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基于Kriging模型多目标遗传算法的高功率动力电池包冷却性能研究 被引量:6

RESEARCH ON COOLING PERFORMANCE OF HIGH POWER POWER BATTERY PACK BASED ON MULTI-OBJECTIVE GENETIC ALGORITHM
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摘要 电芯温度对动力电池包性能的影响极大,为了降低某液冷型动力电池包在高倍率放电工况下的最高温度以及提升电池包的能量密度,对电池包模组进行冷却结构参数优化。首先建立了单体电芯放电发热模型和电池模组计算模型,并对电芯放电发热模型进行试验标定。接着以电芯间距和冷却液进口温度为优化变量,电池模组最高温度和体积为优化目标,最大温差、电芯间距和冷却液进口温度为约束条件,利用拉丁超立方法对优化变量进行参数化组合样本的建立,结合Kriging代理模型和多目标遗传算法对电池模组进行寻优求解,优化结果显示:相比于原始的最高温度下降了9.7%,最大温差下降了12.5%,电芯间距体积减小了7.1%。最后依照优化结果进行样件的试制并完成台架试验,优化结果与试验测试值具有良好的一致性,验证了优化方法的有效性,为提升电池包的散热性能和能量密度提供理论参考。 The temperature of the cell has a great influence on the performance of the power battery pack, in order to reduce the maximum temperature and improve the energy density of a liquid-cooled power battery pack under high rate discharge condition, the cooling structure parameters of the battery pack module were optimized. Firstly, the electricity-heat model of single cell was established and calibrated, built the battery pack calculation model based on the cell electricity-heat coupling model. Then, the cell spacing and the inlet temperature of coolant were taken as the optimization variables, the maximum temperature and volume of the battery module were taken as the optimization objectives, the maximum temperature difference, the cell spacing and the inlet temperature of coolant were taken as constraints. Used the Latin hypertaxis method to establish a parameterized combination sample for the optimized variables, combined with the Kriging model and multi-objective genetic algorithm for the optimal solution. The optimization results showed that the maximum temperature decreased by 9.7%, the maximum temperature difference decreased by 12.5%, and the cell spacing decreased by 7.1%. Finally, according to the optimization results, the prototype was trial-produced and the bench test was completed, experimental test values and calculated results have a good consistency, so as to verify the reliability of the results and which was conducive to improving the heat dissipation performance and energy density of the battery pack.
作者 童高鹏 王之丰 TONG GaoPeng;WANG ZhiFeng(Institute of Transportation y Zhejiang Industry Polytechnic College,Shaoxing 312000,China;Zhejiang Geely Automobile Research Institute Co.,Ltd.,Hangzhou 315336,China)
出处 《机械强度》 CAS CSCD 北大核心 2021年第6期1366-1372,共7页 Journal of Mechanical Strength
关键词 动力电池包 冷却性能 KRIGING模型 多目标遗传算法 Power battery pack Cooling performance Kriging model Multi-objective genetic algorithm
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