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Hybrid Neural Net Model of a Lithium Ion Battery

[+] Author Affiliations
Rehan Refai, Dongmei Chen, Benito Fernandez-Rodriguez

The University of Texas at Austin, Austin, TX

Sandeep Yayathi

National Aeronautics and Space Administration, Houston, TX

Paper No. DSCC2011-6043, pp. 239-246; 8 pages
  • ASME 2011 Dynamic Systems and Control Conference and Bath/ASME Symposium on Fluid Power and Motion Control
  • ASME 2011 Dynamic Systems and Control Conference and Bath/ASME Symposium on Fluid Power and Motion Control, Volume 2
  • Arlington, Virginia, USA, October 31–November 2, 2011
  • Conference Sponsors: Dynamic Systems and Control Division
  • ISBN: 978-0-7918-5476-1
  • Copyright © 2011 by ASME


This paper discusses the development of a hybrid neural net and physics based battery model. A control oriented one dimensional physics based model of a battery is developed. A neural net based modeling approach is used to compensate for the lack of knowledge of material parameters for the battery cell. Given the knowledge of the physics of the battery, sparse recurrent neural nets are used. Multiple types of standalone neural nets as well as hybrid neural net and physics based battery models are developed and tested to determine the appropriate configuration for an optimal performance. All neural nets are trained as open-loop (feed-forward) systems and are tested as recurrent systems, with the battery state of charge being fed back. The neural nets are trained, tested and validated using test data from a 4.4Ah Boston Power lithium ion battery cell. The modeling approach presented in this paper is able to accurately simulate battery performance for multiple current profiles.

Copyright © 2011 by ASME



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