A Surface Temperature Estimation Method for Lithium-Ion Battery Using Enhanced GRU-RNN

Yao, Q; Lu, DDC; Lei, G

A Surface Temperature Estimation Method for Lithium-Ion Battery Using Enhanced GRU-RNN

Yao, Q Lu, DDC Lei, G

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Publisher:: Institute of Electrical and Electronics Engineers (IEEE)
Publication Type:: Journal Article
Citation:: IEEE Transactions on Transportation Electrification, 2023, 9, (1), pp. 1103-1112
Issue Date:: 2023-03-01

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Field	Value	Language
dc.contributor.author	Yao, Q
dc.contributor.author	Lu, DDC
dc.contributor.author	Lei, G https://orcid.org/0000-0002-8369-6802
dc.date.accessioned	2023-03-19T23:17:29Z
dc.date.available	2023-03-19T23:17:29Z
dc.date.issued	2023-03-01
dc.identifier.citation	IEEE Transactions on Transportation Electrification, 2023, 9, (1), pp. 1103-1112
dc.identifier.issn	2332-7782
dc.identifier.issn	2332-7782
dc.identifier.uri	http://hdl.handle.net/10453/167613
dc.description.abstract	To monitor the thermal performance of the battery, the surface temperature (ST) of the battery is normally directly measured by temperature sensors. As the number of battery cells or strings increases, the number of temperature sensors increases proportionally. This increases the cost and reduces the reliability of the battery systems. To solve this problem, this article introduces a method to accurately estimate the ST of lithium-ion batteries using a recurrent neural network (RNN) with gated recurrent unit (GRU). First, this article analyzes the battery ST distribution theory and proves that it is a time series task since the present ST is conditioned on the previous state. Second, a GRU-RNN model is adopted to estimate the battery ST as this model has the ability to automatically encode dependencies in time and accurately estimate the battery ST without using any physical battery models or filters. Third, an improved data normalization method is proposed to enhance the estimation accuracy and robustness. Fourthly, the proposed data normalization method is incorporated into the stacked GRU-RNN to estimate the battery ST from compulsory online signals. The proposed method is verified with LiFePO4 using US06 and Federal Urban Driving Schedule (FUDS) profiles under four fixed ambient temperatures and with LiNiCoAIO2 using a mixed dynamic profiles under varying ambient temperature ranges (from 10 °C to 25 °C). The estimation error using mean absolute error (MAE) is less than 0.2 °C over all the fixed ambient temperature conditions and 0.42 °C over the varying ambient temperature conditions.
dc.language	en
dc.publisher	Institute of Electrical and Electronics Engineers (IEEE)
dc.relation.ispartof	IEEE Transactions on Transportation Electrification
dc.relation.isbasedon	10.1109/TTE.2022.3197927
dc.rights	info:eu-repo/semantics/embargoedAccess
dc.subject	0906 Electrical and Electronic Engineering
dc.title	A Surface Temperature Estimation Method for Lithium-Ion Battery Using Enhanced GRU-RNN
dc.type	Journal Article
utslib.citation.volume	9
utslib.for	0906 Electrical and Electronic Engineering
pubs.organisational-group	/University of Technology Sydney
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology/School of Electrical and Data Engineering
utslib.copyright.status	open_access	*
utslib.copyright.embargo	2024-08-10T00:00:00+1000Z
dc.date.updated	2023-03-19T23:17:27Z
pubs.issue	1
pubs.publication-status	Published
pubs.volume	9
utslib.citation.issue	1

Abstract:

To monitor the thermal performance of the battery, the surface temperature (ST) of the battery is normally directly measured by temperature sensors. As the number of battery cells or strings increases, the number of temperature sensors increases proportionally. This increases the cost and reduces the reliability of the battery systems. To solve this problem, this article introduces a method to accurately estimate the ST of lithium-ion batteries using a recurrent neural network (RNN) with gated recurrent unit (GRU). First, this article analyzes the battery ST distribution theory and proves that it is a time series task since the present ST is conditioned on the previous state. Second, a GRU-RNN model is adopted to estimate the battery ST as this model has the ability to automatically encode dependencies in time and accurately estimate the battery ST without using any physical battery models or filters. Third, an improved data normalization method is proposed to enhance the estimation accuracy and robustness. Fourthly, the proposed data normalization method is incorporated into the stacked GRU-RNN to estimate the battery ST from compulsory online signals. The proposed method is verified with LiFePO4 using US06 and Federal Urban Driving Schedule (FUDS) profiles under four fixed ambient temperatures and with LiNiCoAIO2 using a mixed dynamic profiles under varying ambient temperature ranges (from 10 °C to 25 °C). The estimation error using mean absolute error (MAE) is less than 0.2 °C over all the fixed ambient temperature conditions and 0.42 °C over the varying ambient temperature conditions.

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http://hdl.handle.net/10453/167613