Modeling of lead-acid battery capacity loss in a photovoltaic application Metadata

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Title

  • Main Title Modeling of lead-acid battery capacity loss in a photovoltaic application

Creator

  • Author: JUNGST,RUDOLPH G.
    Creator Type: Personal
  • Author: URBINA,ANGEL
    Creator Type: Personal
  • Author: PAEZ,THOMAS L.
    Creator Type: Personal

Contributor

  • Sponsor: United States. Department of Energy.
    Contributor Type: Organization
    Contributor Info: US Department of Energy (United States)

Publisher

  • Name: Sandia National Laboratories
    Place of Publication: Albuquerque, New Mexico
    Additional Info: Sandia National Labs., Albuquerque, NM, and Livermore, CA (United States)

Date

  • Creation: 2000-04-12

Language

  • English

Description

  • Content Description: The authors have developed a model for the probabilistic behavior of a rechargeable battery acting as the energy storage component in a photovoltaic power supply system. Stochastic and deterministic models are created to simulate the behavior of the system components. The components are the solar resource, the photovoltaic power supply system, the rechargeable battery, and a load. One focus of this research is to model battery state of charge and battery capacity as a function of time. The capacity damage effect that occurs during deep discharge is introduced via a non-positive function of duration and depth of deep discharge events. Because the form of this function is unknown and varies with battery type, the authors model it with an artificial neural network (ANN) whose parameters are to be trained with experimental data. The battery capacity loss model will be described and a numerical example will be presented showing the predicted battery life under different PV system use scenarios.
  • Physical Description: 4 p.

Subject

  • STI Subject Categories: 25 Energy Storage
  • Keyword: Mathematical Models
  • Keyword: Voltage Drop
  • Keyword: Battery Charging
  • Keyword: Service Life
  • Keyword: Photovoltaic Power Supplies
  • Keyword: Battery Charge State
  • Keyword: Neural Networks
  • Keyword: Lead-Acid Batteries
  • STI Subject Categories: 14 Solar Energy

Source

  • Conference: 39th Power Sources Conference, Cherry Hill, NJ (US), 06/12/2000--06/15/2000

Collection

  • Name: Office of Scientific & Technical Information Technical Reports
    Code: OSTI

Institution

  • Name: UNT Libraries Government Documents Department
    Code: UNTGD

Resource Type

  • Article

Format

  • Text

Identifier

  • Report No.: SAND2000-0900C
  • Grant Number: AC04-94AL85000
  • Office of Scientific & Technical Information Report Number: 755634
  • Archival Resource Key: ark:/67531/metadc707405

Note

  • Display Note: OSTI as DE00755634
  • Display Note: Medium: P; Size: 4 pages