State of Health Estimation of Lithium Iron Phosphate Batteries Based on Degradation Knowledge Transfer Learning

Lu, X; Qiu, J; Lei, G; Zhu, J

State of Health Estimation of Lithium Iron Phosphate Batteries Based on Degradation Knowledge Transfer Learning

Lu, X Qiu, J Lei, G

Zhu, J

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Publisher:: IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
Publication Type:: Journal Article
Citation:: IEEE Transactions on Transportation Electrification, 2023, 9, (3), pp. 4692-4703
Issue Date:: 2023-09-01

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Full metadata record

Field	Value	Language
dc.contributor.author	Lu, X
dc.contributor.author	Qiu, J
dc.contributor.author	Lei, G https://orcid.org/0000-0002-8369-6802
dc.contributor.author	Zhu, J https://orcid.org/0000-0002-9763-4047
dc.date.accessioned	2024-02-08T11:57:15Z
dc.date.available	2024-02-08T11:57:15Z
dc.date.issued	2023-09-01
dc.identifier.citation	IEEE Transactions on Transportation Electrification, 2023, 9, (3), pp. 4692-4703
dc.identifier.issn	2332-7782
dc.identifier.issn	2332-7782
dc.identifier.uri	http://hdl.handle.net/10453/175508
dc.description.abstract	Accurate state of health (SOH) estimation constitutes a critical task for systems employing lithium-ion (Li-ion) batteries. However, many current studies that focus on data-driven SOH estimation methods ignore the battery degradation modes (DMs). This article proposes a two-stage framework to develop an SOH estimation model for Li-ion batteries considering the transferred DM knowledge. First, a battery DM regression model is designed to diagnose the contributions of three DMs by transferring the DM knowledge. Since the real and synthetic datasets are independent and identically distributed, a DM regression model trained with the synthetic dataset cannot be directly applied to the real dataset. To bridge the gap, this article proposes a deepCoral-based domain adaptation method to minimize the regression loss and domain adaptation loss between the source domain (synthetic) and the target domain (real) such that the degradation knowledge learned from the synthetic batteries can be transferred to the real batteries. The model's structure and parameters are optimized through simulation tests to improve the diagnosis accuracy. Second, we propose a new deep learning model, conditional time series generative adversarial network (CTSGAN), which can effectively preserve temporal dynamics during battery degradation. With the DM and other related conditions, a CTSGAN-based SOH estimation model is constructed, which shows good estimation performance. Finally, case studies verify the effectiveness and superiority of degradation knowledge transfer learning and the SOH estimation for synthetic and real battery datasets.
dc.language	English
dc.publisher	IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
dc.relation.ispartof	IEEE Transactions on Transportation Electrification
dc.relation.isbasedon	10.1109/TTE.2023.3240617
dc.rights	info:eu-repo/semantics/embargoedAccess
dc.subject	0906 Electrical and Electronic Engineering
dc.subject.classification	4008 Electrical engineering
dc.title	State of Health Estimation of Lithium Iron Phosphate Batteries Based on Degradation Knowledge Transfer Learning
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	embargoed	*
utslib.copyright.embargo	2025-09-01T00:00:00+1000Z
dc.date.updated	2024-02-08T11:57:14Z
pubs.issue	3
pubs.publication-status	Published
pubs.volume	9
utslib.citation.issue	3

Abstract:

Accurate state of health (SOH) estimation constitutes a critical task for systems employing lithium-ion (Li-ion) batteries. However, many current studies that focus on data-driven SOH estimation methods ignore the battery degradation modes (DMs). This article proposes a two-stage framework to develop an SOH estimation model for Li-ion batteries considering the transferred DM knowledge. First, a battery DM regression model is designed to diagnose the contributions of three DMs by transferring the DM knowledge. Since the real and synthetic datasets are independent and identically distributed, a DM regression model trained with the synthetic dataset cannot be directly applied to the real dataset. To bridge the gap, this article proposes a deepCoral-based domain adaptation method to minimize the regression loss and domain adaptation loss between the source domain (synthetic) and the target domain (real) such that the degradation knowledge learned from the synthetic batteries can be transferred to the real batteries. The model's structure and parameters are optimized through simulation tests to improve the diagnosis accuracy. Second, we propose a new deep learning model, conditional time series generative adversarial network (CTSGAN), which can effectively preserve temporal dynamics during battery degradation. With the DM and other related conditions, a CTSGAN-based SOH estimation model is constructed, which shows good estimation performance. Finally, case studies verify the effectiveness and superiority of degradation knowledge transfer learning and the SOH estimation for synthetic and real battery datasets.

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http://hdl.handle.net/10453/175508