基于时空卷积神经网络的锂电池内部老化状态估计OA

In low-temperature environments,the electrochemical reaction kinetics of lithium-ion batteries become hindered,leading to accelerated capacity decay and increased internal resistance,which severely impacts their lifespan and safety.To achieve non-destructive estimation of internal aging,a battery internal aging state estimation method based on a spatio-temporal convolutional neural network(ST-CNN)was proposed.Firstly,in-situ analysis techniques examined the battery's incremental capacity(IC)and differential voltage(DV)curves to calculate quantitative parameters for three aging modes:loss of active material(LAM),loss of lithium inventory(LLI),and loss of conductivity(LC).Secondly,a quantitative characterization system for internal aging was established by extracting features from material morphology changes and electrochemical impedance spectroscopy(EIS).Thirdly,a temporal-spatial feature modeling framework based on ST-CNN was designed to accurately map complex internal degradation mechanisms.Finally,the proposed model was validated using experimental data from low-temperature conditions.Experimental results demonstrate that this method achieves high-precision aging state estimation across multiple low-temperature conditions:MAE≤1.3%,RMSE≤6.1%,and R2≥0.99.These findings offer novel insights for battery management system lifespan prediction and safety management.