首页|基于多目标粒子群优化算法的动力电池仿生冷板结构优化设计

基于多目标粒子群优化算法的动力电池仿生冷板结构优化设计

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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。
Optimal Design of Bionic Cold Plate Structure of Power Battery Based on MOPSO
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.

Network channel cold plateSingle factor analysisMulti-Objective Particle Swarm Optimization(MOPSO)algorithmOptimal Latin hypercube samplingEntropy weight method

张荃、张春化、康渝佳

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长安大学,西安 710018

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

陕西省重点研发计划

2019ZDLGY15-07

2024

汽车技术
中国汽车工程学会 长春汽车研究所

汽车技术

CSTPCD北大核心
影响因子:0.522
ISSN:1000-3703
年,卷(期):2024.(4)
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