The prediction of SOC of lithium batteries and varied pulse charge

Author

He, Wei ; Huang, Dagui ; Feng, Daiwei

Author_Institution

Sch. of Mechatron. Eng., Univ. of Electron. Sci. & Technol. of China, Chengdu, China

fYear

2009

fDate

9-12 Aug. 2009

Firstpage

1578

Lastpage

1582

Abstract

Improved RBF neural network arithmetic is mainly characterized by using TI´s Impedance Track TM technology for reference which predict the status of charge (SOC) of lithium battery, and in accordance with the chemistry characteristics of lithium batteries, use varied pulse charge method for their rapid and efficient charging. The results show that the SOC which is predicted by improved RBF arithmetic can meet the performance target under the C++ compiler environment and adopting the varied pulse charge have shorten the charging time by 20%, this method is suitable for rapid charging system.

Keywords

C++ language; battery charge measurement; power engineering computing; primary cells; radial basis function networks; C++ compiler; RBF neural network arithmetic; SOC prediction; TI impedance track TM technology; lithium batteries; radial basis function network; status of charge; varied pulse charge; Aging; Arithmetic; Battery charge measurement; Chemical technology; Electric variables measurement; Impedance; Lithium; Runtime; Temperature; Voltage; Lithium batteries; Radial Basis Function Neural Network; The prediction of SOC; Varied pulse charge;

fLanguage

English

Publisher

ieee

Conference_Titel

Mechatronics and Automation, 2009. ICMA 2009. International Conference on

Conference_Location

Changchun

Print_ISBN

978-1-4244-2692-8

Electronic_ISBN

978-1-4244-2693-5

Type

conf

DOI

10.1109/ICMA.2009.5246426

Filename

5246426