A combined method of battery SOC estimation for electric vehicles

Author

Cui, Naxin ; Zhang, Chenghui ; Kong, Qing ; Shi, Qingsheng

Author_Institution

Sch. of Control Sci. & Eng., Shandong Univ., Jinan, China

fYear

2010

fDate

15-17 June 2010

Firstpage

1147

Lastpage

1151

Abstract

Exact estimation of battery state of charge (SOC) is important for a monitoring system, which is the basis of a energy management system (EMS) in electric vehicles. This paper presents a combined method for estimating the battery SOC for electric vehicles. Diagonal recurrent neural network (DRNN) and Kalman filter (KF) were used to estimate battery SOC respectively. Then the two methods were combined to apply alternately. The combined method synthetized the advantages of the neural network and the Kalman filter, so the it can not only estimate SOC accurately, but also reduce computation amount.

Keywords

Kalman filters; battery management systems; battery powered vehicles; energy management systems; power engineering computing; recurrent neural nets; secondary cells; Kalman filter; battery SOC Estimation; battery state of charge estimation; diagonal recurrent neural network; electric vehicles; energy management system; monitoring system; Batteries; Computational modeling; Electric vehicles; Hybrid electric vehicles; Monitoring; Neural networks; Neurons; Recurrent neural networks; State estimation; Vehicle dynamics; Diagonal Recurrent Neural Network; Kalman Filter; battery; electric vehicle; state of charge estimation;

fLanguage

English

Publisher

ieee

Conference_Titel

Industrial Electronics and Applications (ICIEA), 2010 the 5th IEEE Conference on

Conference_Location

Taichung

Print_ISBN

978-1-4244-5045-9

Electronic_ISBN

978-1-4244-5046-6

Type

conf

DOI

10.1109/ICIEA.2010.5515866

Filename

5515866