An averaging battery model for a lead-acid battery operating in an electric car

A battery model is developed based on time averaging the current or power, and is shown to be an effective means of predicting the performance of a lead acid battery. The effectiveness of this battery model was tested on battery discharge profiles expected during the operation of an electric vehicle following the various SAE J227a driving schedules. The averaging model predicts the performance of a battery that is periodically charged (regenerated) if the regeneration energy is assumed to be converted to retrievable electrochemical energy on a one-to-one basis

Nuclear power systems for lunar and Mars exploration

Initial studies of a variety of mission scenarios for the new Space Exploration Initiative, and the technologies necessary to enable or significantly enhance them, have identified the development of advanced space power systems whether solar, chemical or nuclear to be of prime importance. Lightweight, compact, reliable power systems for planetary rovers and a variety of surface vehicles, utility surface power, and power for advanced propulsion systems have been identified as critical needs for these missions. These mission scenarios, the concomitant power system requirements, and power system options considered are discussed. The significant potential benefits of nuclear power are identified for meeting the power needs of the above applications

Characterization, performance, and prediction of a lead-acid battery under simulated electric vehicle driving requirements

A state-of-the-art 6-V battery module in current use by the electric vehicle industry was tested at the NASA Lewis Research Center to determine its performance characteristics under the SAE J227a driving schedules B, C, and D. The primary objective of the tests was to determine the effects of periods of recuperation and long and short periods of electrical regeneration in improving the performance of the battery module and hence extendng the vehicle range. A secondary objective was to formulate a computer program that would predict the performance of this battery module for the above driving schedules. The results show excellent correlation between the laboratory tests and predicted results. The predicted performance compared with laboratory tests was within +2.4 to -3.7 percent for the D schedule, +0.5 to -7.1 percent for the C schedule, and better than -11.4 percent for the B schedule

Transport theory with self-consistent confinement related to the lattice data

The space-time development of a quark-gluon plasma is calculated from a Vlasov equation for the distribution function of quasiparticles with medium dependent masses. At each space-time point the masses are calculated selfconsistently from a gap equation, whose form is determined by the requirement that in thermal equilibrium and for a range of temperatures the energy density of the quasi-particle system is identical to the one from lattice calculations . The numerical solutions of the Vlasov equation display confinement. Relations to effective theories like that by Friedberg Lee and Nambu Jona-Lasinio are established.Comment: 9 pages, 12 figure

Baseline tests of the EVA contractor electric passenger vehicle

The EVA Contactor four door sedan, an electric passenger vehicle, was tested to characterize the state-of-the-art of electric vehicles. It is a four passenger sedan that was converted to an electric vehicle. It is powered by 16 series connected 6 volt electric vehicle batteries through a four step contactor controller actuated by a foot accelerator pedal. The controller changes the voltage applied to the separately excited DC motor. The braking system is a vacuum assisted hydraulic braking system. Regenerative braking was also provided

Towards dense object tracking in a 2D honeybee hive

From human crowds to cells in tissue, the detection and efficient tracking of multiple objects in dense configurations is an important and unsolved problem. In the past, limitations of image analysis have restricted studies of dense groups to tracking a single or subset of marked individuals, or to coarse-grained group-level dynamics, all of which yield incomplete information. Here, we combine convolutional neural networks (CNNs) with the model environment of a honeybee hive to automatically recognize all individuals in a dense group from raw image data. We create new, adapted individual labeling and use the segmentation architecture U-Net with a loss function dependent on both object identity and orientation. We additionally exploit temporal regularities of the video recording in a recurrent manner and achieve near human-level performance while reducing the network size by 94% compared to the original U-Net architecture. Given our novel application of CNNs, we generate extensive problem-specific image data in which labeled examples are produced through a custom interface with Amazon Mechanical Turk. This dataset contains over 375,000 labeled bee instances across 720 video frames at 2 FPS, representing an extensive resource for the development and testing of tracking methods. We correctly detect 96% of individuals with a location error of ~7% of a typical body dimension, and orientation error of 12 degrees, approximating the variability of human raters. Our results provide an important step towards efficient image-based dense object tracking by allowing for the accurate determination of object location and orientation across time-series image data efficiently within one network architecture.Comment: 15 pages, including supplementary figures. 1 supplemental movie available as an ancillary fil

Baseline tests of the power-train electric delivery van

Vehicle maximum speed, range at constant speed, range over stop-and-go driving schedules, maximum acceleration, gradeability, gradeability limit, road energy consumption, road power, indicated energy consumption, braking capability, battery charger efficiency, and battery characteristics were determined for a modified utility van powered by sixteen 6-volt batteries connected in series. A chopper controller actuated by a foot accelerator pedal changes the voltage applied to the 22-kilowatt (30-hp) series-wound drive motor. In addition to the conventional hydraulic braking system, the vehicle has hydraulic regenerative braking. Cycle tests and acceleration tests were conducted with and without hydraulic regeneration

An electric vehicle propulsion system's impact on battery performance: An overview

The performance of two types of batteries, lead-acid and nickel-zinc, was measured as a function of the charging and discharging demands anticipated from electric vehicle propulsion systems. The benefits of rapid high current charging were mixed: although it allowed quick charges, the energy efficiency was reduced. For low power (overnight) charging the current wave shapes delivered by the charger to the battery tended to have no effect on the battery cycle life. The use of chopper speed controllers with series traction motors resulted in a significant reduction in the energy available from a battery whenever the motor operates at part load. The demand placed on a battery by an electric vehicle propulsion system containing electrical regenerative braking confirmed significant improvment in short term performance of the battery

Baseline tests of the EVA change-of-pace coupe electric passenger vehicle

The EVA Change-of-Pace Coupe, is an electric passenger vehicle, to characterize the state-of-the-art of electric vehicles. The EVA Change-of-Pace Coupe is a four passenger sedan that has been coverted to an electric vehicle. It is powered by twenty 6 volt traction batteries through a silicon controlled rectifier chopper controller actuated by a foot throttle to change the voltage applied to the series wound, direct current motor. Braking is accomplished with a vacuum assist hydraulic braking system. Regenerative braking is also provided