A well-designed battery thermal management system (BTMS) can achieve optimal cooling performance with less power consumption than a poorly-designed system. However, it is difficult to use the computational fluid dynamics (CFD) method to perform an effective and optimal design of BTMSs when there are several structural design parameters and multiple evaluation criteria. In this paper, instead of CFD, a compound surrogate model based on the mixture of experts (MoE) method is developed to accurately approximate the BTMS performance of different structural configurations. Then, the multiple criteria evaluation of the structural design is transformed into a multiobjective optimization (MOO) problem, which is solved by the nondominated sorting genetic algorithm II (NSGA-II). To address the nonuniqueness of the optimal solutions and the contradiction between evaluation criteria, the entropy weight method (EWM) and criteria importance through the intercriteria correlation (CRITIC) method are applied to analyze the weight of each evaluation criterion. Finally, the optimal structural parameters are obtained for the corresponding weights. The results show that the surrogate-based MOO can find a structural design that meets expectations, and this approach can provide guidelines for the design of BTMSs.

与设计不佳的系统相比，设计良好的电池热管理系统 (BTMS) 可以以更少的功耗实现最佳冷却性能。然而，当存在多个结构设计参数和多个评估标准时，很难使用计算流体动力学 (CFD) 方法对 BTMS 进行有效和优化的设计。在本文中，代替 CFD，开发了一种基于混合专家 (MoE) 方法的复合代理模型，以准确近似不同结构配置的 BTMS 性能。然后，将结构设计的多准则评估转化为多目标优化（MOO）问题，由非支配排序遗传算法II（NSGA-II）解决。针对最优解的非唯一性和评价标准之间的矛盾，采用熵权法（EWM）和标准间相关性（CRITIC）方法对各评价标准的权重进行分析。最后，得到相应权重的最优结构参数。结果表明，基于代理的MOO可以找到符合预期的结构设计，这种方法可以为BTMS的设计提供指导。