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Battery modeling for electric vehicle applications using neural networks [electronic resource].

Published
Washington, D.C. : United States. Dept. of Energy, 1993.
Oak Ridge, Tenn. : Distributed by the Office of Scientific and Technical Information, U.S. Dept. of Energy.
Physical Description
9 pages : digital, PDF file
Additional Creators
Texas Engineering Experiment Station, United States. Department of Energy, and United States. Department of Energy. Office of Scientific and Technical Information
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Summary
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.
Report Numbers
E 1.99:conf-9303349--1
conf-9303349--1
Subject(s)
Other Subject(s)
Note
Published through SciTech Connect.
12/31/1993.
"conf-9303349--1"
"DE97006606"
"NONE"
Electric Vehicle international congress and exposition, Detroit, MI (United States), Mar 1993.
Patton, A.D.; Swan, D.H.; Arikara, M.P.
View MARC record | catkey: 13600413