Multi-objective optimization design of a thermal management system for large-capacity energy storage batteries coupling liquid cooling, phase change material and bio-inspired fins

With the increasing deployment of large-capacity energy-storage batteries, improving the thermal performance of their thermal management systems has become increasingly important. However, a single cooling method struggles to meet the stringent requirements of these systems. In this study, a 280 Ah large-capacity energy storage battery is selected as the research object to construct a hybrid thermal management system that couples liquid cooling, phase change material (PCM), and bio-inspired fins. In addition, a multi-objective optimization framework is developed to optimize the thermal performance of the proposed system. After the numerical model was validated against charge-discharge experiments, the effects of fin quantities on temperature evolution, PCM liquid fraction, and the overall battery temperature distribution were analyzed to clarify the associated heat-transfer mechanisms. The results indicate that the Fin-9 configuration exhibits the optimal performance. Compared to the pure PCM cooling thermal management configuration, its maximum temperature and temperature difference are reduced by 2.45 K and 1.75 K, respectively. The results suggest that the fins provide additional conductive heat-transfer paths and suppress the upward migration of liquid PCM, thereby alleviating PCM temperature stratification and improving the battery temperature uniformity. Building upon this, response surface methodology and three multi-objective optimization algorithms are employed to conduct an optimization design of the thermal management system parameters. Based on the IGD, SP, and HV metrics obtained from 20 independent runs, NSGA-III was selected to generate the final Pareto solution set. The results demonstrate that, compared to the original configuration, the maximum battery temperature and temperature difference after the optimization design are reduced by 2.10 K and 1.36 K, respectively. Furthermore, compared to the pure PCM cooling configuration, these metrics are reduced by 4.55 K and 3.11 K, respectively. This effectively validates the efficacy of the multi-objective optimization framework formulated in this study, providing reference for the optimized design of thermal management technologies for large-capacity energy storage batteries.