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Pareto排序遗传算法及响应面优化在纯电车电池壳体参数设计的工程应用

高媛媛 刘娜 刘鹏 王成诺

高媛媛,刘娜,刘鹏, 等. Pareto排序遗传算法及响应面优化在纯电车电池壳体参数设计的工程应用[J]. 机械科学与技术,2024,43(5):819-831 doi: 10.13433/j.cnki.1003-8728.20220283
引用本文: 高媛媛,刘娜,刘鹏, 等. Pareto排序遗传算法及响应面优化在纯电车电池壳体参数设计的工程应用[J]. 机械科学与技术,2024,43(5):819-831 doi: 10.13433/j.cnki.1003-8728.20220283
GAO Yuanyuan, LIU Na, LIU Peng, WANG Chengnuo. Engineering Application of Pareto Sorting Genetic Algorithm in Parameter Design of Battery Case of Pure Electric Car[J]. Mechanical Science and Technology for Aerospace Engineering, 2024, 43(5): 819-831. doi: 10.13433/j.cnki.1003-8728.20220283
Citation: GAO Yuanyuan, LIU Na, LIU Peng, WANG Chengnuo. Engineering Application of Pareto Sorting Genetic Algorithm in Parameter Design of Battery Case of Pure Electric Car[J]. Mechanical Science and Technology for Aerospace Engineering, 2024, 43(5): 819-831. doi: 10.13433/j.cnki.1003-8728.20220283

Pareto排序遗传算法及响应面优化在纯电车电池壳体参数设计的工程应用

doi: 10.13433/j.cnki.1003-8728.20220283
基金项目: 山东省高等教育科技计划(J18KA006)与交通运输行业车辆测试、诊断与维护技术关键实验室开放基金项目(JTZL2004)
详细信息
    作者简介:

    高媛媛,硕士研究生,531986117@qq.com

    通讯作者:

    刘娜,副教授,硕士生导师,liuna_sd@sdjzu.edu.cn

  • 中图分类号: U469.72

Engineering Application of Pareto Sorting Genetic Algorithm in Parameter Design of Battery Case of Pure Electric Car

  • 摘要: 为解决三元锂电池壳厚重与应力集中的问题。本文利用拉丁超立方法对变密度拓扑优化后参数抽样,利用了Pareto排序多目标遗传算法,选择应力等作为目标函数进行迭代计算。本文利用克里格空间插值法得到各响应图谱以及初步预测的优化方案。基于上述结果利用Design-Expert建立了62组响应面数学模型,计算所得帕累托最优为70%,p-value≤0.0001,证明响应面模型的准确性,经仿真计算优化后的结构较原模型各方面性能都有所提升,具有较好的可靠性。
  • 图  1  电池壳体与简化后等效模型

    Figure  1.  Battery shell and the simplified equivalent model

    图  2  拓扑优化结果及其收敛曲线

    Figure  2.  Topology optimization results and their convergence curves

    图  3  拓扑结构优化结构以及静力分析

    Figure  3.  Topological structure optimization structure and hydrodynamic analysis

    图  4  设计变量对应位置

    Figure  4.  Corresponding position of the design variables

    图  5  电池壳体新结构

    Figure  5.  New structure of the battery shell

    图  6  拟合优度

    Figure  6.  The goodness of fit

    图  7  响应图谱以及收敛性

    Figure  7.  Response profiles as well as convergence

    图  8  计算迭代图

    Figure  8.  Computational iteration graph

    图  9  优化结果

    Figure  9.  Optimization results

    图  10  颠簸路面急转弯工况时应力与位移云图

    Figure  10.  Stress and displacement cloud diagram during sharp turn condition of bumpy road surface

    图  11  电池壳体原应力云图以及位移云图

    Figure  11.  Battery case original stress cloud map and displacement cloud map

    图  12  共62组数据样本分布

    Figure  12.  Distribution of 62 data samples

    图  13  质量响应图

    Figure  13.  Mass response plot

    图  14  频率响应图

    Figure  14.  Frequency response diagram

    图  15  优化结果

    Figure  15.  The optimization results

    图  16  有限元计算结果

    Figure  16.  Finite element calculation results

    表  1  尺寸参数取值区间

    Table  1.   Value val of dimensional parameters

    参数名称取值范围
    下壳体底板厚度T1/mm1 ~ 5
    后板厚度T2/mm1 ~ 5
    上壳体厚度T3/mm3 ~ 5
    吊耳(1 ~ 4)厚度L1 ~ L4/mm7 ~ 9
    下载: 导出CSV

    表  2  预测结果与仿真对比

    Table  2.   Comparof prediction results and simulation

    质量/
    kg
    应力/
    MPa
    应力位移/
    mm
    频率/
    Hz
    有限元仿真 200.58 114.52 1.2755 62.619
    遗传算法预测 199.91 97.27 1.7851 54.094
    误差/% −0.35 −14.60 + 11.70 −12.90
    下载: 导出CSV

    表  3  质量响应

    Table  3.   Quality response

    Source Sum of squares Mean square F-value p-value
    Model 3847.78 137.42 698.90 <0.0001
    下载: 导出CSV

    表  4  频率响应

    Table  4.   Frequency responses

    Source Sum of squares Mean square F-value p-value
    Model 3771.02 137.42 45.11 <0.0001
    下载: 导出CSV

    表  5  预测结果与仿真对比

    Table  5.   Comparison of prediction results with simulation results

    参数 质量/kg 频率/Hz
    有限元仿真 190.720 91.824
    响应面预测 191.079 91.897
    误差/% −0.19 0
    下载: 导出CSV
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出版历程
  • 收稿日期:  2022-03-24
  • 刊出日期:  2024-05-31

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