Modeling and prediction for discharge lifetime of battery systems using hybrid evolutionary algorithms-Reference-Cited by-同舟云学术

Modeling and prediction for discharge lifetime of battery systems using hybrid evolutionary algorithms

Published:2001-05 Issue:3 Volume:25 Page:251-259
ISSN:0097-8485
Container-title:Computers & Chemistry
language:en
Short-container-title:Computers & Chemistry

Author:

Cao Hongqing,Yu Jingxian,Kang Lishan,Yang Hanxi,Ai Xinping

Publisher

Elsevier BV

Subject

General Chemical Engineering,Applied Microbiology and Biotechnology,Biotechnology

Reference19 articles.

1. Banzhaf, W., Nordin, P., Keller, R.E., Francone, F.D., 1997. Genetic Programming: An Introduction on the Automatic Evolution of Computer Programs and its Applications. Morgan Kaufmann.

2. Lead-Acid Batteries;Bode,1977

3. Evolutionary Computation;Eiben,1999

4. Gan, R.-C., 1991. The Statistical Analysis of Dynamic Data. Beijing University of Science and Technology Press, Beijing (in Chinese).

5. He, J.-X., 1995. System Modelling and Mathematical Models. Fujian Science & Technology Press, Xiamen (in Chinese).

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