Artificial neural network simulation of battery performance

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Although they appear deceptively simple, batteries embody a complex set of interacting physical and chemical processes. While the discrete engineering characteristics of a battery such as the physical dimensions of the individual components, are relatively straightforward to define explicitly, their myriad chemical and physical processes, including interactions, are much more difficult to accurately represent. Within this category are the diffusive and solubility characteristics of individual species, reaction kinetics and mechanisms of primary chemical species as well as intermediates, and growth and morphology characteristics of reaction products as influenced by environmental and operational use profiles. For this reason, development of analytical ... continued below

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10 p.

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O`Gorman, C.C.; Ingersoll, D.; Jungst, R.G. & Paez, T.L. December 31, 1998.

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  • Sandia National Laboratories
    Publisher Info: Sandia National Labs., Albuquerque, NM (United States)
    Place of Publication: Albuquerque, New Mexico

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Description

Although they appear deceptively simple, batteries embody a complex set of interacting physical and chemical processes. While the discrete engineering characteristics of a battery such as the physical dimensions of the individual components, are relatively straightforward to define explicitly, their myriad chemical and physical processes, including interactions, are much more difficult to accurately represent. Within this category are the diffusive and solubility characteristics of individual species, reaction kinetics and mechanisms of primary chemical species as well as intermediates, and growth and morphology characteristics of reaction products as influenced by environmental and operational use profiles. For this reason, development of analytical models that can consistently predict the performance of a battery has only been partially successful, even though significant resources have been applied to this problem. As an alternative approach, the authors have begun development of a non-phenomenological model for battery systems based on artificial neural networks. Both recurrent and non-recurrent forms of these networks have been successfully used to develop accurate representations of battery behavior. The connectionist normalized linear spline (CMLS) network has been implemented with a self-organizing layer to model a battery system with the generalized radial basis function net. Concurrently, efforts are under way to use the feedforward back propagation network to map the {open_quotes}state{close_quotes} of a battery system. Because of the complexity of battery systems, accurate representation of the input and output parameters has proven to be very important. This paper describes these initial feasibility studies as well as the current models and makes comparisons between predicted and actual performance.

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10 p.

Notes

OSTI as DE98000233

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  • Hawaii international conference on system sciences (HICSS 31), Kohala Coast, HI (United States), 6-9 Jan 1998

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  • Other: DE98000233
  • Report No.: SAND--97-2318C
  • Report No.: CONF-980113--
  • Grant Number: AC04-94AL85000
  • Office of Scientific & Technical Information Report Number: 563809
  • Archival Resource Key: ark:/67531/metadc697792

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

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  • December 31, 1998

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  • Aug. 14, 2015, 8:43 a.m.

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  • April 14, 2016, 3:13 p.m.

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O`Gorman, C.C.; Ingersoll, D.; Jungst, R.G. & Paez, T.L. Artificial neural network simulation of battery performance, article, December 31, 1998; Albuquerque, New Mexico. (digital.library.unt.edu/ark:/67531/metadc697792/: accessed December 17, 2017), University of North Texas Libraries, Digital Library, digital.library.unt.edu; crediting UNT Libraries Government Documents Department.