Neural Network Modeling of the Lithium/Thionyl Chloride Battery System

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Battery systems have traditionally relied on extensive build and test procedures for product realization. Analytical models have been developed to diminish this reliance, but have only been partially successful in consistently predicting the performance of battery systems. The complex set of interacting physical and chemical processes within battery systems has made the development of analytical models a significant challenge. Advanced simulation tools are needed to more accurately model battery systems which will reduce the time and cost required for product realization. Sandia has initiated an advanced model-based design strategy to battery systems, beginning with the performance of lithiumhhionyl chloride cells. ... continued below

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Ingersoll, D.; Jungst, R.G.; O'Gorman, C.C. & Paez, T.L. October 29, 1998.

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  • Sandia National Laboratories
    Publisher Info: Sandia National Laboratories, Albuquerque, NM, and Livermore, CA
    Place of Publication: Albuquerque, New Mexico

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Description

Battery systems have traditionally relied on extensive build and test procedures for product realization. Analytical models have been developed to diminish this reliance, but have only been partially successful in consistently predicting the performance of battery systems. The complex set of interacting physical and chemical processes within battery systems has made the development of analytical models a significant challenge. Advanced simulation tools are needed to more accurately model battery systems which will reduce the time and cost required for product realization. Sandia has initiated an advanced model-based design strategy to battery systems, beginning with the performance of lithiumhhionyl chloride cells. As an alternative approach, we have begun development of cell performance modeling using non-phenomenological models for battery systems based on artificial neural networks (ANNs). ANNs are inductive models for simulating input/output mappings with certain advantages over phenomenological models, particularly for complex systems. Among these advantages is the ability to avoid making measurements of hard to determine physical parameters or having to understand cell processes sufficiently to write mathematical functions describing their behavior. For example, ANN models are also being studied for simulating complex physical processes within the Li/SOC12 cell, such as the time and temperature dependence of the anode interracial resistance. ANNs have been shown to provide a very robust and computationally efficient simulation tool for predicting voltage and capacity output for Li/SOC12 cells under a variety of operating conditions. The ANN modeling approach should be applicable to a wide variety of battery chemistries, including rechargeable systems.

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  • 194th Meeting of the Electrochemical Society, Inc.; Boston, MA; 11/01-06/1998

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  • Other: DE00001536
  • Report No.: SAND98-2443C
  • Grant Number: AC04-94AL85000
  • Office of Scientific & Technical Information Report Number: 1536
  • Archival Resource Key: ark:/67531/metadc622082

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Office of Scientific & Technical Information Technical Reports

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

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  • October 29, 1998

Added to The UNT Digital Library

  • June 16, 2015, 7:43 a.m.

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  • Dec. 7, 2016, 6:12 p.m.

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Ingersoll, D.; Jungst, R.G.; O'Gorman, C.C. & Paez, T.L. Neural Network Modeling of the Lithium/Thionyl Chloride Battery System, article, October 29, 1998; Albuquerque, New Mexico. (digital.library.unt.edu/ark:/67531/metadc622082/: accessed October 21, 2017), University of North Texas Libraries, Digital Library, digital.library.unt.edu; crediting UNT Libraries Government Documents Department.