用于加速电动汽车电池外壳耐撞性能设计的概率代理模型
Probabilistic Surrogate Model for Accelerating the Design of Electric Vehicle Battery Enclosures for Crash Performance
AI总结：
本文提出基于高通量有限元仿真和高斯过程回归的概率代理模型，用于加速电动汽车电池外壳耐撞性能设计，所有输出变量平均绝对百分比误差在8.08%以内。
摘要：
本文提出了一种概率代理模型，用于加速电动汽车电池外壳的设计，重点关注耐撞性能。该研究整合了高通量有限元仿真和高斯过程回归（Gaussian Process Regression），开发出一个代理模型，能够高精度地预测碰撞参数，同时提供不确定性估计。该模型使用来自热成型和碰撞仿真的数据训练，覆盖了一系列材料和工艺参数。针对新仿真数据的验证表明，该模型具有预测准确性，所有输出变量的平均绝对百分比误差均在8.08%以内。此外，蒙特卡洛不确定性传播研究揭示了输入变异性对输出的影响。结果凸显了高斯过程回归模型在捕捉数据集中复杂关系方面的有效性，为复合材料电池外壳的设计优化提供了一种稳健且高效的工具。
英文摘要：
This paper presents a probabilistic surrogate model for the accelerated design of electric vehicle battery enclosures with a focus on crash performance. The study integrates high-throughput finite element simulations and Gaussian Process Regression to develop a surrogate model that predicts crash parameters with high accuracy while providing uncertainty estimates. The model was trained using data generated from thermoforming and crash simulations over a range of material and process parameters. Validation against new simulation data demonstrated the model's predictive accuracy with mean absolute percentage errors within 8.08% for all output variables. Additionally, a Monte Carlo uncertainty propagation study revealed the impact of input variability on outputs. The results highlight the efficacy of the Gaussian Process Regression model in capturing complex relationships within the dataset, offering a robust and efficient tool for the design optimization of composite battery enclosures.
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