福州大学学报（自然科学版）2026，Vol.54Issue(3)：276-283,8.DOI:10.7631/issn.1000-2243.25105
车用三元锂电池CNN-LSTM挤压力学响应预测
Prediction of compression mechanical response for automotive ternary lithium-ion batteries using CNN-LSTM
摘要
Abstract
Focusing on ternary lithium-ion batteries for vehicles,a prediction method for mechanical response under extrusion is proposed based on CNN-LSTM hybrid model.Firstly,a high-precision battery cell extrusion model is established using LS-DYNA finite element simulation software to simu-late the extrusion process under spherical and cylindrical indenters with diameters of 10,15,20 and 25 mm.Subsequently,a time-series dataset containing force-displacement curves is constructed based on simulation data.Finally,the CNN-LSTM neural network model is employed to predict battery extru-sion failure under indenter with diameter of 25 mm,with comparative analysis against standalone LSTM models.Experimental results demonstrate that the CNN-LSTM hybrid model effectively captures spatiotemporal coupling characteristics during nonlinear structural deformation,establishing a high-pre-cision and strongly generalized analytical method for predicting mechanical abuse failure in automotive ternary lithium-ion batteries.关键词
三元锂电池/力学响应特性/失效预测/卷积神经网络/长短期记忆神经网络Key words
ternary lithium battery/mechanical response characteristics/failure prediction/convolu-tional neural network/long short-term memory neural network分类
交通工程引用本文复制引用
白圆悦,黎衍,于潇雁,姚立纲..车用三元锂电池CNN-LSTM挤压力学响应预测[J].福州大学学报（自然科学版）,2026,54(3):276-283,8.基金项目
国家重点研发计划资助项目(2022YFB4702401) （2022YFB4702401）
