State of Power Capability Prediction of Lithium-Ion Battery from the Perspective of Electrochemical Mechanisms Considering Temperature Effect

Sun, X; Xu, N; Chen, Q; Yang, J; Zhu, J; Xu, J; Zheng, L

State of Power Capability Prediction of Lithium-Ion Battery from the Perspective of Electrochemical Mechanisms Considering Temperature Effect

Sun, X Xu, N Chen, Q Yang, J Zhu, J

Xu, J Zheng, L

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Publisher:: Institute of Electrical and Electronics Engineers (IEEE)
Publication Type:: Journal Article
Citation:: IEEE Transactions on Transportation Electrification, 2022, PP, (99), pp. 1-1
Issue Date:: 2022-01-01

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Field	Value	Language
dc.contributor.author	Sun, X
dc.contributor.author	Xu, N
dc.contributor.author	Chen, Q
dc.contributor.author	Yang, J
dc.contributor.author	Zhu, J https://orcid.org/0000-0002-9763-4047
dc.contributor.author	Xu, J
dc.contributor.author	Zheng, L
dc.date.accessioned	2023-03-21T01:00:25Z
dc.date.available	2023-03-21T01:00:25Z
dc.date.issued	2022-01-01
dc.identifier.citation	IEEE Transactions on Transportation Electrification, 2022, PP, (99), pp. 1-1
dc.identifier.issn	2332-7782
dc.identifier.issn	2332-7782
dc.identifier.uri	http://hdl.handle.net/10453/167868
dc.description.abstract	Safe and efficient battery operations necessitate the technique of accurate state of power (SOP) capability prediction for lithium-ion batteries. From the perspective of electrochemical mechanisms, the battery SOP depends strongly on the status of battery electrochemical reaction processes and associated variables, especially the lithium-ion concentrations in solid phases. Battery electrochemical models (EMs) are capable of depicting dynamics inside the battery with high fidelity, but most EMs fail to capture the temperature dependences of model parameters, thereby limiting the model accuracy and their application. This paper proposes an EM-based SOP prediction method considering temperature effect, in which battery peak currents and power capabilities are mainly determined by the intercalated or de-intercalated number of lithium-ions from solid particles, and the temperature dependences of battery EM parameters are investigated to enhance the robustness of the model. With the physical limits of lithium-ion concentrations in solid particles, the grey wolf optimizer (GWO) algorithm is applied to seek the peak currents and power capabilities within different predictive time horizons. The accuracy and robustness of the proposed method are evaluated systematically, and promising SOP prediction results with most of the mean absolute percentage errors of less than 4.0% can be achieved under various temperatures.
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.2022.3206452
dc.rights	info:eu-repo/semantics/closedAccess
dc.subject	0906 Electrical and Electronic Engineering
dc.title	State of Power Capability Prediction of Lithium-Ion Battery from the Perspective of Electrochemical Mechanisms Considering Temperature Effect
dc.type	Journal Article
utslib.citation.volume	PP
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	closed_access	*
dc.date.updated	2023-03-21T01:00:20Z
pubs.issue	99
pubs.publication-status	Published
pubs.volume	PP
utslib.citation.issue	99

Abstract:

Safe and efficient battery operations necessitate the technique of accurate state of power (SOP) capability prediction for lithium-ion batteries. From the perspective of electrochemical mechanisms, the battery SOP depends strongly on the status of battery electrochemical reaction processes and associated variables, especially the lithium-ion concentrations in solid phases. Battery electrochemical models (EMs) are capable of depicting dynamics inside the battery with high fidelity, but most EMs fail to capture the temperature dependences of model parameters, thereby limiting the model accuracy and their application. This paper proposes an EM-based SOP prediction method considering temperature effect, in which battery peak currents and power capabilities are mainly determined by the intercalated or de-intercalated number of lithium-ions from solid particles, and the temperature dependences of battery EM parameters are investigated to enhance the robustness of the model. With the physical limits of lithium-ion concentrations in solid particles, the grey wolf optimizer (GWO) algorithm is applied to seek the peak currents and power capabilities within different predictive time horizons. The accuracy and robustness of the proposed method are evaluated systematically, and promising SOP prediction results with most of the mean absolute percentage errors of less than 4.0% can be achieved under various temperatures.

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http://hdl.handle.net/10453/167868