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

研究成果: Conference contribution同行評審

14 引文 斯高帕斯(Scopus)

摘要

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.

原文English
主出版物標題GoodIT 2021 - Proceedings of the 2021 Conference on Information Technology for Social Good
發行者Association for Computing Machinery, Inc
頁面85-90
頁數6
ISBN(電子)9781450384780
DOIs
出版狀態Published - 9 9月 2021
事件1st Conference on Information Technology for Social Good, GoodIT 2021 - Rome, Italy
持續時間: 9 9月 202111 9月 2021

出版系列

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

Conference

Conference1st Conference on Information Technology for Social Good, GoodIT 2021
國家/地區Italy
城市Rome
期間9/09/2111/09/21

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