{"id":{"repo_id":"uiuc","oai_identifier":"oai:www.ideals.illinois.edu:2142/124704"},"canonical_url":"https://search.dev.ndltd.org/etd/uiuc/oai:www.ideals.illinois.edu:2142/124704","repository":{"repo_id":"uiuc","name":"University of Illinois - Urbana-Champaign","base_url":"https://www.ideals.illinois.edu/oai-pmh"},"display":{"title":"A comparative analysis of air and liquid cooling techniques for battery packs with machine learning insights into immersion cooling","abstract":"Submission published under a 24 month embargo labeled 'Closed Access', the embargo will last until 2026-05-01","abstract_html":"Submission published under a 24 month embargo labeled 'Closed Access', the embargo will last until 2026-05-01","abstract_has_math":false,"creators":["Kabirzadeh, Pouya"],"institution":"University of Illinois at Urbana-Champaign","degree_name":"M.S.","degree_level":"Thesis","degree_discipline":"Mechanical Engineering","degree_department":null,"school":null,"contributors":["Miljkovic, Nenad"],"advisors":[],"committee_chairs":[],"committee_members":[],"year":2024,"date_issued":"2024-05","date_published":"2024-05","updated_at":"2026-07-22T22:25:02Z","subjects":["Battery Thermal Management","Immersion Cooling","Machine Learning","Literature Review","Optimization","Air Cooling"],"languages":["en","eng"],"rights":["Copyright 2024 Pouya Kabirzadeh"],"rights_urls":[],"identifier_entries":[]},"links":{"outbound_url":"https://hdl.handle.net/2142/124704","outbound_label":"Handle","outbound_source":"dc:identifier"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor","label":"Contributor","values":["Miljkovic, Nenad"]},{"key":"dc:creator","label":"Author","values":["Kabirzadeh, Pouya"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date","label":"Dc Date","values":["2024-05","2024-05-01"]},{"key":"dc:type","label":"Dc Type","values":["text"]},{"key":"thesis:degree_discipline","label":"Discipline","values":["Mechanical Engineering"]},{"key":"thesis:degree_level","label":"Degree Level","values":["Thesis"]},{"key":"thesis:degree_name","label":"Degree Name","values":["M.S."]},{"key":"thesis:institution_name","label":"Thesis Institution Name","values":["University of Illinois at Urbana-Champaign"]}]},{"id":"subjects_keywords","label":"Subjects and Keywords","entries":[{"key":"dc:subject","label":"Dc Subject","values":["Battery Thermal Management","Immersion Cooling","Machine Learning","Literature Review","Optimization","Air Cooling"]}]},{"id":"language_rights","label":"Language and Rights","entries":[{"key":"dc:language","label":"Dc Language","values":["en","eng"]},{"key":"dc:rights","label":"Dc Rights","values":["Copyright 2024 Pouya Kabirzadeh"]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier","label":"Identifier","values":["https://hdl.handle.net/2142/124704"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description","label":"Description","values":["Submission published under a 24 month embargo labeled 'Closed Access', the embargo will last until 2026-05-01","The student, Pouya Kabirzadeh, accepted the attached license on 2024-04-29 at 09:55.","The student, Pouya Kabirzadeh, submitted this Thesis for approval on 2024-04-29 at 10:31.","This Thesis was approved for publication on 2024-05-01 at 16:29.","DSpace SAF Submission Ingestion Package generated from Vireo submission #20625 on 2024-09-16 at 00:50:56","This work presents an integrated approach to optimizing the design of a 21700 cylindrical battery pack with immersion cooling to enhance thermal management and reduce energy consumption under harsh loading conditions. A thorough literature review on air cooling and immersion cooling systems provided foundational insights that informed our approach. Cell-to-pack technology, a widely adopted strategy for electric vehicles, increases the energy and volumetric density of battery packs but requires robust thermal management to maintain temperature uniformity and ensure optimal battery performance. In our study, we developed a high-fidelity finite element model based on experimental data to predict temperature variations and energy consumption across different battery layouts and target temperatures. A Gaussian process-based surrogate model was used alongside a data-driven generative design method employing a variational autoencoder. This combination allowed for mining useful properties from a dataset of existing battery layout designs and performance metrics, facilitating the identification of optimal design configurations. The results demonstrate that our co-design approach not only enhances the effectiveness of immersion cooling systems by ensuring better temperature control but also reduces the system’s energy consumption by 13%. Additionally, candidate designs optimizing the layout decisions significantly lower the cooling costs by 90%, making this method particularly effective for managing the thermal environment of battery packs in electric vehicles. This comprehensive modeling and optimization framework effectively integrates battery design and cooling system performance, paving the way for more efficient electric vehicle technologies."]},{"key":"dc:format","label":"Dc Format","values":["application/pdf"]},{"key":"dc:title","label":"Title","values":["A comparative analysis of air and liquid cooling techniques for battery packs with machine learning insights into immersion cooling"]}]}],"canonical_facts":{"dc:contributor":["Miljkovic, Nenad"],"dc:creator":["Kabirzadeh, Pouya"],"dc:date":["2024-05","2024-05-01"],"dc:description":["Submission published under a 24 month embargo labeled 'Closed Access', the embargo will last until 2026-05-01","The student, Pouya Kabirzadeh, accepted the attached license on 2024-04-29 at 09:55.","The student, Pouya Kabirzadeh, submitted this Thesis for approval on 2024-04-29 at 10:31.","This Thesis was approved for publication on 2024-05-01 at 16:29.","DSpace SAF Submission Ingestion Package generated from Vireo submission #20625 on 2024-09-16 at 00:50:56","This work presents an integrated approach to optimizing the design of a 21700 cylindrical battery pack with immersion cooling to enhance thermal management and reduce energy consumption under harsh loading conditions. A thorough literature review on air cooling and immersion cooling systems provided foundational insights that informed our approach. Cell-to-pack technology, a widely adopted strategy for electric vehicles, increases the energy and volumetric density of battery packs but requires robust thermal management to maintain temperature uniformity and ensure optimal battery performance. In our study, we developed a high-fidelity finite element model based on experimental data to predict temperature variations and energy consumption across different battery layouts and target temperatures. A Gaussian process-based surrogate model was used alongside a data-driven generative design method employing a variational autoencoder. This combination allowed for mining useful properties from a dataset of existing battery layout designs and performance metrics, facilitating the identification of optimal design configurations. The results demonstrate that our co-design approach not only enhances the effectiveness of immersion cooling systems by ensuring better temperature control but also reduces the system’s energy consumption by 13%. Additionally, candidate designs optimizing the layout decisions significantly lower the cooling costs by 90%, making this method particularly effective for managing the thermal environment of battery packs in electric vehicles. This comprehensive modeling and optimization framework effectively integrates battery design and cooling system performance, paving the way for more efficient electric vehicle technologies."],"dc:format":["application/pdf"],"dc:identifier":["https://hdl.handle.net/2142/124704"],"dc:language":["en","eng"],"dc:rights":["Copyright 2024 Pouya Kabirzadeh"],"dc:subject":["Battery Thermal Management","Immersion Cooling","Machine Learning","Literature Review","Optimization","Air Cooling"],"dc:title":["A comparative analysis of air and liquid cooling techniques for battery packs with machine learning insights into immersion cooling"],"dc:type":["text"],"thesis:degree_discipline":["Mechanical Engineering"],"thesis:degree_level":["Thesis"],"thesis:degree_name":["M.S."],"thesis:institution_name":["University of Illinois at Urbana-Champaign"]},"updated_at":"2026-07-22T22:25:02Z"}