Abstract
Dynamic processes are often influenced by external conditions. We expand the neural network approximation capability to behavior modeling within an original hierarchical master-slave relation. Unlike the control theory paradigm, neural weights will replace “state variables” that may be impossible to measure. An application aiming at predicting the end of discharge for rechargeable batteries is fully described. This new battery management tool leads to accurate predictions (mean error is about 3 %) and its implementation into a portable equipment demonstrates that neural networks could be useful even for small size products. The system is further improved by on-line adaptation to actual conditions and individual behavior. This improvement reduces the error prediction to a low 1.5 %.
Preview
Unable to display preview. Download preview PDF.
References
O. Gérard and J. N. Patillon. Une procédure d'optimisation des pentes dans un réseau de neurones. VALGO, 96(1), 1996.
O. Gérard, J.N. Patillon, and F. d'Alché Buc. Discharge prediction of rechargeable batteries with neural network. International Journal of Integrated Computer-Aided Engineering, 1997. to appear.
K. Hornik, M. Stinchcombe, and H. White. Multilayer feedforward networks are universal estimators. Neural Networks, 2:359–366, 1989.
A. Lapedes and Farber. Programming a massively parallel, computation universal system: Static behavior. In J.S. Denker, editor, Snowbird 1986, pages 283–298. American Institute of Physics, New York, 1986.
D. Linden, editor. Handbook of batteries. McGraw-Hill, NY, 1995.
J.-N. Patillon, O. Gérard, F. d'Alché Buc, S. Gourrier, and J.-P. NadaL Smart battery management. 1995 LEP Annual Review, pages 52–54, 1996.
N. Pican. Intrinsic and parallel performances of the OWE neural network architecture. In ICANN'96, pages 755–760, 1996.
Author information
Authors and Affiliations
Editor information
Rights and permissions
Copyright information
© 1997 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Gérard, O., Patillon, JN., d'Alché-Buc, F. (1997). Neural network adaptive modeling of battery discharge behavior. In: Gerstner, W., Germond, A., Hasler, M., Nicoud, JD. (eds) Artificial Neural Networks — ICANN'97. ICANN 1997. Lecture Notes in Computer Science, vol 1327. Springer, Berlin, Heidelberg. https://doi.org/10.1007/BFb0020299
Download citation
DOI: https://doi.org/10.1007/BFb0020299
Published:
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-63631-1
Online ISBN: 978-3-540-69620-9
eBook Packages: Springer Book Archive