Multi-objective optimization of heat transfer performance of immersion-cooling battery thermal management system based on the shunt structure

Aiming at the local high-temperature issues (caused by unequally distributed streamline) and heat accumulation in the downstream outlet section faced by immersion-cooling battery thermal management systems (BTMS), this research proposes a shunt structure, which can optimize the spatial distribution and flow distribution of the coolant. Specifically, a battery module composed of 12 prismatic 25 Ah batteries was established. Under a discharge rate of 3C, the effects of battery spacing, pipe diameter , aperture ratio , and coolant flow rate on the highest temperature ( T max ), maximum temperature difference ∆ T max , and pressure drop ∆ P of the BTMS were investigated. The influence mechanism of factor interactions on the thermal performance of the BTMS was qualitatively analyzed using Response Surface Method (RSM). Quantitative multi-objective optimization was performed using Back Propagation Neural Network (BPNN), Non-dominated Sorting Genetic Algorithm II (NSGA-II), and the Entropy Weight-TOPSIS method, to maximize thermal performance and minimize flow pressure drop. The results show that the BPNN achieved a correlation coefficient R of 0.999998 and a mean squared error (MSE) of 0.039265. According to the entropy weight method, the weights assigned to T max , ∆ T max and ∆ P are 26.33 %, 31.08 %, and 42.60 %, respectively. Under the optimal solution ( Optimal TOPSIS ), compared to the conventional structure (without shunt), the BTMS exhibited a 5.31 K (1.69 %) reduction in T max , a 34.11 % reduction in maximum temperature rise, and a 9.66 K (81.04 %) reduction in ∆ T max , with only a 1.26 Pa (0.76 %) increase in ∆ P . Additionally, the battery spacing is reduced from 6.00 mm to 4.01 mm, enhancing module compactness by 7.53 %. • An immersion battery thermal management system with a shunt structure is proposed. • The spatial distribution and flow distribution of the coolant is optimized. • BPNN and NSGA-II algorithm are coupled to obtain 140 sets of Pareto solutions. • The balance between thermal performance and flow pressure drop is realized. • Max temperature rise and temperature difference are reduced by 34.11 % and 81.04 %.