This work presents a systematic study of the relationship between structural stochasticity and the crush energy absorption capability of lattice structures, with controlled stiffness and weight. We develop a Voronoi tessellation-based approach to generate multiple series of lattice structures with either equal weight or equal stiffness, smoothly transitioning from periodic to stochastic configurations for crush energy absorption analysis. The generated lattice series fall into two categories, originating from periodic honeycomb and diamond lattice structures. A new stochasticity metric is proposed for quantifying the structural stochasticity and is compared with the state-of-the-art stochasticity metrics to ensure a consistent measurement. The crush energy absorption properties are obtained using explicit finite element analysis and we observe similar stochasticity-property trends in simulations using both elastic-plastic and hyperelastic materials. We report a new observation that an intermediate level of stochasticity between periodic and high randomness leads to the best crush energy absorption performance. Our analysis reveals that this optimal performance arises from enhanced activation of deformation hinges, promoting efficient energy absorption.

The structural integrity of lithium-ion batteries (LIBs) under mechanical loading is critical for ensuring safe operation in electric vehicle applications. This study investigates the influence of electrochemical aging and mechanical constraints on the structural response of prismatic LIBs under indentation loading. Commercial LIBs were subjected to controlled aging protocols under both constrained and unconstrained conditions, followed by quasi-static and dynamic indentation tests. Results demonstrate that mechanical constraint during cycling significantly preserves structural integrity by limiting internal gas generation and preventing electrode delamination. Cells aged without constraint exhibited reduced stiffness and different failure characteristics after 100 cycles, while mechanically constrained cells maintained nearly identical force–displacement responses up to 200 cycles. X-ray computed tomography revealed that unconstrained aging led to substantial casing deformation and electrode-separator delamination, whereas constrained cells showed minimal structural changes. The findings provide crucial insights for battery pack design and safety assessment, highlighting the importance of appropriate mechanical constraints in maintaining both electrochemical performance and structural integrity throughout battery lifetime.

Mechanical characterization remains one of the principal efficiency-limiting steps in the materials development and deployment pipeline. The traditional paradigm, based on standardized tests using geometrically simple specimens followed by calibration and validation of pre-assumed constitutive laws, has proven remarkably effective for ensuring reproducibility and inter-laboratory comparability. However, it was not conceived for the combinatorial design spaces emerging from modern materials discovery frameworks. Each conventional test probes only a limited region of the admissible stress–strain space, whereas characterizing complex or anisotropic materials requires multiple specimens and loading configurations, increasing inter-specimen variability and compounding time and resource costs. Consequently, existing workflows remain structurally incompatible with the throughput, adaptability, and autonomy demanded by modern manufacturing and digital design systems. Recent advances in experimental and computational mechanics are reshaping what is achievable. Full-field measurement techniques now provide high-resolution full-field kinematic information per experiment, while developments in scientific computing and machine learning enable data-driven constitutive model discovery and strategies for autonomous systems. Despite progress, these components remain largely fragmented, and coherent frameworks linking experimental design, data integration, model discovery, and validation remain underdeveloped. This review identifies four interdependent pillars enabling autonomous mechanics-based materials characterization: informative experimental design using heterogeneous specimen geometries; multi-fidelity data integration for denoising, sparse reconstruction, and dimensionality reduction of full-field measurements; physics-informed constitutive model discovery enforcing thermodynamic admissibility; and closed-loop validation with adaptive feedback driven by residual model uncertainty. We outline a path toward autonomous characterization systems operating at the pace and scale required for materials engineering.

This work presents a comprehensive molecular dynamics simulation study investigating the anisotropic mechanical response and fracture mechanisms of defective Dodecanophene nanosheets, a novel two-dimensional carbon allotrope. Using the AIREBO-M reactive force field validated against Density Functional Theory (DFT) calculations, we systematically evaluate the effects of crack orientation (0°-90°), temperature-dependent behavior (200-1000 K), and pre-existing crack size (30-60 Å) on elastic modulus, tensile strength, fracture toughness, and energy absorption. The nanosheets exhibit clear anisotropy: the y-direction shows higher stiffness (562.41 GPa) and strength (148.38 GPa), while the x-direction shows superior toughness (34.53 GPa). Crack orientation plays a critical role, with perpendicular cracks causing severe degradation (48.0-54.0%) compared to moderate losses (16-24%) for parallel cracks. Temperature-dependent behavior is pronounced, as toughness rises 160% at 200 K but declines 65.0% at 1000 K. Increasing pre-existing crack length drastically reduces strength (75.0-86.0%) and toughness (79.0-86.0%). Distinct failure modes emerge: x-loading promotes ductile behavior with crack deflection and gradual bond breaking, while y-loading induces brittle catastrophic fracture with rapid crack propagation. This represents the first systematic investigation of pre-existing crack effects on Dodecanophene's fracture mechanics across extreme thermal conditions (200-1000 K), providing critical insights for defect-tolerant design of 2D carbon materials.

