Artificial Neural Network-Based Optimization of an Inlet Perforated Distributor Plate for Uniform Coolant Entry in 10 kWh 24S24P Cylindrical Battery Module

In this study, a multi-objective optimization framework based on an artificial neural network (ANN) was developed for an inlet perforated distributor plate in a 24S24P 10 kWh cylindrical lithium-ion battery module using immersion cooling. A combined Newman, Tiedeman, Gu and Kim with Computational Fluid Dynamics (NTGK-CFD) model was used to generate a symmetrically designed space by varying the input variables, including hole size A (mm), hole spacing ΔH (mm), and coolant mass flow rate Vin (kg/s). A three-level full factorial design was used to generate 27 cases, then CFD simulations were performed to provide a training data for the ANN model to predict the output variables, including maximum temperature Tmax, maximum temperature difference ΔTmax, and pressure drop ΔP. The results show that the ANN model provides a reliable predictive model, capable of reproducing the thermal-hydraulic behavior of the immersion-cooled battery module with high fidelity via correlation coefficients R of 0.997 for all three output variables. In addition, Pareto-based optimization shows designs that balance cooling efficiency and pumping power. The selected optimal solution maintains Tmax within the optimal range at 37.97 °C while reducing ΔP by up to 44%, providing a practical solution for large-scale battery module thermal management in EVs.