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

MA Chenghao; ZHUANG Ziao; SHIN Jonghyeon; XING Bobin; XIA Yong; ZHOU Qing

doi:10.11883/bzycj-2024-0318

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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

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

doi: 10.11883/bzycj-2024-0318

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

Received Date: 2024-08-31
Rev Recd Date: 2024-10-16

Available Online: 2024-10-18

Abstract

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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References(21)

References

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