Multi-Objective Optimization Design of Wavey-Channel Cold Plates for Li-Ion Batteries by Deep Neural Network

The continuously improving power density of Li-ion batteries and the widespread application of fast charging and discharging have rendered thermal management an increasingly critical task. Cold plates are among the most important means for such a task, and their channel structure significantly affects battery performance. Aiming to further improve the thermohydraulic performance of cold plate, this study proposes a cold plate with sinusoidal wave-shaped channel. Using channel quantity, amplitude, wavelength, diameter, and coolant mass flow rate as variables, the orthogonal experimental scheme is employed to design combinations of different variables for numerical simulation. The numerical simulation results are used to train a deep neural network for cold plate performance prediction. The trained neural network can accurately predict the maximum temperature, comprehensive performance indicators, and entropy generation rate with errors below 5.0%, 5.0%, and 10.0%, respectively. Multi-objective optimization design (MOOD) is implemented by combining a deep neural network with the NSGA-II genetic optimization, yielding two sets of Pareto fronts as follows: one for maximizing comprehensive performance indicator and minimizing entropy generation rate, and the other for minimizing maximum temperature and entropy generation rate, and TOPSIS decision points are provided. This study provides a new method and valuable MOOD results for the thermal management of Li-ion batteries and cold plate engineering while offering theoretical guidance for practical applications.