基于CFD与智能算法的电池组散热优化设计

Optimization design of batteries heat dissipation based on CFD and intelligence algorithm

针对电动挖掘机电池包的形状及电池排布方式,对三种散热方案进行了CFD仿真研究,并通过神经网络获得电池单体间隙与电池组最高温度的映射关系,利用遗传算法进行寻优,获得最优电池组排布。结果表明,与其他两种散热方式相比,正交风向的通风方式可以有效降低电池组最高温度,同时可以保证电池组各电池单体温度一致性,在此基础上通过神经网络及遗传算法进行电池组间隙优化,使得电池最高温度降低5 K,电池组体积减小13%,使得其排布更加合理。

Considering the shape of battery pack for electric excavator and the cell arrangement, the proposed three heat dissipation schemes were simulated by CFD. The mapping relationship between the gap of cells and the upmost temperature of batteries was obtained by neural network, and the genetic algorithm was adopted to find the optimal arrangement of batteries. It is concluded that compared with the other heat dissipation schemes, the orthogonal airflows can effectively decline the highest temperature and guarantee the consistency of batteries' temperature. The gap between cells was optimized by combining the neural network and genetic algorithm. The optimization can decline the highest temperature by 5 K and shrink its volume by 13%, and make the batteries' arrangement more reasonable.