Lithium-ion battery thermal management via advanced cooling parameters: State-of-the-art review on application of machine learning with exergy, economic and environmental analysis

Parsa, SM; Norozpour, F; Shoeibi, S; Shahsavar, A; Aberoumand, S; Afrand, M; Said, Z; Karimi, N

Lithium-ion battery thermal management via advanced cooling parameters: State-of-the-art review on application of machine learning with exergy, economic and environmental analysis

Parsa, SM Norozpour, F Shoeibi, S Shahsavar, A Aberoumand, S Afrand, M Said, Z Karimi, N

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Publisher:: Elsevier
Publication Type:: Journal Article
Citation:: Journal of the Taiwan Institute of Chemical Engineers, 2023, 148, pp. 104854
Issue Date:: 2023-07-01

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Field	Value	Language
dc.contributor.author	Parsa, SM
dc.contributor.author	Norozpour, F
dc.contributor.author	Shoeibi, S
dc.contributor.author	Shahsavar, A
dc.contributor.author	Aberoumand, S
dc.contributor.author	Afrand, M
dc.contributor.author	Said, Z
dc.contributor.author	Karimi, N
dc.date.accessioned	2024-04-12T09:42:54Z
dc.date.available	2024-04-12T09:42:54Z
dc.date.issued	2023-07-01
dc.identifier.citation	Journal of the Taiwan Institute of Chemical Engineers, 2023, 148, pp. 104854
dc.identifier.issn	1876-1070
dc.identifier.uri	http://hdl.handle.net/10453/177873
dc.description.abstract	Background: Lithium-ion (Li-ion) batteries are one of the most attractive and promising energy storage systems that emerge in different industrial sectors –at the top of them electrical vehicles (EVs) and electronic devices –regarding the tight collaboration of scientific community and industry. Among crucial factors on performance of Li-ion batteries, thermal management is of great importance as it directly impacted the system from different views. Methods: In the present review, state of the art of advance cooling systems’ (such as air/liquid-based cooling, PCM, refrigeration, heat pipe and thermoelectric) parameters of Li-ion batteries from different aspects are scrutinized. Exergy, economic and environmental (3E) analysis used as powerful tools to realize important parameters in battery thermal management. Furthermore, data-driven and machine learning applications in thermal regulation of Li-ion battery and their impact on putting the next steps in this context have been discussed. Significant findings: The pros and cons of each system considering aforementioned tools are realized. Particularly, it was realized that machine learning can be play a vital role in this context while other parameters with respect to 3E analysis can put several steps for better thermal management. Finally, concluding remarks and recommendations and research gaps as the future directions presented.
dc.language	en
dc.publisher	Elsevier
dc.relation.ispartof	Journal of the Taiwan Institute of Chemical Engineers
dc.relation.isbasedon	10.1016/j.jtice.2023.104854
dc.rights	info:eu-repo/semantics/closedAccess
dc.subject.classification	Chemical Engineering
dc.title	Lithium-ion battery thermal management via advanced cooling parameters: State-of-the-art review on application of machine learning with exergy, economic and environmental analysis
dc.type	Journal Article
utslib.citation.volume	148
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 Civil and Environmental Engineering
utslib.copyright.status	closed_access	*
dc.date.updated	2024-04-12T09:42:44Z
pubs.publication-status	Published
pubs.volume	148

Abstract:

Background: Lithium-ion (Li-ion) batteries are one of the most attractive and promising energy storage systems that emerge in different industrial sectors –at the top of them electrical vehicles (EVs) and electronic devices –regarding the tight collaboration of scientific community and industry. Among crucial factors on performance of Li-ion batteries, thermal management is of great importance as it directly impacted the system from different views. Methods: In the present review, state of the art of advance cooling systems’ (such as air/liquid-based cooling, PCM, refrigeration, heat pipe and thermoelectric) parameters of Li-ion batteries from different aspects are scrutinized. Exergy, economic and environmental (3E) analysis used as powerful tools to realize important parameters in battery thermal management. Furthermore, data-driven and machine learning applications in thermal regulation of Li-ion battery and their impact on putting the next steps in this context have been discussed. Significant findings: The pros and cons of each system considering aforementioned tools are realized. Particularly, it was realized that machine learning can be play a vital role in this context while other parameters with respect to 3E analysis can put several steps for better thermal management. Finally, concluding remarks and recommendations and research gaps as the future directions presented.

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http://hdl.handle.net/10453/177873