An intelligence-based state of charge prediction for VRLA batteries

De Shaunna Scott, Jide Lu, Haneen Aburub, Aditya Sundararajan, Arif I. Sarwat

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

4 Scopus citations

Abstract

A battery management system (BMS) has three main functions, voltage monitoring, current discharge monitoring and remaining life monitoring. This paper primarily focuses on remaining life monitoring through the estimation of battery's state of charge (SOC). An Experimental set-up was prepared to measure the Valve-Regulated Lead-Acid (VRLA) battery's SOC under different operating conditions. Backpropagation (BP) neural network to estimate the battery's SOC using the experimental data. The results showed a satisfactory estimation of battery's SOC with a small (4.25%) root mean square perdition error (RMS).

Original languageEnglish
Title of host publication2017 IEEE Transportation Electrification Conference, ITEC-India 2017
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages1-4
Number of pages4
ISBN (Electronic)9781538626689
DOIs
StatePublished - Jul 2 2017
Externally publishedYes
Event2017 IEEE Transportation Electrification Conference, ITEC-India 2017 - Pune, India
Duration: Dec 13 2017Dec 15 2017

Publication series

Name2017 IEEE Transportation Electrification Conference, ITEC-India 2017
Volume2018-January

Conference

Conference2017 IEEE Transportation Electrification Conference, ITEC-India 2017
Country/TerritoryIndia
CityPune
Period12/13/1712/15/17

Keywords

  • Neural Network
  • SOC
  • SOC estimation
  • state of charge

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