The safety concern stemming from the unstoppable thermal runaway (TR) of abused Li-ion batteries (LIB) remains a critical challenge for the wide deployment of electric vehicles and battery energy storage systems. Although mechanical abuse is a crucial driving factor of TR, conventional nail penetration tests cannot rank the severity of the mechanically induced TR because of its polarized Pass or Fail criterion. A single-side mechanical indentation test conducted at Oak Ridge National Laboratory provides a distinguishable failure database of large-format LIB pouch cells where the failure response varies with electrode chemistry and the state of charge (SOC). Based on this database, this work develops a new fundamental framework to comprehend the thermo-electrochemical dynamics and quantify the TR risk of LIBs subjected to mechanical indentation tests. The developed framework bridges the gap between experimentally measured cell-level voltage and temperature response and electrode-level self-discharge process via the internal short circuit (ISC). Thus, the initial voltage drop and temperature rise at the onset of ISC are successfully predicted across different cell chemistries and SOCs. Furthermore, the framework elucidates the physical mechanisms of unique features observed from mechanical indentation tests, including 1) SOC-dependent voltage rebound after initial voltage drop and 2) higher maximum temperature rise from lower SOCs in non-TR response. To this end, this work highlights the critical influence of fluid-structure interaction between the generated gas and the solid electrical contact of ISC, successfully delivering a physical explanation of the unique features. Thus, this work will establish a fundamental basis for the safety analysis and the TR risk prediction of mechanically abused large-format LIBs.

This study investigates the role of mechanical constraints in enhancing the performance and longevity of calendar-aged lithium-ion batteries (LIBs). By analyzing their cycling behavior under constrained and unconstrained conditions, we demonstrate that suppressing gas generation and preserving internal structural integrity are pivotal for lifetime extension. Unconstrained cycling leads to swelling, temperature rise, accelerated gas generation, and electrode delamination, culminating in the end of life (EOL) within 600 cycles for a calendar-aged cell. In contrast, applying external pressure during cycling suppresses gas generation by mitigating the side reactions responsible for gas evolution. Mechanical constraints preserve the cell's internal structure, enabling an improved relative capacity of 83% after 600 cycles and facilitating capacity recovery and lifespan extension of cells previously subjected to unconstrained cycling. These results highlight the potential of mechanical constraints for extending the operational life of LIBs and underscores the importance of proper handling of spent batteries for potential second-life applications.

