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Lithium-ion (or Li-ion) battery systems are being increasingly used as the main power source in new generation hybrid and electric vehicles. Their mechanical integrity under abuse loading conditions is very important for vehicle safety design. In this research, a computational study was performed to simulate mechanical tests on vehicle battery cells and modules. The tests were conducted on commercial Li-ion battery cells and entire modules at low speed using a high capacity material testing system. Based on loading and boundary conditions during the tests, finite element (FE) models using the explicit FEA solver LS-DYNA, were developed. The model predictions demonstrated reasonable agreement in terms of failure modes and force-displace response at both cell and module levels.

A new design methodology based on data mining theory has been proposed and used in the vehicle crashworthiness design. The method allows exploring the big dataset of crash simulations to discover the underlying complicated relationships between response and design variables, and derive design rules based on the structural response to make decisions towards the component design. An S-shaped beam is used as an example to demonstrate the performance of this method. A large amount of simulations are conducted and the results form a big dataset. The dataset is then mined to build a decision tree. Based on the decision tree, the interrelationship among the geometric design variables are revealed, and then the design rules are derived to produce the design cases with good energy absorbing capacity. The accuracy of this method is verified by comparing the data mining model prediction and simulation data.