基于工况适应替代模型的电池液冷系统结构优化

The structure of cold plate limits the performance of liquid cooling systems. The inappropriate structure parameter of cold plate leads to a high energy loss in the liquid cooling system, large mass and volume, and insignificant temperature suppression function. Aiming at the problem of low efficiency of the structural optimization in different application scenarios, the working condition parameters are used as the input variables of the surrogate model, and a kriging surrogate model is constructed. Accurate simulation of maximum temperature, maximum temperature difference, and pressure drop are realized under any working conditions and any structural parameters. Three optimization objectives of energy loss, cold plate mass, and maximum temperature are determined, and the cold plate structure optimization under different working conditions is completed by combining the second-generation non-dominated genetic algorithm. Taking the structure parameters when the maximum temperature is the minimum value of as an example, the variation of the flow channel diameter with the working conditions is analyzed. The time required to complete structure optimization is compared, and the efficiency and convenience of the surrogate model including the working conditions is confirmed.