State of charge estimation for a lead-acid battery using backpropagation neural network method

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

8 Citations (Scopus)

Abstract

An accurate battery State of Charge (SOC) method are essential for having optimum utilization of a battery. The SOC estimation in this research propose Back propagation Neural Network method, then the result compare with Open Circuit Voltage (OCV) prediction and coulometric counting method Experiment results show that the SOC estimation shows accurate measurements with maximum average percentage error no more than 0.893%.

Original languageEnglish
Title of host publicationProceedings of 2014 International Conference on Electrical Engineering and Computer Science, ICEECS 2014
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages274-278
Number of pages5
ISBN (Electronic)9781479984770
DOIs
Publication statusPublished - 18 Feb 2014
Event1st International Conference on Electrical Engineering and Computer Science, ICEECS 2014 - Denpasar, Indonesia
Duration: 24 Nov 201425 Nov 2014

Publication series

NameProceedings of 2014 International Conference on Electrical Engineering and Computer Science, ICEECS 2014

Conference

Conference1st International Conference on Electrical Engineering and Computer Science, ICEECS 2014
CountryIndonesia
CityDenpasar
Period24/11/1425/11/14

Keywords

  • coulometric counting
  • lead-acid batteries
  • neural network
  • open circuit voltage
  • state-of-charge estimation

Fingerprint Dive into the research topics of 'State of charge estimation for a lead-acid battery using backpropagation neural network method'. Together they form a unique fingerprint.

Cite this