Electrochemical impedance spectroscopy (EIS) is a widely used non-invasive method for characterizing and diagnosing lithium-ion batteries (LIBs) 1 . The key to utilizing EIS lies in interpreting the measured impedance spectrum. This involves fitting the experimental data to an impedance model to understand the internal states of the battery. However, due to the multiscale and multiphysical nature of LIBs, impedance models can be complex and have many parameters. As a result, there is a high risk of over-fitting the experimental data, making the interpretation of the EIS data challenging 2 . In addition to electrical signals, the mechanical responses of a LIB, such as pressure and thickness change during charge/discharge, provides valuable information for characterization and diagnosis 3-5 . Most existing analyses of mechanical signals focus on the time domain. Recently, von Kessel et al 6 introduced Mechanical Impedance Spectroscopy (MIS) as a frequency-analyzing tool for characterizing LIBs. The MIS spectrum (the displacement response subject to a cyclic pressure trigger) turned out to vary with the state of the batteries, demonstrating the great potential of using mechanical responses in the frequency domain to characterize and diagnosis the LIBs. Nevertheless, measuring displacement for LIBs in real-world applications is challenging, as it requires a customized testing machine to measure the micrometer-level displacement while exerting a sinusoidal pressure input. This requirement for customized testing equipment limits the application scope of the MIS method. A free LIB cell cyclic changes its dimensions during charge/discharge. Under confinements where dimension change constricted, cyclic pressure change will be generated, which could be used for frequency analysis. This observation led us to propose a new method called mechano-electro-chemical impedance spectroscopy (MeIS). An MeIS spectrum is defined as the ratio of pressure perturbation to the input current, denoted as . Measurements can be obtained by perturbating the battery with sinusoidal current and recording the resulting pressure or displacement response. The basic transfer function for MeIS is derived from the electro-chemo-mechanical coupling of the porous electrode. The MeIS consists of two parts, the MIS term and an electrochemical term resulting from the insertion/extraction deformation of the electroactive particles. The great advantage of MeIS is that it requires only a pressure or displacement sensor and a charger capable of providing sinusoidal current, making it potentially applicable in in-field scenarios such as management of EV batteries or real-time monitoring of a battery-based energy storage facility. Sensitivity analysis reveals that MeIS is highly sensitive to changes in the structure of porous electrodes, thus providing valuable insights into the internal structural integrity and degradation of LIBs. In addition, the experimental design and demonstrational results are also provided. We believe that MeIS could serve as a convenient and useful complement to EIS, enhancing the non-invasive diagnostic toolbox for LIBs. Reference: K. Mc Carthy, H. Gullapalli, K. M. Ryan, and T. Kennedy, Journal of The Electrochemical Society, 168 (8), (2021). F. Ciucci, Current Opinion in Electrochemistry, 13 132-139 (2019). J. Zhu, T. Wierzbicki, and W. Li, Journal of Power Sources, 378 153-168 (2018). B. Rieger, S. Schlueter, S. V. Erhard, J. Schmalz, G. Reinhart, and A. Jossen, Journal of Energy Storage, 6 213-221 (2016). Z. J. Schiffer, J. Cannarella, and C. B. Arnold, Journal of The Electrochemical Society, 163 (3), A427-A433 (2015). O. von Kessel, T. Deich, S. Hahn, F. Brauchle, D. Vrankovic, T. Soczka-Guth, and K. P. Birke, Journal of Power Sources, 508 (2021). Figure 1

Void growth during the plating and stripping process is an important interfacial phenomenon that hinders the development of Li-metal solid-state batteries (SSBs) because it can potentially result in inter-component delamination or growth of Li dendrites. Behind the void growth is the complex interaction between electrochemistry and mechanics. Voids usually grow from micro- or nanoscale initial imperfections on the Li-solid electrolyte (SE) interface. It is, therefore, important to analyze the stress concentration at the initial void tips, which largely determines the growth/shrinking of the void as well as the consequent changes in the internal resistance and overall battery efficiency. Recently, the development of in situ operando experimental technology has enabled the electro-chemo-mechanical characterization of the void growth phenomenon on the Li-SE interface. In this study, we explore how voids in SSBs evolve with a multi-physics model in COMSOL Multiphysics. Based on this model, we perform a systematic parametric study in the space of the mechanical stack pressure and the applied current density. The simulation results show different deformation patterns and potential failure mechanisms under different combination of stack pressure and current density. To better understand the phenomena, we develop an analytical solution to understand the void deformation induced by the inhomogeneous Li-ion concentration field under stack pressure. This analytical approach offers a complementary and intuitive perspective on the mechanical aspect of our simulation findings. By combining the simulation and analytical solutions, we depict a phase diagram, in which we identify the “safe zone” that will not result in void growth. The new insights of this research hold the promise of guiding the development of stabilized SSB interfaces.

Lithium-ion batteries change their geometric dimensions during cycling as a macroscopic result of a series of microscale mechanisms, including but not limited to diffusion-induced expansion/shrinkage, gas evolution, growth of solid-electrolyte interphase, and particle cracking. Predicting the nonlinear dimensional changes with mathematical models is critical to the lifetime prediction, health management, and non-destructive assessment of batteries. In this study, we present an approach to implement an elastoplasticity model for powder materials into the porous electrode theory (PET). By decomposing the overall deformation into elastic, plastic, and diffusion-induced portions and using the powder plasticity model to describe the plastic portion, the model can capture the reversible thickness change caused by Li-ion (de-)intercalation as well as the irreversible thickness change due to the rearrangement and consolidation of particles. For real-world applications of the model to predict battery health and safety, the key lies in solving the mathematical equations rapidly. Here, we implemented the coupled model into the open-source software PETLION for millisecond-scale simulation. The computational model is parameterized using values gathered from literature, tested under varying conditions, briefly compared to real-world observations, and qualitatively analyzed to find parameter-output relations.

Phase-field models are widely used to describe phase transitions and interface evolution in various scientific disciplines. In this Tutorial, we present two neural network methods for solving them. The first method is based on physics-informed neural networks (PINNs), which enforce the governing equations and boundary/initial conditions in the loss function. The second method is based on deep operator neural networks (DeepONets), which treat the neural network as an operator that maps the current state of the field variable to the next state. Both methods are demonstrated with the Allen–Cahn equation in one dimension, and the results are compared with the ground truth. This Tutorial also discusses the advantages and limitations of each method, as well as the potential extensions and improvements.

