汽车工程学报2026，Vol.16Issue(5)：745-757,13.DOI:10.3969/j.issn.2095‒1469.2026.05.03
基于多保真度融合的电池包底部碰撞侵入量预测研究
Prediction of Battery Pack Intrusion Depth Under Underbody Impact Using Multi-Fidelity Fusion
摘要
Abstract
To address the challenges of high experimental costs and low simulation efficiency in safety assessment of electric vehicle battery packs under underbody impact,this study proposes an intrusion depth prediction method based on multi-fidelity fusion and adaptive sampling.A conical obstacle model was established through dynamic analysis,and the analytical relationship between intrusion depth and collision parameters was derived using nonlinear deformation theory.Latin hypercube sampling was employed to construct a multi-fidelity dataset,while a two-level enhanced multi-fidelity Gaussian process(EMFGP)model was developed and further optimized through active learning.Numerical validation show that,for 2D benchmark problems,the proposed method reduces the normalized root-mean-square error by 62.7%compared to conventional multi-fidelity neural networks(MFNN).In the engineering application,the model predicts intrusion depth for an 8-dimensional parameter space,including impact velocity,impact angle and underbody panel thickness,with an error of less than 5%,while requiring only 10%of the computational time of the MFNN.The results reveal that intrusion depth is positively correlated with obstacle sharpness and impact angle,and negatively correlated with underbody panel thickness,providing an efficient digital tool for the design of battery pack underbody protection.关键词
动力电池包/底部碰撞/机器学习/多保真度融合Key words
power battery pack/underbody collision/machine learning/multi-fidelity fusion分类
交通工程引用本文复制引用
鲁俊,蒋胜,杨鑫,史方圆,白芳华,毛溶洁,刘学文..基于多保真度融合的电池包底部碰撞侵入量预测研究[J].汽车工程学报,2026,16(5):745-757,13.基金项目
重庆市自然科学基金创新发展联合基金项目(CSTB2024NSCQ‐LZX0042) （CSTB2024NSCQ‐LZX0042）
