This study proposes a multi-objective optimization framework for a cooling plate-based indirect liquid cooling system applied to a 6S2P lithium-ion battery module during 3C fast charging. A three-dimensional computational fluid dynamics (CFD) model coupled with the multi-scale multi-domain (MSMD)–Newman–Tiedemann–Gu–Kim (NTGK) battery heat generation model was developed to investigate the system thermal–hydraulic behavior. The numerical model was experimentally validated through single-cell charging tests, with temperature deviations below 5%, confirming its reliability. A systematic parametric analysis was conducted to evaluate the effects of coolant channel number, channel width, channel spacing, and coolant mass flow rate on maximum temperature (Tmax), temperature difference (ΔT), and pressure drop (ΔP). The results indicated that increasing the coolant flow rate significantly enhanced thermal performance but caused a substantial increase in hydraulic losses, whereas geometric parameters had comparatively smaller effects. To improve optimization efficiency, 30 design samples were generated using Latin hypercube sampling and used to train ANN surrogate models, which demonstrated high predictive accuracy with test R2 values of 0.9931, 0.9960, and 0.9842 for Tmax, ΔT, and pumping power (Ppump), respectively. Subsequently, NSGA-II combined with TOPSIS identified the optimal design with a channel width of 6.22 mm, channel spacing of 4.84 mm, and coolant flow rate of 2.55 LPM. Under these conditions, the optimized system achieved a Tmax of 30.47 °C, a ΔT of 4.50 °C, and a Ppump of 0.05879 W. The relative deviations between ANN predictions and CFD results were all below 1%, demonstrating the robustness of the proposed optimization framework. These findings provide an effective design methodology for enhancing heat transfer while minimizing pumping power in advanced battery thermal management systems.
Park et al. (2026) studied this question.