This study proposes a multi-objective optimization strategy for the structural design of liquid-cooled channels in battery systems, aiming to identify liquid-cooled plate design schemes with better cooling performance and acceptable flow resistance. Optimal Latin hypercube sampling (OLHS) was combined with computational fluid dynamics (CFD) simulations to construct a CFD-generated dataset that includes the maximum temperature and system pressure drop. Then, modeFRONTIER was employed to integrate surrogate-model training, rapid prediction, and non-dominated sorting genetic algorithm II (NSGA-II) optimization, thereby obtaining the Pareto optimal set. The technique for order preference by similarity to ideal solution (TOPSIS) decision method was further introduced to determine the final optimal design. Results indicate that the optimized liquid-cooling system exhibits outstanding comprehensive performance in terms of balancing heat dissipation and flow resistance at a 5 C discharge rate. Remarkably, sensitivity analysis shows that inlet velocity is the dominant factor affecting the maximum battery temperature, with a correlation coefficient of −0.789. The maximum temperature of the battery module is effectively limited to 30.07 °C, while the flow pressure drop is only 799.58 Pa, achieving an excellent balance between heat dissipation efficiency and energy consumption.
Ma et al. (Wed,) studied this question.