基于遗传算法的电池包高效热管理流道优化

Optimization of efficient thermal management channel for battery pack based on genetic algorithm

电池包的热管理对于避免过热和热失控等问题至关重要,必须采用主动冷却系统来保持电池的安全温度,提高电池的性能和寿命。液体冷却是一种有效的冷却方法,但是关于结构参数对冷却效果影响的参数化研究仍然缺乏。本文采用了一种基于微通道硅基冷板的液体冷却方法,采用计算流体力学方法建立流-固耦合散热模型。采用拉丁超立方法生成参数组合样本,通过多目标遗传优化方法开发出具有高效散热性能和较低能耗的冷却系统。实验结果表明,优化后的液冷系统能够有效地控制模块的温度低于45℃,单体电池间的温度偏差也可以控制在5℃的小范围内。本研究结果将为电池组件热管理系统的设计和优化提供有效的研究思路,有助于推动电池在实际产品上的应用。

The thermal management of the battery pack is essential to avoid problems such as overheating and thermal runaway.An active cooling system must be used to maintain a safe temperature of the battery and improve the performance and life of the battery.Liquid cooling is an effective cooling method,but parameterized research on the influence of structural parameters on cooling effects is still lacking.In this paper,a liquid cooling method based on a micro channel silicon-based cold plate is used,and a fluid-solid coupling heat dissipation model is established by using computational fluid dynamics.The Latin super vertical method is used to generate parameter combination samples and a cooling system with efficient heat dissipation performance and lower energy consumption is developed through a multi-objective genetic optimization method.Experimental results show that the optimized liquid cooling system is sufficient to control the temperature of the module below 45℃,and the temperature deviation between single cells can also be controlled within a small range of 2℃.The results of this study will provide effective research ideas for the design and optimization of the thermal management system of battery components,and will help promote the application of batteries in actual products.