Battery modeling for electric vehicle applications using neural networks

PDF Version Also Available for Download.

Description

Neural networking is a new approach to modeling batteries for electric vehicle applications. This modeling technique is much less complex then a first principles model but can consider more parameters then classic empirical modeling. Test data indicates that individual cell size and geometry and operating conditions affect a battery performance (energy density, power density and life). Given sufficient battery data, system parameters and operating conditions a neural network model could be used to interpolate and perhaps even extrapolate battery performance under wide variety of operating conditions. As a result the method could be a valuable design tool for electric vehicle ... continued below

Physical Description

9 p.

Creation Information

Swan, D.H.; Arikara, M.P. & Patton, A.D. December 31, 1993.

Context

This article is part of the collection entitled: Office of Scientific & Technical Information Technical Reports and was provided by UNT Libraries Government Documents Department to Digital Library, a digital repository hosted by the UNT Libraries. More information about this article can be viewed below.

Who

People and organizations associated with either the creation of this article or its content.

Sponsor

Provided By

UNT Libraries Government Documents Department

Serving as both a federal and a state depository library, the UNT Libraries Government Documents Department maintains millions of items in a variety of formats. The department is a member of the FDLP Content Partnerships Program and an Affiliated Archive of the National Archives.

Contact Us

What

Descriptive information to help identify this article. Follow the links below to find similar items on the Digital Library.

Description

Neural networking is a new approach to modeling batteries for electric vehicle applications. This modeling technique is much less complex then a first principles model but can consider more parameters then classic empirical modeling. Test data indicates that individual cell size and geometry and operating conditions affect a battery performance (energy density, power density and life). Given sufficient battery data, system parameters and operating conditions a neural network model could be used to interpolate and perhaps even extrapolate battery performance under wide variety of operating conditions. As a result the method could be a valuable design tool for electric vehicle battery design and application. This paper describes the on going modeling method at Texas A and M University and presents preliminary results of a tubular lead acid battery model. The ultimate goal of this modeling effort is to develop the values necessary to be able to predict performance for batteries as wide ranging as sodium sulfur to zinc bromine.

Physical Description

9 p.

Notes

OSTI as DE97006606

Source

  • Electric Vehicle international congress and exposition, Detroit, MI (United States), Mar 1993

Language

Item Type

Identifier

Unique identifying numbers for this article in the Digital Library or other systems.

  • Other: DE97006606
  • Report No.: CONF-9303349--1
  • Office of Scientific & Technical Information Report Number: 495752
  • Archival Resource Key: ark:/67531/metadc687542

Collections

This article is part of the following collection of related materials.

Office of Scientific & Technical Information Technical Reports

Reports, articles and other documents harvested from the Office of Scientific and Technical Information.

Office of Scientific and Technical Information (OSTI) is the Department of Energy (DOE) office that collects, preserves, and disseminates DOE-sponsored research and development (R&D) results that are the outcomes of R&D projects or other funded activities at DOE labs and facilities nationwide and grantees at universities and other institutions.

What responsibilities do I have when using this article?

When

Dates and time periods associated with this article.

Creation Date

  • December 31, 1993

Added to The UNT Digital Library

  • July 25, 2015, 2:21 a.m.

Description Last Updated

  • Nov. 12, 2015, 6:20 p.m.

Usage Statistics

When was this article last used?

Yesterday: 0
Past 30 days: 2
Total Uses: 7

Interact With This Article

Here are some suggestions for what to do next.

Start Reading

PDF Version Also Available for Download.

Citations, Rights, Re-Use

Swan, D.H.; Arikara, M.P. & Patton, A.D. Battery modeling for electric vehicle applications using neural networks, article, December 31, 1993; United States. (digital.library.unt.edu/ark:/67531/metadc687542/: accessed November 22, 2017), University of North Texas Libraries, Digital Library, digital.library.unt.edu; crediting UNT Libraries Government Documents Department.