Estimating the state of health of lithium ion batteries using neural networks

Belal Elsabbagh, M.E.A. Farrag

Research output: Chapter in Book/Report/Conference proceedingConference contribution

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Abstract

With the world steadily transitioning to use electric vehicles, a new problem arises as for how batteries can be better maintained. Their maintenance requires a fully featured battery management system that can optimize the usage of these batteries for longer lifespans. One of the functionalities that could be useful is predicting the state of health of a lithium-ion battery. This paper explains how a model can be built to predict the Remaining Useful Life (RUL) of a battery by estimating the State of Health (SoH) of the battery using a neural network with given resistance measurements of a lithium-ion battery.
Original languageEnglish
Title of host publication24th International Middle East Power Systems Conference (MEPCON 2023)
PublisherIEEE
Number of pages6
ISBN (Electronic)9798350358469
ISBN (Print)9798350358476
DOIs
Publication statusPublished - 18 Mar 2024
Event24th International Middle East Power Systems Conference MEPCON 2023 - Mansoura University, Mansoura City, Egypt
Duration: 19 Dec 202321 Dec 2023
https://mepcon2023.mans.edu.eg/NewSU/libraries/eConference.aspx?DefaultLang=En&ScopeID=1.&BibID=11124355

Publication series

Name
ISSN (Print)2573-3044

Conference

Conference24th International Middle East Power Systems Conference MEPCON 2023
Country/TerritoryEgypt
CityMansoura City
Period19/12/2321/12/23
Internet address

ASJC Scopus subject areas

  • Artificial Intelligence
  • Mechanical Engineering
  • Control and Optimization
  • Safety, Risk, Reliability and Quality
  • Energy Engineering and Power Technology
  • Electrical and Electronic Engineering

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