State of Charge Estimation of Lead-Acid Battery with Coulomb Counting and Feed-Forward Neural Network Method

Derry Rifqi Septian Nugraha, Anjar Bryantiko Pangestu, Faiz Husnayain

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

Abstract

This study aims to design and assess the simulation of the state of charge (SoC) estimation on lead-acid batteries using the Coulomb counting (CC) and feed-forward neural network (FFNN) method. Also, this study compared the effectiveness of each technique. CC and FFNN methods were designed and simulated in Simulink, and the results were analyzed. The two estimation results are compared to see the level of efficiency of each technique. The results of this study show that the feed-forward neural network method is better than the Coulomb counting method in load variation with a ratio of 5.56% on the first data input and 0.46% on the second data input, and 5.78% in temperature variation.

Original languageEnglish
Title of host publicationProceeding - 1st FORTEI-International Conference on Electrical Engineering, FORTEI-ICEE 2020
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages119-124
Number of pages6
ISBN (Electronic)9781728194349
DOIs
Publication statusPublished - 23 Sep 2020
Event1st FORTEI-International Conference on Electrical Engineering, FORTEI-ICEE 2020 - Virtual, Bandung, Indonesia
Duration: 23 Sep 202024 Sep 2020

Publication series

NameProceeding - 1st FORTEI-International Conference on Electrical Engineering, FORTEI-ICEE 2020

Conference

Conference1st FORTEI-International Conference on Electrical Engineering, FORTEI-ICEE 2020
CountryIndonesia
CityVirtual, Bandung
Period23/09/2024/09/20

Keywords

  • Coulomb counting
  • feed-forward neural network
  • lead-acid battery
  • state of charge

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