Thermal–Hydraulic Optimization of a Metal Foam Manifold Cold Plate for Energy Storage Battery Systems Using CFD and Machine Learning

This work presents a new Battery Thermal Management System (BTMS) concept utilizing a metal foam manifold cold plate (MFMCP), developed specifically to meet the rising thermal dissipation needs of lithium-ion batteries. By coupling a manifold flow design and high-conductivity metal foams (characterized via SEM), the system significantly enhanced heat transfer and temperature uniformity. A Local Thermal Equilibrium (LTE) model evaluated thermal–hydraulic performance across varying Reynolds numbers (40–200), foam structures (PPI and porosity), and Al2O3 nanofluid concentrations. Results indicated that an optimal foam structure (95 PPI, porosity of 0.905) yielded a significant surface temperature reduction of 15 K. Although the Al2O3 nanofluid provided an additional cooling effect of 0.3 K, its pressure penalty lowered the performance evaluation criterion (PEC); the highest initial PEC of 2.57 was established when employing the pure base fluid without any functional additives. A Multi-Layer Perceptron (MLP) model was developed to expedite design validation, achieving high predictive accuracy as evidenced by an R2 of 0.983 and an MRE of 1.8%. Subsequent optimization via a genetic algorithm (GA) further increased the maximum PEC by 12% to 2.88. Equal pumping power system simulations indicated that the MFMCP design achieved a peak cell temperature of 304.35 K, outperforming traditional designs which peaked at 308.33 K. Furthermore, transient tests at 1C to 2C discharge rates confirmed consistent cooling improvements, demonstrating the MFMCP’s promising potential for dynamic operational conditions.