A comparative analysis of air and liquid cooling techniques for battery packs with machine learning insights into immersion cooling
Kabirzadeh, Pouya
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https://hdl.handle.net/2142/124704
Description
Title
A comparative analysis of air and liquid cooling techniques for battery packs with machine learning insights into immersion cooling
Author(s)
Kabirzadeh, Pouya
Issue Date
2024-05-01
Director of Research (if dissertation) or Advisor (if thesis)
Miljkovic, Nenad
Department of Study
Mechanical Sci & Engineering
Discipline
Mechanical Engineering
Degree Granting Institution
University of Illinois at Urbana-Champaign
Degree Name
M.S.
Degree Level
Thesis
Keyword(s)
Battery Thermal Management
Immersion Cooling
Machine Learning
Literature Review
Optimization
Air Cooling
Language
eng
Abstract
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.
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