Modeling Ni-MH battery based on immune evolutionary network

Author

Bo, Cheng ; Min, Ye ; Yanlu, Zhou ; Junping, Wang ; Binggang, Cao

Author_Institution

Sch. of Constr. Machinery, Chang´´ an Univ., Xi´´an, China

fYear

2010

Firstpage

4578

Lastpage

4583

Abstract

In order to overcome the defect of conventional neural networks, computational algorithm is used to train RBF network to model the Ni-MH battery. First, RBF network centre is identified by the artificial immune data clustering method. A new immune algorithm, adaptive parallel immune evolutionary strategy, PIES is used to train RBF network and RBF neural network training steps are designed. Finally, under the state of constant current discharging and FUDS discharging, validity of the battery model is verified within an error of 0.3V.

Keywords

artificial immune systems; evolutionary computation; neural nets; nickel; radial basis function networks; secondary cells; Ni; Ni-MH battery; NiJkH; PIES; RBF network; adaptive parallel immune evolutionary strategy; artificial immune data clustering; computational algorithm; constant current discharging; neural networks; Adaptation model; Artificial neural networks; Batteries; Clustering algorithms; Computational modeling; Radial basis function networks; System-on-a-chip; battery model; electric vehicle; immune clustering; neural network;

fLanguage

English

Publisher

ieee

Conference_Titel

Intelligent Control and Automation (WCICA), 2010 8th World Congress on

Conference_Location

Jinan

Print_ISBN

978-1-4244-6712-9

Type

conf

DOI

10.1109/WCICA.2010.5554121

Filename

5554121