Optimization and analysis of maximum temperature in a battery pack affected by low to high Prandtl number coolants using response surface methodology and particle swarm optimization algorithm

Thermal analysis of heat-generating battery pack cooled by several coolants is analyzed numerically. The coolant used is gases, oils, thermal oils, nanofluids, and liquid metals to find the best coolant for temperature distribution. The conductivity ratio between the battery and coolant, flow Reynol...

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Main Authors: Afzal, Asif, Mokashi, Imran, Khan, Sher Afghan, Abdullah, Nur Azam, Azami, Muhammad Hanafi
Format: Article
Language:English
Published: Taylor and Francis 2020
Subjects:
Online Access:http://irep.iium.edu.my/85261/1/85261_Optimization%20and%20analysis%20of%20maximum%20temperature.pdf
http://irep.iium.edu.my/85261/
https://www.tandfonline.com/doi/abs/10.1080/10407782.2020.1845560?journalCode=unht20
https://doi.org/10.1080/10407782.2020.1845560
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spelling my.iium.irep.852612020-12-01T06:32:16Z http://irep.iium.edu.my/85261/ Optimization and analysis of maximum temperature in a battery pack affected by low to high Prandtl number coolants using response surface methodology and particle swarm optimization algorithm Afzal, Asif Mokashi, Imran Khan, Sher Afghan Abdullah, Nur Azam Azami, Muhammad Hanafi TL Motor vehicles. Aeronautics. Astronautics TL1 Motor vehicles Thermal analysis of heat-generating battery pack cooled by several coolants is analyzed numerically. The coolant used is gases, oils, thermal oils, nanofluids, and liquid metals to find the best coolant for temperature distribution. The conductivity ratio between the battery and coolant, flow Reynolds number, and heat generation inside the pack are varied for each coolant. The axial temperature variation, which provides the location and magnitude of maximum temperature, is studied. The maximum temperature of the battery pack is analyzed using response surface methodology, optimization of maximum temperature is performed using particle swarm optimization algorithm, and regression analysis is carried out at the end of this work. The thermal analysis carried out reveals that the gas coolants are inefficient in providing lower temperatures while nanofluids are the most suitable. The response surface reveals that the maximum temperature behavior is different for each category of coolant. Taylor and Francis 2020-11-22 Article PeerReviewed application/pdf en http://irep.iium.edu.my/85261/1/85261_Optimization%20and%20analysis%20of%20maximum%20temperature.pdf Afzal, Asif and Mokashi, Imran and Khan, Sher Afghan and Abdullah, Nur Azam and Azami, Muhammad Hanafi (2020) Optimization and analysis of maximum temperature in a battery pack affected by low to high Prandtl number coolants using response surface methodology and particle swarm optimization algorithm. Numerical Heat Transfer, Part A: Applications. ISSN 1521-0634 E-ISSN 1040-7782 (In Press) https://www.tandfonline.com/doi/abs/10.1080/10407782.2020.1845560?journalCode=unht20 https://doi.org/10.1080/10407782.2020.1845560
institution Universiti Islam Antarabangsa Malaysia
building IIUM Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider International Islamic University Malaysia
content_source IIUM Repository (IREP)
url_provider http://irep.iium.edu.my/
language English
topic TL Motor vehicles. Aeronautics. Astronautics
TL1 Motor vehicles
spellingShingle TL Motor vehicles. Aeronautics. Astronautics
TL1 Motor vehicles
Afzal, Asif
Mokashi, Imran
Khan, Sher Afghan
Abdullah, Nur Azam
Azami, Muhammad Hanafi
Optimization and analysis of maximum temperature in a battery pack affected by low to high Prandtl number coolants using response surface methodology and particle swarm optimization algorithm
description Thermal analysis of heat-generating battery pack cooled by several coolants is analyzed numerically. The coolant used is gases, oils, thermal oils, nanofluids, and liquid metals to find the best coolant for temperature distribution. The conductivity ratio between the battery and coolant, flow Reynolds number, and heat generation inside the pack are varied for each coolant. The axial temperature variation, which provides the location and magnitude of maximum temperature, is studied. The maximum temperature of the battery pack is analyzed using response surface methodology, optimization of maximum temperature is performed using particle swarm optimization algorithm, and regression analysis is carried out at the end of this work. The thermal analysis carried out reveals that the gas coolants are inefficient in providing lower temperatures while nanofluids are the most suitable. The response surface reveals that the maximum temperature behavior is different for each category of coolant.
format Article
author Afzal, Asif
Mokashi, Imran
Khan, Sher Afghan
Abdullah, Nur Azam
Azami, Muhammad Hanafi
author_facet Afzal, Asif
Mokashi, Imran
Khan, Sher Afghan
Abdullah, Nur Azam
Azami, Muhammad Hanafi
author_sort Afzal, Asif
title Optimization and analysis of maximum temperature in a battery pack affected by low to high Prandtl number coolants using response surface methodology and particle swarm optimization algorithm
title_short Optimization and analysis of maximum temperature in a battery pack affected by low to high Prandtl number coolants using response surface methodology and particle swarm optimization algorithm
title_full Optimization and analysis of maximum temperature in a battery pack affected by low to high Prandtl number coolants using response surface methodology and particle swarm optimization algorithm
title_fullStr Optimization and analysis of maximum temperature in a battery pack affected by low to high Prandtl number coolants using response surface methodology and particle swarm optimization algorithm
title_full_unstemmed Optimization and analysis of maximum temperature in a battery pack affected by low to high Prandtl number coolants using response surface methodology and particle swarm optimization algorithm
title_sort optimization and analysis of maximum temperature in a battery pack affected by low to high prandtl number coolants using response surface methodology and particle swarm optimization algorithm
publisher Taylor and Francis
publishDate 2020
url http://irep.iium.edu.my/85261/1/85261_Optimization%20and%20analysis%20of%20maximum%20temperature.pdf
http://irep.iium.edu.my/85261/
https://www.tandfonline.com/doi/abs/10.1080/10407782.2020.1845560?journalCode=unht20
https://doi.org/10.1080/10407782.2020.1845560
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score 13.209306