Estimating the State of Charge for Ni-MH Battery in HEV by RBF Neural Network

Author

Guo Hongyu ; Jiang Jiuchun ; Wang Zhanguo

Author_Institution

Sch. of Electr. Eng., Beijing Jiaotong Univ., Beijing

fYear

2009

fDate

23-24 May 2009

Firstpage

1

Lastpage

4

Abstract

Forecasting of the state of charge (SOC) of Ni-MH battery is the most important task for battery management system of hybrid electric vehicle (HEV). On the basis of analyzing charging and discharging characteristics of Ni-MH battery and using the advantages of radial basis function (RBF) neural network, model for estimating the state of charge for Ni-MH battery was established with the piecewise modeling idea. The model was tested with data which was from battery experiments. Results show that the operation speed and estimation accuracy of forecasting model can meet the demands in practice and the model has certain value of application.

Keywords

hybrid electric vehicles; nickel; radial basis function networks; secondary cells; Ni-MH battery; RBF neural network; hybrid electric vehicle; radial basis function; Battery management systems; Demand forecasting; Engines; Hybrid electric vehicles; Neural networks; Predictive models; State estimation; System testing; Temperature; Voltage;

fLanguage

English

Publisher

ieee

Conference_Titel

Intelligent Systems and Applications, 2009. ISA 2009. International Workshop on

Conference_Location

Wuhan

Print_ISBN

978-1-4244-3893-8

Electronic_ISBN

978-1-4244-3894-5

Type

conf

DOI

10.1109/IWISA.2009.5072852

Filename

5072852