Toward Safe and Reliable Batteries: Multi-Objective Optimization of a Serpentine Cooling Channel for Battery Thermal Management Using GPR and NSGA-II

Thermal management plays a critical role in maintaining the safety and reliability of lithium-ion batteries by limiting excessive temperature rise and reducing non-uniform temperature distribution within battery packs. This study proposes a geometry-driven multi-objective optimization framework for a serpentine liquid-cooling channel to enhance the thermal behavior of a battery module under fixed operating conditions. A three-dimensional computational fluid dynamics (CFD) model was developed for a 40-cell battery module, and Latin hypercube sampling was employed to generate training data for Gaussian Process Regression (GPR) surrogate models. Three geometric design variables, namely, channel thickness (tc), wall thickness (tw), and contact surface angle (θ), were considered, while the maximum battery temperature (Tmax) and the maximum temperature difference within the battery pack (ΔTmax) were selected as optimization objectives. Sensitivity analysis showed that wall thickness was the dominant parameter, contributing 65.41% and 64.77% to the variations in Tmax and ΔTmax, respectively, followed by channel thickness, whereas the influence of the contact surface angle was comparatively limited. The trained GPR models were then coupled with the non-dominated sorting genetic algorithm (NSGA-II) to identify the optimal channel geometry. The optimal design was obtained at tc = 2.95 mm, tw = 0.949 mm, and θ = 60°. CFD validation confirmed that the optimized design reduced Tmax from 307.639 K to 306.653 K, corresponding to a temperature drop of 0.986 K, while ΔTmax decreased from 8.752 K to 7.887 K, representing a reduction of 9.88%. Although the reduction in Tmax is modest, the improvement in temperature uniformity is meaningful, which benefits cell consistency and long-term reliability. These results demonstrate that geometric optimization of cooling channels can provide an effective and energy-efficient approach to improving thermal uniformity in lithium-ion battery systems.