derived for the reaction of HONO with HNO3. From the observed decay of HONO in the presence of mixtures of N2O5 and NO2 we have also derived upper limits for the rate constants of the reactions of HONO with NO3 and N2O5 of 2×10-15 and 7×10-19 cm3 molecule-1 s-1, respectively. These results are discussed with respect to previous studies and to the atmospheric chemistry of HONO.

Innovative Ultra-low NOx Controlled Auto-Ignition Combustion Process for Gasoline Engines: the 4-SPACE Project

Abstract: The purpose of the 4-SPACE (4-Stroke Powered gasoline Auto-ignition Controlled combustion Engine) industrial research project is to research and develop an innovative controlled auto-ignition combustion process for lean burn automotive gasoline 4-stroke engines application. The engine concepts to be developed could have the potential to replace the existing stoichiometric / 3-way catalyst automotive spark ignition 4-stroke engines by offering the potential to meet the most stringent EURO 4 emissions limits in the year 2005 without requiring DeNOx catalyst technology. A reduction of fuel consumption and therefore of corresponding CO2 emissions of 15 to 20% in average urban conditions of use, is expected for the « 4-SPACE » lean burn 4-stroke engine with additional reduction of CO emissions. This paper describes the first set of results of different experimental and numerical studies aiming to get such new combustion process in 4-stroke engines within the framework of this European consortium. One of the target of this consortium driven by IFP, is to develop a 4-stroke gasoline engine running conventionally at high load (with a normal compression ratio and without any intake air heating) and able to achieve Controlled Auto-Ignition (CAI) process at part load by reproducing the 2-stroke internal conditions (internal EGR rate and fluid dynamic control, temperature level...) favorable to this particular combustion process. For this purpose and as a starting point of the work program, a production 2-stroke engine known for its part load auto-ignition behavior is fully studied. Such work is focused on the analysis of in-cylinder conditions prior to auto-ignition using combined experimental testing, 3D CFD computations and optical diagnostics. From this analysis, 1D CFD computations have been extensively performed to evaluate the possible 4-stroke concepts able to reproduce internal conditions favorable to CAI. Then, the most “promising” configurations have been experimentally investigated. Encouraging preliminary results have already shown that NOx emissions are reduced by 10 to 40 times and the fuel economy is improved by 8 to 10% when compared with stoichiometric reference conditions. Other ways of getting auto-ignition of the lean fresh mixture are also explored by the project partners. The effects of several parameters, such as the fuel composition, the engine compression ratio, the intake air temperature level, etc... are also included in the research program. Thus, to analyze better analyze intrinsic autoignition process, specific tools as for example Rapid Compression Machine have been developed. Different fuels at various initial conditions (e.g. temperature, excess air) have been tested and compared, for example in terms for example of combustion rate and auto-ignition delay. Results obtained contribute to the better understanding of the auto-ignition process. Preliminary visualization results from specially designed single cylinder engines (2-stroke and 4-stroke) have been obtained for controlled auto-ignition combustion. The effect of charge stratification is briefly discussed.

Warning apparatus for a motor vehicle

Abstract: Warning apparatus (2) for a motor vehicle (1) having an indicator system (6) for indicating a change of lane comprising at least one signal display (7) arranged in the field of view of the driver and having a sensor system for detecting obstacles in the blind spot (4), wherein the sensor system generates a warning signal in the event of detection of an obstacle, wherein the signal display (7) of the indicator system (6) is connected to the sensor system such that the warning signal is reproduced in visual form via the signal display (7).