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Lithium-ion batteries serve as the main power source for contemporary electric vehicles. Safeguarding these batteries against damage is paramount, as it can trigger accelerated performance deterioration, potential fire hazards, environmental threats, and more. This study explores damage progression of a commercial vehicle lithium-ion battery module containing prismatic cells under indentation crush loading. We employed computational simulations of mechanical loading tests to investigate this behavior. Physical tests involved subjecting modules to low-speed (0.05 m/s) indentations using a V-shaped stainless-steel wedge, under six unique loading conditions. During the tests, force, and voltage change with wedge displacement were monitored. Utilizing experimental insights, we constructed a finite element model, which included key components of the battery module, such as the prismatic cells, steel frames, and various plastic parts.

Lithium-ion batteries (LIBs) have been used as the main power source for Electric vehicles (EVs) in recent years. The mechanical behavior of LIBs subject to crush loading is crucial in assessing and improving the impact safety of battery systems and EVs. In this work, a detailed 3D finite element model for a commercial vehicle battery was built, in order to better understand battery failure behavior under various loading conditions. The model included the major components of a prismatic battery jellyroll, i.e., cathodes, anodes, and separators. The models for these components were validated against the corresponding material coupon tests (e.g., tension and compression). Then the components were integrated into the cell level model for simulation of jellyroll loading and damage behavior under three types of compressive indenter loading: (1) Flat-end punch, (2) Hemispherical punch and (3) Round-edge wedge. The comparisons showed reasonable agreement between modeling and experiments.

Cellular solids are excellent energy absorbers and widely applied in the automotive passive safety area. Their microstructures offer the ability to undergo large plastic deformation at nearly constant nominal stress and thus can absorb a large amount of kinetic energy before collapsing to a more stable configuration or fracture. To further improve their performance, it is imperative to develop a systematic design method, to tailor microstructures’ behavior by adjusting their geometric parameters, especially for those with irregular, random shapes. In this research, we proposed a machine learning based method, which combines the finite element (FE) analysis to design open cell foams for crash energy absorption. The foam geometry is generated utilizing a large number of core points and convex polygons, known as the Voronoi diagram, and then converted to the FE model to compute the plateau stress under crush loading.

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.

Spring-back phenomena are critical in stamping procedures for advanced high strength steel. An analytical model is developed to predict the spring-back effect for a U-channel part with post-stretching process. The stress distribution is obtained by direct application of material constitutive relationship. The subjected loading conditions are sequentially bending, (un-bending), and uniform stretching, based on different zones in the part. Both the loading history and the friction effects are considered in the model. The bending moments are obtained to generate a theoretical spring-back shape. Great performance in spring-back control is achieved by applying certain high level of external forces. FE simulation is conducted for the identical stamping process with post-stretching. Good correlation is observed between the analytical and numerical solutions/experimental results under various scenarios.

Twist spring-back would interfere with stamping or assembling procedures for advanced high strength steel. A “homeopathic” resolution for controlling the twist spring-back is proposed using unbalanced post-stretching configuration. Finite element forming simulation is applied to evaluate and compare the performance for each set of unbalanced post-stretching setup. The post-stretching is effectuated by stake bead application. The beads are separated into multiple independent segments, the height and radii of which can be adjusted individually and asymmetrically. Simulation results indicate that the twist spring-back can be effectively controlled by reducing the post-stretching proximate to the asymmetric part area. Its mechanism is qualitatively revealed by stress analyses, that an additional but acceptable cross-sectional spring-back re-balances the sprung asymmetrical geometry to counter the twist effect.

To help predict the injury responses of child pedestrians and occupants in traffic incidents, finite element (FE) modeling has become a common research tool. Until now, there was no whole-body FE model for 10-year-old (10 YO) children. This paper introduces the development of two 10 YO whole-body pediatric FE models (named CHARM-10) with a standing posture to represent a pedestrian and a seated posture to represent an occupant with sufficient anatomic details. The geometric data was obtained from medical images and the key dimensions were compared to literature data. Component-level sub-models were built and validated against experimental results of post mortem human subjects (PMHS). Most of these studies have been mostly published previously and briefly summarized in this paper. For the current study, focus was put on the late stage model development.

This paper represents the development of a new design methodology based on data mining theory for decision making in vehicle crashworthy components (or parts) development. The new methodology allows exploring the big crash simulation dataset to discover the underlying complicated relationships between vehicle crash responses and design variables at multi-levels, and deriving design rules based on the whole vehicle safety requirements to make decisions towards the component and sub-component level design. The method to be developed will resolve the issue of existing design approaches for vehicle crashworthiness, i.e. limited information exploring capability from big datasets, which may hamper the decision making and lead to a nonoptimal design. A preliminary design case study is presented to demonstrate the performance of the new method. This method will have direct impacts on improving vehicle safety design and can readily be applied to other complex systems.

Anthropometric test devices (ATDs), such as the Hybrid III crash-test dummy, have been used to simulate lower-extremity responses to military personnel subjected to loading conditions from anti-vehicular (AV) landmine blasts. Numerical simulations [e.g., finite element (FE) analysis] of such high-speed vertical loading on ATD parts require accurate material parameters that are dependent on strain rate. This study presents a combined experimental and computational study to calibrate the rate-dependent properties of three materials on the lower extremities of the Hybrid III dummy. The three materials are heel-pad foam, foot skin, and lower-leg flesh, and each has properties that can affect simulation results of forces and moments transferred to the lower extremities.

As early as the 1950's, Gurdjian and colleagues (Gurdjian et al., 1955) observed that brain injuries could occur by direct pressure loading without any global head accelerations. This pressure-induced injury mechanism was "forgotten" for some time and is being rekindled due to the many mild traumatic brain injuries attributed to blast overpressure. The aim of the current study was to develop a finite element (FE) model to predict the biomechanical response of rat brain under a shock tube environment. The rat head model, including more than 530,000 hexahedral elements with a typical element size of 100 to 300 microns was developed based on a previous rat brain model for simulating a blunt controlled cortical impact. An FE model, which represents gas flow in a 0.305-m diameter shock tube, was formulated to provide input (incident) blast overpressures to the rat model. It used an Eulerian approach and the predicted pressures were verified with experimental data.