A long short-term memory network for online state-of-charge estimation of li-ion battery cells

Z. Shi; M. Savargaonkar; A.A. Chehade; A.A. Hussein

doi:10.1109/itec48692.2020.9161487

A long short-term memory network for online state-of-charge estimation of li-ion battery cells

Z. Shi
, M. Savargaonkar
, A.A. Chehade
, A.A. Hussein

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

11 Scopus citations

Abstract

This paper proposes a new long short-term memory neural network model to estimate the state-of-charge (SOC) of lithium-ion (Li-ion) battery cells. The proposed model improves the estimation accuracy by accounting for the changes in the battery parameters due to ageing by utilizing relevant knowledge from previous cycles when estimating the current state-of-charge. Derivation and details of the proposed model followed by experimental verification using commercial Li-ion battery cells are provided.

Original language	English
Title of host publication	2020 IEEE Transportation Electrification Conference and Expo, ITEC 2020
Pages	594-597
Number of pages	4
ISBN (Electronic)	9781728146294
DOIs	https://doi.org/10.1109/itec48692.2020.9161487
State	Published - Jun 2020
Externally published	Yes

Publication series

Name	2020 IEEE Transportation Electrification Conference and Expo, ITEC 2020

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10.1109/itec48692.2020.9161487

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Shi, Z., Savargaonkar, M., Chehade, A. A., & Hussein, A. A. (2020). A long short-term memory network for online state-of-charge estimation of li-ion battery cells. In 2020 IEEE Transportation Electrification Conference and Expo, ITEC 2020 (pp. 594-597). Article 9161487 (2020 IEEE Transportation Electrification Conference and Expo, ITEC 2020). https://doi.org/10.1109/itec48692.2020.9161487

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title = "A long short-term memory network for online state-of-charge estimation of li-ion battery cells",

abstract = "This paper proposes a new long short-term memory neural network model to estimate the state-of-charge (SOC) of lithium-ion (Li-ion) battery cells. The proposed model improves the estimation accuracy by accounting for the changes in the battery parameters due to ageing by utilizing relevant knowledge from previous cycles when estimating the current state-of-charge. Derivation and details of the proposed model followed by experimental verification using commercial Li-ion battery cells are provided.",

author = "Z. Shi and M. Savargaonkar and A.A. Chehade and A.A. Hussein",

note = "Publisher Copyright: {\textcopyright} 2020 IEEE.",

year = "2020",

month = jun,

doi = "10.1109/itec48692.2020.9161487",

language = "English",

isbn = "9781728146294",

series = "2020 IEEE Transportation Electrification Conference and Expo, ITEC 2020",

pages = "594--597",

booktitle = "2020 IEEE Transportation Electrification Conference and Expo, ITEC 2020",

}

Shi, Z, Savargaonkar, M, Chehade, AA & Hussein, AA 2020, A long short-term memory network for online state-of-charge estimation of li-ion battery cells. in 2020 IEEE Transportation Electrification Conference and Expo, ITEC 2020., 9161487, 2020 IEEE Transportation Electrification Conference and Expo, ITEC 2020, pp. 594-597. https://doi.org/10.1109/itec48692.2020.9161487

A long short-term memory network for online state-of-charge estimation of li-ion battery cells. / Shi, Z.; Savargaonkar, M.; Chehade, A.A. et al.
2020 IEEE Transportation Electrification Conference and Expo, ITEC 2020. 2020. p. 594-597 9161487 (2020 IEEE Transportation Electrification Conference and Expo, ITEC 2020).

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

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AU - Chehade, A.A.

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AB - This paper proposes a new long short-term memory neural network model to estimate the state-of-charge (SOC) of lithium-ion (Li-ion) battery cells. The proposed model improves the estimation accuracy by accounting for the changes in the battery parameters due to ageing by utilizing relevant knowledge from previous cycles when estimating the current state-of-charge. Derivation and details of the proposed model followed by experimental verification using commercial Li-ion battery cells are provided.

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Shi Z, Savargaonkar M, Chehade AA, Hussein AA. A long short-term memory network for online state-of-charge estimation of li-ion battery cells. In 2020 IEEE Transportation Electrification Conference and Expo, ITEC 2020. 2020. p. 594-597. 9161487. (2020 IEEE Transportation Electrification Conference and Expo, ITEC 2020). Epub 2020 Jun. doi: 10.1109/itec48692.2020.9161487

A long short-term memory network for online state-of-charge estimation of li-ion battery cells

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