An improved model based on artificial neural networks and Thevenin model for nickel metal hydride power battery

Author

Piao, Changhao ; Yang, Xiaoyong ; Teng, Cong ; Yang, HuiQian

Author_Institution

Minist. of Educ. Key Lab. of Network Control Tech. & Intell. Instrum., Chongqing Univ. of Posts & Commun., Chongqing, China

Volume

1

fYear

2010

fDate

10-11 May 2010

Firstpage

115

Lastpage

118

Abstract

Based on artificial neural networks and Thenvenin model, this paper uses an improved model predicting state of charge. We combine artificial neural networks model with Thevenin model, and predict state of charge in real time at the same time. When the difference between the predictive value of artificial neural networks model and the predictive value of Thevenin model is more than 10%, we revised the predictive value of artificial neural networks model by weighted average value. The results show that it can reduce the error of artificial neural networks model obvious and the average error is 4.72%. It is lower independence on initial state of charge than artificial neural networks model.

Keywords

Artificial intelligence; Artificial neural networks; Batteries; Hybrid electric vehicles; Input variables; Neural networks; Neurons; Nickel; Optical computing; Predictive models; Thevenin model; artificial neural networks; nickel metal hydride power battery; state of charge;

fLanguage

English

Publisher

ieee

Conference_Titel

Optics Photonics and Energy Engineering (OPEE), 2010 International Conference on

Conference_Location

Wuhan, China

Print_ISBN

978-1-4244-5234-7

Electronic_ISBN

978-1-4244-5236-1

Type

conf

DOI

10.1109/OPEE.2010.5508184

Filename

5508184