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

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

清华大学智能绿色车辆与交通全国重点实验室，北京 100084

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
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- 文章访问数: 30
- HTML全文浏览量: 7
- PDF下载量: 2
- 被引次数: 0
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

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

National Key Laboratory of Intelligent Green Vehicles and Transportation, Tsinghua University, Beijing 100084, China

摘要

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。
- 电池包 /
- 碰撞安全性 /
- 侧面柱碰撞 /
- 机器学习 /
- 有限元仿真
Abstract: The battery pack of electric vehicles is highly susceptible to failure under side pole collision. To accurately and quickly evaluate the safety of battery packs under such conditions, this paper introduces a local region refined battery pack model that can effectively characterize the deformation and mechanical response of the jellyroll of battery. Simulation analyses were conducted under varying impact velocity, angles, positions, and vehicle loading configuration, with the latter achieved by uniformly applying mass compensation to the side wall of the battery pack. A simulation matrix was designed using an optimized Latin hypercube sampling (LHS) strategy, and a dataset was generated through image recognition methods. This dataset includes parameters such as the maximum intrusion depth, intrusion location, intrusion width of the battery pack side wall, and the deformation of the jellyroll of battery. New features, including collision energy and velocity components in the x and y directions, were derived and selected as input features for model training through correlation analysis. Support vector machine (SVM), random forest (RF), and back propagation neural networks (BPNN) were employed to build a data-driven predictive model. The SVM model demonstrated superior performance, achieving an average R² of 0.96 across prediction parameters. The prediction of the maximum intrusion depth of the battery pack side wall was particularly accurate, with an R² exceeding 0.95 for all three models. Additionally, the robustness of the models was tested by introducing Gaussian noise, where the BP neural network exhibited better robustness. Even with the addition of Gaussian noise with a standard deviation of 0.5, the BP model maintained an average R² of 0.91 for the prediction parameters. The established data-driven model can effectively predict mechanical response of battery packs under side pole collisions and provide a reliable tool for evaluating battery pack safety.
- battery pack /
- crash safety /
- side pole collision /
- machine learning /
- finite element simulation

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图 1 仿真模型和设置

Figure 1. Simulation model and configuration

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图 2 不同电池模组模型

Figure 2. Different types of battery model module

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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

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图 4 电池包侧面柱碰撞仿真结果

Figure 4. Simulation result of battery pack side pole collision

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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

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图 6 Pearson相关系数分析热力图

Figure 6. Pearson correlation coefficient analysis heat map

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图 7 数据驱动模型建立流程图

Figure 7. Data-driven model building flowchart

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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

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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

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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

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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

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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
	控制树的最大深度	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	控制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	隐藏层	(50,50)
BP神经网络	线性激活函数	‘identity’

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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	R²	MSE	RMSE	MAE	MAPE
随机森林	X_max	0.9312	92.47	9.616	7.352	1.009
	I₁	0.9569	33.02	5.747	4.594	5.043
	W	0.9440	589.6	24.28	17.63	4.312
	I₂	0.9540	3.954	1.989	1.338	130.5
支持向量机	X_max	0.9640	48.38	6.955	5.195	0.7235
	I₁	0.9839	12.33	3.512	2.556	2.946
	W	0.9373	659.8	25.69	18.73	4.656
	I₂	0.9636	3.127	1.768	1.416	306.4
BP神经网络	X_max	0.9678	43.27	6.578	5.233	0.7151
	I₁	0.9694	23.46	4.843	3.826	4.110
	W	0.9483	544.4	23.33	18.55	4.424
	I₂	0.8849	9.898	3.146	2.573	591.2

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表 3 为所有训练集数据添加噪声（σ=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 (σ=0.5) to all training data

机器学习模型	预测参数	R²	MSE/mm	RMSE/mm	MAE/mm	MAPE/mm
随机森林	X_max	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	X_max	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	X_max	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

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