Search Results

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.

Lithium-ion battery systems have been used as the main power source for electric vehicles due to their lightweight and high energy density. The impact safety of these battery systems has been a primary issue. In this work, the crashworthiness design of a typical vehicle battery module is implemented through numerical (finite element) simulations integrated with machine learning algorithms (decision trees). The module with multiple layered porous cells is modeled with a simplified, homogeneous material law, and subjects to the impact of a cylindrical indenter. The main protective component on the module - cover plate is designed as an energy absorbing sandwich structure with a core of cellular solids. Large scale simulations are conducted with various design variable values for the sandwich structure, and the results form a design (simulation) dataset.

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.

Due to the variation of compartment design and occupant’s posture in self-driving cars, there is a new and major challenge for occupant protection. In particular, the studies on occupant restraint systems used in the self-driving car have been significantly delayed compared to the development of the autonomous technologies. In this paper, a numerical study was conducted to investigate the effectiveness of three typical restraint systems on the driver protection in three different scenarios.

Lithium-ion (Li-ion) batteries are considered as one of the solutions for electric vehicles (EV) in the automotive industry due to their lightweight and high energy density. Their mechanical performance is of great importance for EV crashworthiness design. In this study, quasi-static and dynamic indentation tests were conducted on commercially available vehicle battery pouch cells to investigate their structural integrity. Three indenters, namely, a 19.1 mm (3/4 in.) diameter flat end (FE), a 25.4 mm (1 in.) diameter hemispherical (LH), and a 12.7 mm (1/2 in.) diameter hemispherical (SH), were used to investigate the punch force-deflection responses of the cells. Loading velocity varied in the range of 0.06 mm/s to 3 m/s to test the strain rate effect. Simplified closed-form analysis solutions were developed to predict the pouch cell force-deflection response by considering the effect of compression, tension, and shear of the battery component materials.

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.

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.

Numerical models of Hybrid III had been widely used to study the effect of underbody blast loading on lower extremities. These models had been primarily validated for automotive loading conditions of shorter magnitude in longer time span which are different than typical blast loading conditions of higher magnitude of shorter duration. Therefore, additional strain rate dependent material models were used to validate lower extremity of LSTC Hybrid III model for such loading conditions. Current study focuses on analyzing the mitigating effect of combat boots in injury responses with the help of validated LSTC Hybrid III model. Numerical simulations were run for various impactor speeds using validated LSTC Hybrid III model without any boot (bare foot) and with combat boot.