Li-Ion batteries state-of-charge estimation using deep LSTM at various battery specifications and discharge cycles

Kei Long Wong, Michael Bosello, Rita Tse, Carlo Falcomer, Claudio Rossi, Giovanni Pau

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

14 Citations (Scopus)

Abstract

Lithium-ion battery technologies play a key role in transforming the economy reducing its dependency on fossil fuels. Transportation, manufacturing, and services are being electrified. The European Commission predicts that in Europe everything that can be electrified will be electrified within a decade. The ability to accurate state of charge (SOC) estimation is crucial to ensure the safety of the operation of battery-powered electric devices and to guide users taking behaviors that can extend battery life and re-usability. In this paper, we investigate how machine learning models can predict the SOC of cylindrical Li-Ion batteries considering a variety of cells under different charge-discharge cycles.

Original languageEnglish
Title of host publicationGoodIT 2021 - Proceedings of the 2021 Conference on Information Technology for Social Good
PublisherAssociation for Computing Machinery, Inc
Pages85-90
Number of pages6
ISBN (Electronic)9781450384780
DOIs
Publication statusPublished - 9 Sept 2021
Event1st Conference on Information Technology for Social Good, GoodIT 2021 - Rome, Italy
Duration: 9 Sept 202111 Sept 2021

Publication series

NameGoodIT 2021 - Proceedings of the 2021 Conference on Information Technology for Social Good

Conference

Conference1st Conference on Information Technology for Social Good, GoodIT 2021
Country/TerritoryItaly
CityRome
Period9/09/2111/09/21

Keywords

  • Lithium-ion battery
  • Long short-term memory
  • Recurrent neural network
  • State of charge estimation

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