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

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

29 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 language	English
Title of host publication	GoodIT 2021 - Proceedings of the 2021 Conference on Information Technology for Social Good
Publisher	Association for Computing Machinery, Inc
Pages	85-90
Number of pages	6
ISBN (Electronic)	9781450384780
DOIs	https://doi.org/10.1145/3462203.3475878
Publication status	Published - 9 Sept 2021
Event	1st Conference on Information Technology for Social Good, GoodIT 2021 - Rome, Italy Duration: 9 Sept 2021 → 11 Sept 2021

Publication series

Name	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
Country/Territory	Italy
City	Rome
Period	9/09/21 → 11/09/21

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

SDG 7 Affordable and Clean Energy

Keywords

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

Access to Document

10.1145/3462203.3475878

Cite this

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. In GoodIT 2021 - Proceedings of the 2021 Conference on Information Technology for Social Good (pp. 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 et al. / 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. pp. 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",

pages = "85--90",

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

}

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. in 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, pp. 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 et al.
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).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

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. In 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

Abstract

Publication series

Conference

UN SDGs

Keywords

Access to Document

Other files and links

Fingerprint

Cite this