DocumentCode :
1985640
Title :
The ANN models for SOC/BRC estimation of Li-ion battery
Author :
Shi, Pu ; Bu, Chunguang ; Zhao, Yiwen
Author_Institution :
Shenyang Inst. of Autom., Chinese Acad. of Sci., Shenyang, China
fYear :
2005
fDate :
27 June-3 July 2005
Abstract :
Lithium-ion battery is a kind of advanced sources and is a quite complex and nonlinear system comprised of interacting physical and chemical processes. Its state-of-charge (SOC)/ battery residual capacity (BRC), which is parameters to describe how much energy battery has, is key factors in applications; its estimations is an important and challenging task. To achieve this goal, the traditional and ANN system will be presented. Firstly, the paper defines the concepts of SOC/BRC. Secondly, the paper compares the traditional approaches with ANN dynamic techniques used to estimate the remaining battery capacity of lithium-ion battery and describes the latter in detail. Finally, a conclusion is given.
Keywords :
lithium; neural nets; power engineering computing; secondary cells; ANN model; SOC-BRC estimation; battery residual capacity; chemical process; lithium-ion battery; nonlinear system; physical process; state-of-charge; Artificial neural networks; Automation; Batteries; Chemical processes; Electrodes; IEEE news; Lithium; Nonlinear systems; State estimation; Voltage;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Information Acquisition, 2005 IEEE International Conference on
Print_ISBN :
0-7803-9303-1
Type :
conf
DOI :
10.1109/ICIA.2005.1635151
Filename :
1635151
Link To Document :
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