Estimation of the residual capacity of sealed lead-acid batteries by neural network

Author

Yamazaki, Tsutomu ; Sakurai, Kimio ; Muramoto, K.

Author_Institution

Grad. Sch. of Natural Sci. & Technol., Kanazawa Univ.

fYear

1998

fDate

1998

Firstpage

210

Lastpage

214

Abstract

This paper presents a method for estimating the remaining capacity of sealed type lead-acid batteries. The approach can be divided into three parts, first a survey on battery properties over a long period of time was conducted. This data was used in the second phase to train a feedforward neural network. Finally, the third phase tested the accuracy of prediction of this network using real data. It was found that using this method, a maximum error of prediction of 10% and an average mean error of 3% could be obtained

Keywords

feedforward neural nets; lead acid batteries; neural nets; power engineering computing; Pb; Pb-acid sealed batteries; battery properties; current measurement; feedforward neural network; impedance measurement; neural network training; remaining capacity estimation; residual capacity estimation; sealed lead-acid batteries; temperature measurement; voltage measurement; Battery charge measurement; Computational modeling; Computer simulation; Current measurement; Equations; Impedance measurement; Multiplexing; Neural networks; Temperature; Testing;

fLanguage

English

Publisher

ieee

Conference_Titel

Telecommunications Energy Conference, 1998. INTELEC. Twentieth International

Conference_Location

San Francisco, CA

Print_ISBN

0-7803-5069-3

Type

conf

DOI

10.1109/INTLEC.1998.793500

Filename

793500