摘要
为了提高锂离子电池的冷却效果,提出一种高度对称的仿生网状流道冷板。首先,利用单因子分析法分析了冷板结构参数对其性能的影响,然后,以冷板的平均温度、温度标准差和冷却液压力损失为性能指标,采用多目标粒子群优化(MOPSO)算法对冷板的结构参数进行了优化,得到性能最优时的流道宽度、流道深度和冷板壁厚分别为9.0 mm、1.5 mm和1.4 mm,对应的平均温度、温度标准差和压力损失分别为33.20℃、1.33℃和65.63 Pa,相比于初始结构参数,优化后的平均温度和温度标准差分别下降1.92℃和0.02℃,但压力损失增大27.10 Pa。最后,在电池模组层面验证了优化结果。
To improve the cooling effect,this paper proposed a highly symmetrical bionic network channel cold plate.It firstly analyzed the influence of the cold plate’s structure parameters on its performance through single-factor analysis,then,optimized the structure parameters of the cold plate using the Multi-Objective Particle Swarm Optimization(MOPSO)algorithm,with the average temperature,temperature standard deviation,and coolant pressure loss of the cold plate serving as performance indexes.The optimal channel width,channel depth,and cold plate wall thickness were found to be 9.0 mm,1.5 mm,and 1.4 mm respectively.The corresponding average temperature,temperature standard deviation,and pressure loss were measured as 33.20℃,1.33℃,and 65.63 Pa respectively.When compared with the initial structural parameters,the optimized mean temperature and temperature standard deviation decreased by 1.92℃and 0.02℃ respectively,while the pressure loss increased by 27.10 Pa.Finally,the optimization results were verified using the battery module.
作者
张荃
张春化
康渝佳
Zhang Quan;Zhang Chunhua;Kang Yujia(Chang’an University,Xi’an 710018)
出处
《汽车技术》
CSCD
北大核心
2024年第4期47-56,共10页
Automobile Technology
基金
陕西省重点研发计划项目(2019ZDLGY15-07)。
关键词
网状流道冷板
单因素分析
多目标粒子群优化算法
最优拉丁超立方抽样
熵权法
Network channel cold plate
Single factor analysis
Multi-Objective Particle Swarm Optimization(MOPSO)algorithm
Optimal Latin hypercube sampling
Entropy weight method
