Dimensionless analysis and novel configuration for enhanced natural convection cooling in lithium-ion batteries
Year: 2025
Type: article
Abstract: Lithium-ion batteries are widely used due to their high energy density and long life, but their temperature-sensitive performance demands effective thermal management. Natural convection is a passive, energy-efficient and reliable cooling method, particularly suitable for scenarios with low heat generation. This study investigates the thermal behavior of cylindrical battery modules under natural convection, focusing on the effects of dimensionless spacing and aspect ratio. A dimensionless analysis framework was developed and coupled with response surface models and a multi-objective genetic algorithm to optimize battery layouts for heat dissipation and temperature uniformity. Optimized configurations achieved a 12.5 % reduction in temperature difference without sacrificing average heat rejection. To address the increased computational cost for larger modules, a priority-based sequential optimization method was introduced. Applying this method to an 8 × 8 module resulted in a 17.6 % reduction in temperature difference, with only a 0.28 % increase in average module temperature. These findings provide generalizable insights for passive thermal design and demonstrate that natural convection offers a low-cost and energy-efficient foundation for hybrid cooling systems. By passively managing standby or low-load heat without the need for fans or pumps, the proposed approach helps reduce the usage and complexity of active cooling, improving system efficiency and maintainability.
Authors: Congyu Mao, Min Liang Wang, Kewei Gao, Hongdi Jin, Woo Young Cho, Hernando Leon-Rodriguez, Kootaek Hong, Seunghwoi Han, Jihoon Jeong, Hyun Wook Kang
Institutions: Chonnam National University, Texas A&M University
Language: English
Subfield: Automotive Engineering
Field: Engineering
Domain: Physical Sciences
Open Access status: gold
Estimated APC paid: $1,620
Awards: G448759262, G6446108346
OpenAlex ID: W4412658396
