TY - GEN
T1 - Li-Ion batteries state-of-charge estimation using deep LSTM at various battery specifications and discharge cycles
AU - Wong, Kei Long
AU - Bosello, Michael
AU - Tse, Rita
AU - Falcomer, Carlo
AU - Rossi, Claudio
AU - Pau, Giovanni
N1 - Publisher Copyright:
© 2021 ACM.
PY - 2021/9/9
Y1 - 2021/9/9
N2 - 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.
AB - 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.
KW - Lithium-ion battery
KW - Long short-term memory
KW - Recurrent neural network
KW - State of charge estimation
UR - http://www.scopus.com/inward/record.url?scp=85115362086&partnerID=8YFLogxK
U2 - 10.1145/3462203.3475878
DO - 10.1145/3462203.3475878
M3 - Conference contribution
AN - SCOPUS:85115362086
T3 - GoodIT 2021 - Proceedings of the 2021 Conference on Information Technology for Social Good
SP - 85
EP - 90
BT - GoodIT 2021 - Proceedings of the 2021 Conference on Information Technology for Social Good
PB - Association for Computing Machinery, Inc
T2 - 1st Conference on Information Technology for Social Good, GoodIT 2021
Y2 - 9 September 2021 through 11 September 2021
ER -
