Textual sentiment classification incorporating dual emoji attention mechanisms

The battery pack of electric vehicles is highly susceptible to failure and may catch fire under side pole collision. To accurately and rapidly 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 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 x and y directions, were derived and selected as input features for model training through correlation analysis. Support vector machine (SVM), random forest (RF) method, and back propagation neural network (BPNN) machine learning method were employed to build a data-driven predictive model. The SVM model demonstrated superior performance, achieving an average determination coefficient 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 Gaussian noise, where the BPNN exhibited better robustness. The BP model maintained an average R² of 0.91 for the prediction parameters when Gaussian noise with a standard deviation of 0.5. The established data-driven model can effectively predict the mechanical response of battery packs under side pole collisions and provide a reliable tool for evaluating the battery pack safety.