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

doi:10.1145/3462203.3475878

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

Faculty of Applied Sciences

University of Bologna
University of California at Los Angeles

研究成果: Conference contribution › 同行評審

32 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	https://doi.org/10.1145/3462203.3475878
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 2021 → 11 9月 2021

出版系列

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

Conference

Conference	1st Conference on Information Technology for Social Good, GoodIT 2021
國家/地區	Italy
城市	Rome
期間	9/09/21 → 11/09/21

UN SDG

此研究成果有助於以下永續發展目標

Affordable and clean energy

存取文件

10.1145/3462203.3475878

其它文件與鏈接

Link to publication in Scopus

引用此

Wong, K. L., Bosello, M., Tse, R., Falcomer, C., Rossi, C., & Pau, G. (2021). Li-Ion batteries state-of-charge estimation using deep LSTM at various battery specifications and discharge cycles. 於 GoodIT 2021 - Proceedings of the 2021 Conference on Information Technology for Social Good (頁 85-90). (GoodIT 2021 - Proceedings of the 2021 Conference on Information Technology for Social Good). Association for Computing Machinery, Inc. https://doi.org/10.1145/3462203.3475878

Wong, Kei Long ; Bosello, Michael ; Tse, Rita 等. / Li-Ion batteries state-of-charge estimation using deep LSTM at various battery specifications and discharge cycles. GoodIT 2021 - Proceedings of the 2021 Conference on Information Technology for Social Good. Association for Computing Machinery, Inc, 2021. 頁 85-90 (GoodIT 2021 - Proceedings of the 2021 Conference on Information Technology for Social Good).

@inproceedings{56ba277231d242e7a99178715e4ede64,

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

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.",

keywords = "Lithium-ion battery, Long short-term memory, Recurrent neural network, State of charge estimation",

author = "Wong, \{Kei Long\} and Michael Bosello and Rita Tse and Carlo Falcomer and Claudio Rossi and Giovanni Pau",

note = "Publisher Copyright: {\textcopyright} 2021 ACM.; 1st Conference on Information Technology for Social Good, GoodIT 2021 ; Conference date: 09-09-2021 Through 11-09-2021",

year = "2021",

month = sep,

day = "9",

doi = "10.1145/3462203.3475878",

language = "English",

series = "GoodIT 2021 - Proceedings of the 2021 Conference on Information Technology for Social Good",

publisher = "Association for Computing Machinery, Inc",

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}

Wong, KL, Bosello, M, Tse, R, Falcomer, C, Rossi, C & Pau, G 2021, Li-Ion batteries state-of-charge estimation using deep LSTM at various battery specifications and discharge cycles. 於 GoodIT 2021 - Proceedings of the 2021 Conference on Information Technology for Social Good. GoodIT 2021 - Proceedings of the 2021 Conference on Information Technology for Social Good, Association for Computing Machinery, Inc, 頁 85-90, 1st Conference on Information Technology for Social Good, GoodIT 2021, Rome, Italy, 9/09/21. https://doi.org/10.1145/3462203.3475878

Li-Ion batteries state-of-charge estimation using deep LSTM at various battery specifications and discharge cycles. / Wong, Kei Long; Bosello, Michael; Tse, Rita 等.
GoodIT 2021 - Proceedings of the 2021 Conference on Information Technology for Social Good. Association for Computing Machinery, Inc, 2021. p. 85-90 (GoodIT 2021 - Proceedings of the 2021 Conference on Information Technology for Social Good).

研究成果: Conference contribution › 同行評審

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

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 - https://www.scopus.com/pages/publications/85115362086

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 -

Wong KL, Bosello M, Tse R, Falcomer C, Rossi C, Pau G. Li-Ion batteries state-of-charge estimation using deep LSTM at various battery specifications and discharge cycles. 於 GoodIT 2021 - Proceedings of the 2021 Conference on Information Technology for Social Good. Association for Computing Machinery, Inc. 2021. p. 85-90. (GoodIT 2021 - Proceedings of the 2021 Conference on Information Technology for Social Good). doi: 10.1145/3462203.3475878

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

摘要

出版系列

Conference

UN SDG

存取文件

其它文件與鏈接

指紋

引用此