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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

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
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
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.
1876-1070
Parsa, Seyed Masoud
693c3d9f-cd1a-42d2-afec-49ab313abafb
Norozpour, Fatemeh
a56f0bc3-f70c-4ae2-b326-795440816419
Shoeibi, Shahin
6d0af273-c965-4d12-945c-a130d439c181
Shahsavar, Amin
457aa6c1-7956-4a51-bdac-9fcd154bedad
Aberoumand, Sadegh
88009d8d-447a-4663-b4ed-ad0cc14620b2
Afrand, Masoud
20ba8319-7e98-4955-b3b8-95fb3224d0f8
Said, Zafar
7ba4b59d-98fb-4a99-a25a-d45184cecf3e
Karimi, Nader
620646d6-27c9-4e1e-948f-f23e4a1e773a
Parsa, Seyed Masoud
693c3d9f-cd1a-42d2-afec-49ab313abafb
Norozpour, Fatemeh
a56f0bc3-f70c-4ae2-b326-795440816419
Shoeibi, Shahin
6d0af273-c965-4d12-945c-a130d439c181
Shahsavar, Amin
457aa6c1-7956-4a51-bdac-9fcd154bedad
Aberoumand, Sadegh
88009d8d-447a-4663-b4ed-ad0cc14620b2
Afrand, Masoud
20ba8319-7e98-4955-b3b8-95fb3224d0f8
Said, Zafar
7ba4b59d-98fb-4a99-a25a-d45184cecf3e
Karimi, Nader
620646d6-27c9-4e1e-948f-f23e4a1e773a

Parsa, Seyed Masoud, Norozpour, Fatemeh, Shoeibi, Shahin, Shahsavar, Amin, Aberoumand, Sadegh, Afrand, Masoud, Said, Zafar and Karimi, Nader (2023) 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. Journal of the Taiwan Institute of Chemical Engineers, 148, [104854]. (doi:10.1016/j.jtice.2023.104854).

Record type: Article

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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More information

Published date: 1 July 2023

Identifiers

Local EPrints ID: 509196
URI: http://eprints.soton.ac.uk/id/eprint/509196
ISSN: 1876-1070
PURE UUID: 12eeddb0-c916-4ac1-99d6-5d1951cc418d
ORCID for Nader Karimi: ORCID iD orcid.org/0000-0002-4559-6245

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Date deposited: 12 Feb 2026 17:51
Last modified: 13 Feb 2026 03:16

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Contributors

Author: Seyed Masoud Parsa
Author: Fatemeh Norozpour
Author: Shahin Shoeibi
Author: Amin Shahsavar
Author: Sadegh Aberoumand
Author: Masoud Afrand
Author: Zafar Said
Author: Nader Karimi ORCID iD

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