The mechanical integrity of the cathode is critical for the operation and safety of lithium-ion batteries (LIBs). This study presents a comprehensive exploration into the progression and underlying mechanisms of failure within LIB cathodes. First, shear and 180° peel tests are designed and conducted to calibrate the parameter values governing the interface between the Al foil and the active layer, leveraging cohesive zone modeling. Furthermore, based on finite element modeling (FEM) and extended finite element modeling (XFEM), failure criteria and damage models are introduced to simulate the damage behavior of both Al foils and active layer. Notably, XFEM computes the initiation and expansion of the crack in cathode. Moreover, a parametric analysis is conducted to study the influence of the active layer and interface on cathode failure. It reveals that as the failure strain of the active layer increases, the failure strain of the cathode rises, ultimately converging with the failure strain of an isolated Al foil. However, the failure strain of cathode decreases and then increases with increasing interface peel strength. The results enhance our understanding of the failure process and mechanisms in LIB electrodes and thus provide guidance for the design and fabrication of cathode.

All-solid-state-batteries (ASSBs) are considered the future replacements for traditional lithium-ion batteries, thanks to their superior energy density and enhanced safety. The ionic conductivity of solid electrolytes (SEs) could be improved to match that of liquid electrolytes through careful material design and optimization. However, the relatively sluggish kinetics at the interface between at the active material (lithium metal or cathode materials) and the SE interface remain a hurdle for the wider adoption of ASSBs. The SE’s contact with the anode and cathode materials is inferior compared to that of its liquid counterpart, and improper working conditions can easily lead to the formation of many voids and pores. This results in an increased interface resistance and a deteriorated power density of the ASSB cell. The mechanical contact between the anode material and the SE is more significant due to the larger volumetric expansion and contraction during cycling compared to the cathode side. In addition, high stack pressure and plating current density can also induce the penetration of lithium dendrites. Therefore, it is vital to explore the stability envelope for the anode-SE interface, namely determining the operation condition under which void formation and dendrite growth could be suppressed for the effective design and utilization of ASSBs. Pressure and current density are two controllable condition parameters in the operation of ASSBs 1, 2 . High current density can lead to the formation of numerous voids at the Li/SE interface during stripping, as well as the initiation and growth of lithium metal dendrite within the SE. This results in increased polarization and potential battery failure. Applying stack pressure to the ASSB can stabilize the Li/SE interface during manufacturing and operation, as the creep of Li metal is believed to occur once its stress surpasses a certain threshold. The plastic ‘flow’ of Li metal could help to hinder the growth of voids 2 and keep good contact condition. However, at an excessively large pressure 3, 4 , dendrites can penetrate the SE, which cause a short-circuit in the ASSB and lead to failure 5 . Therefore, an electro-chemo-mechanical model that can describe the large-deformation mechanical properties of Li metal and its coupled mechanisms with the reactions at the Li/SE interface is essential for determining the stability envelope for stable stripping and plating of lithium in the ASSBs. In this work, a phase-field electro-chemo-mechanical model is proposed, in which the coupling of void diffusion, lattice annihilation, stripping and plating reactions, and mechanical properties of lithium metal are comprehensively described. The first contribution of this work is a comprehensive summary of the mechanical properties of the lithium metal under different temperatures and deformation rates, generating a unified deformation-mechanism map for the general battery manufacturing and characterizing community. Based on this map, our phase-field electro-chemo-mechanical model is developed to include important features below. The diffusion of vacancies and Li sites in the lithium metal is considered to simulate the lattice annihilation and the void formation during stripping. The flow of lithium metal caused by creeping or plasticity is incorporated in the kinetical equation of the order parameters. The plating and stripping kinetics are described by the modified Butler-Volmer equation in which the effect of vacancies is considered The general theory proposed in this work can simulate the electro-chemo-mechanical effects at different operation conditions for Li metal or other Li alloy anode materials, which is believed to be a powerful tool for the effective design, manufacturing and management of next-generation batteries. References: T. Krauskopf, H. Hartmann, W. G. Zeier, and J. Janek, ACS Appl Mater Interfaces, 11 (15), 14463-14477 (2019). T. Krauskopf, B. Mogwitz, C. Rosenbach, W. G. Zeier, and J. Janek, Advanced Energy Materials, 9 (44), (2019). D. Cao, K. Zhang, W. Li, Y. Zhang, T. Ji, X. Zhao, E. Cakmak, J. Zhu, Y. Cao, and H. Zhu, Advanced Functional Materials, (2023). L. Zhao, W. Li, C. Wu, Q. Ai, L. Guo, Z. Chen, J. Zheng, M. Anderson, H. Guo, J. Lou, Y. Liang, Z. Fan, J. Zhu, and Y. Yao, Advanced Energy Materials, (2023). E. J. Cheng, A. Sharafi, and J. Sakamoto, Electrochimica Acta, 223 85-91 (2017). Figure 1

