数据驱动的动力电池包侧面柱碰撞安全性预测方法

马骋浩 庄梓傲 SHINJonghyeon 邢伯斌 夏勇 周青

马骋浩, 庄梓傲, SHINJonghyeon, 邢伯斌, 夏勇, 周青. 数据驱动的动力电池包侧面柱碰撞安全性预测方法[J]. 爆炸与冲击. doi: 10.11883/bzycj-2024-0318
引用本文: 马骋浩, 庄梓傲, SHINJonghyeon, 邢伯斌, 夏勇, 周青. 数据驱动的动力电池包侧面柱碰撞安全性预测方法[J]. 爆炸与冲击. doi: 10.11883/bzycj-2024-0318
MA Chenghao, ZHUANG Ziao, SHIN Jonghyeon, XING Bobin, XIA Yong, ZHOU Qing. Data-driven safety prediction of battery pack under side pole collision[J]. Explosion And Shock Waves. doi: 10.11883/bzycj-2024-0318
Citation: MA Chenghao, ZHUANG Ziao, SHIN Jonghyeon, XING Bobin, XIA Yong, ZHOU Qing. Data-driven safety prediction of battery pack under side pole collision[J]. Explosion And Shock Waves. doi: 10.11883/bzycj-2024-0318

数据驱动的动力电池包侧面柱碰撞安全性预测方法

doi: 10.11883/bzycj-2024-0318
详细信息
    作者简介:

    马骋浩(2002- ),男,博士研究生,mch23@mails.tsinghua.edu.cn

    通讯作者:

    夏 勇(1976- ),男,博士,副研究员,xiayong@tsinghua.edu.cn

  • 中图分类号: O383; U469.72

Data-driven safety prediction of battery pack under side pole collision

  • 摘要: 电动汽车电池包在侧面柱碰撞下极易失效并可能发生着火。为准确、快速地评估电池包在侧面柱碰撞下安全性,本文采用了区域细化的电池包模型,在不同的碰撞速度、碰撞角度、碰撞位置,车辆装载状态下开展仿真分析,采用了优化拉丁超立方采样策略设计了仿真矩阵,并通过图像识别的方法批量提取电池包碰撞响应生成数据集。对研究参数进行组合生成了新特征,并对参数进行相关性分析确定了模型训练的输入特征。采用了支持向量机(Support vector machine, SVM)、随机森林方法(random forest, RF)和误差反向传播神经网络机器学习(back propagation neural networks, BPNN)方法建立了数据驱动的预测模型。结果表明,支持向量机模型性能最优,模型预测参数的平均决定系数R2为0.96。为训练数据集引入标准差不同的高斯噪声,以对模型鲁棒性进行检验,BPNN的鲁棒性较优。建立的数据驱动模型能预测电池包侧面柱碰撞下的变形情况,评估电池包碰撞安全性。
  • 图  1  仿真模型和设置

    Figure  1.  Simulation model and configuration

    图  2  不同电池模组模型

    Figure  2.  Different types of battery model module

    图  3  优化拉丁超立方采样生成的仿真矩阵样本分布

    Figure  3.  Sample distribution of simulation matrix generated by optimized Latin hypercube sampling

    图  4  电池包侧面柱碰撞仿真结果

    Figure  4.  Simulation result of battery pack side pole collision

    图  5  电池包仿真结果后处理示意图

    Figure  5.  Schematic diagram of post-processing of battery pack simulation results

    图  6  Pearson相关系数分析热力图

    Figure  6.  Pearson correlation coefficient analysis heat map

    图  7  数据驱动模型建立流程图

    Figure  7.  Data-driven model building flowchart

    图  8  随机森林模型预测结果与仿真结果的比较

    Figure  8.  Comparison between RF model prediction results and simulation results

    图  9  支持向量机模型预测结果与仿真结果比较图

    Figure  9.  Comparison between SVM model prediction results and simulation results

    图  10  BP神经网络模型预测结果与仿真结果比较图

    Figure  10.  Comparison between BPNN model prediction results and simulation results

    图  11  增加高斯噪声后机器学习模型预测效果

    Figure  11.  Machine learning model prediction after adding Gaussian noise

    表  1  机器学习模型超参数组合

    Table  1.   Machine learning model hyperparameter selection

    算法超参数取值
    随机森林随机森林中树的数量100
    树的最大深度None
    分割内部节点所需的最小样本数2
    叶子结点上所需的最小样本数1
    支持向量机回归模型的惩罚程度100
    误差惩罚边界0.5
    核函数‘rbf’
    BP神经网络各隐藏层中的神经元数目(50,50)
    激活函数‘identity’
    下载: 导出CSV

    表  2  机器学习模型预测效果

    Table  2.   Accuracy analysis of ML models

    机器学习模型预测参数R2MSE/mm2RMSE/mmMAE/mmMAPE/%
    随机森林Xmax0.931292.479.6167.3521.009
    I10.956933.025.7474.5945.043
    W0.9440589.624.2817.634.312
    I20.95403.9541.9891.338130.5
    支持向量机Xmax0.964048.386.9555.1950.7235
    I10.983912.333.5122.5562.946
    W0.9373659.825.6918.734.656
    I20.96363.1271.7681.416306.4
    BP神经网络Xmax0.967843.276.5785.2330.7151
    I10.969423.464.8433.8264.110
    W0.9483544.423.3318.554.424
    I20.88499.8983.1462.573591.2
    下载: 导出CSV

    表  3  为所有训练集数据添加噪声($\sigma=0.5 $)后各类机器学习模型预测效果

    Table  3.   Accuracy analysis of ML models after adding Gaussian noise ($\sigma=0.5 $) to all training data

    机器学习模型 预测参数 R2 MSE/mm2 RMSE/mm MAE/mm MAPE/%
    随机森林 Xmax 0.9171 111.3973 10.5545 8.7621 1.2081
    I1 0.8941 81.1519 9.0084 7.5526 8.5254
    W 0.8498 1580.4298 39.7546 32.2293 7.5761
    I2 0.9358 5.5154 2.3485 1.6952 179.2652
    支持向量机 Xmax 0.9097 121.4039 11.0183 8.3659 1.1563
    I1 0.9286 54.7504 7.3993 5.7387 6.2406
    W 0.8989 1063.2993 32.6083 24.4519 5.935
    I2 0.8638 11.7067 3.4215 2.8056 732.7073
    BP神经网络 Xmax 0.9308 92.951 9.6411 7.9584 1.0837
    I1 0.9415 44.8129 6.6942 5.524 6.1102
    W 0.9084 964.28 31.0529 24.8844 5.8305
    I2 0.8673 11.4063 3.3773 2.8312 575.6947
    下载: 导出CSV
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出版历程
  • 收稿日期:  2024-08-31
  • 修回日期:  2024-10-16
  • 网络出版日期:  2024-10-18

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