Regression of Quasi-Static and Dynamic Bottom Crush on Electric Vehicles Battery Pack Bottom Strike

Current studies about battery pack bottom strike usually focus on one test condition individually. To study the relation between quasi-static and dynamic crush in battery pack bottom strike, the paper combined quasi-static crush result and dynamic strike preset kinetic energy value with the same displacement damage on the battery pack bottom plate and cell. First, based on the finite element model of the battery pack, the quasi-static crush is applied. Several dynamic crush tests with different initial kinetic energy sets are also introduced. Then based on the same displacement damage, the pressure in quasi-static and kinetic energy in dynamic conditions are summarized. Fitting methods including polynomial regression, support vector regression (SVR), extreme learning machine (ELM), multilayer perceptron (MLP), Gaussian process regression (GPR), and K-nearest neighbor (KNN) regression are used to study the relation between the two different test load. The result shows that they have a strong relation. Compared with the case test of GPR and KNN, polynomial regression with a degree of 4 could be the best choice to predict the dynamic load value from quasi-static results globally for bottom plate and degree of 3 for cell.