The pandemic caused by the SARS-CoV-2 virus has claimed more than 6.5 million lives worldwide. This global challenge has led to accelerated development of highly effective vaccines tied to their ability to elicit a sustained immune response. While numerous studies have focused primarily on the spike (S) protein, less is known about the interior of the virus. Here we propose a methodology that combines several experimental and simulation techniques to elucidate the internal structure and mechanical properties of the SARS-CoV-2 virus. The mechanical response of the virus was analyzed by nanoindentation tests using a novel flat indenter and evaluated in comparison to a conventional sharp tip indentation. The elastic properties of the viral membrane were estimated by analytical solutions, molecular dynamics (MD) simulations on a membrane patch and by a 3D Finite Element (FE)-beam model of the virion's spike protein and membrane molecular structure. The FE-based inverse engineering approach provided a reasonable reproduction of the mechanical response of the virus from the sharp tip indentation and was successfully verified against the flat tip indentation results. The elastic modulus of the viral membrane was estimated in the range of 7-20 MPa. MD simulations showed that the presence of proteins significantly reduces the fracture strength of the membrane patch. However, FE simulations revealed an overall high fracture strength of the virus, with a mechanical behavior similar to the highly ductile behavior of engineering metallic materials. The failure mechanics of the membrane during sharp tip indentation includes progressive damage combined with localized collapse of the membrane due to severe bending. Furthermore, the results support the hypothesis of a close association of the long membrane proteins (M) with membrane-bound hexagonally packed ribonucleoproteins (RNPs). Beyond improved understanding of coronavirus structure, the present findings offer a knowledge base for the development of novel prevention and treatment methods that are independent of the immune system.

All-solid-state batteries (ASSBs) provide higher energy densities and safer alternatives to Li-ion batteries by incorporating Li-metal anode and inflammable solid electrolytes. To match the increased capacity of metallic anodes, ASSBs require high-energy-density cathode materials such as LiNi x Co y Mn 1-x-y O 2 (also known as NMC cathode) blended with a solid-state electrolyte (SSE). However, a key challenge is resolving the interfacial incompatibility between the active particulate material and the solid-state electrolyte within these composite cathodes. To obtain intimate contact between SSE and the active material, an external load (or stack pressure) needs to be applied during the processing and service life of cell. Nevertheless, such densification of cathode composites can lead to grain boundary fracture and/or complete particle disintegration. Therefore, the present work utilizes a microstructural modulation procedure to alleviate stack pressure-induced fracture in a polycrystalline cathode. Accordingly, a thermodynamically consistent computational framework is developed to understand the interplay between the stack pressure, microstructural modulation, and fracture behavior for polycrystalline NMC secondary particles embedded in a sulfide-based solid electrolyte. A phase-field fracture variable is employed to consider the initiation and propagation of cracks in the active material and SSE. This modeling framework is implemented in the open-source finite element package (MOOSE) to solve three state variables: concentration, displacement, and the phase-field damage parameter. A systematic parametric study is performed to explore the effects of stack pressure, aspect ratio, and the crystal orientation of grains on the chemo-mechanical performance of the composite electrode. We also quantitatively validate the numerical model with experimental investigation using state-of-the-art Nano-CT (Compact tomography) technique. The findings of this study offer predictive insights for designing solid-state batteries with reduced fracture evolution and stable performance. Figure 1

The future of all-electric aircraft depends on the innovation of battery technology today. Since the energy density of conventional Li-ion battery cells with graphite and metal oxides electrodes is limited to about 300 Wh/kg at the cell level, “next-generation batteries” such as the Li-metal all-solid-state batteries (ASSBs) are demanded to achieve the minimum energy density (~600 Wh/kg) necessary to make electric flight viable. The major obstacles preventing the widespread adoption of Li-metal ASSBs are rapid degradation and poor rate capacities, which are directly linked to various interfacial issues involving multiple electro-chemo-mechanical processes. Overcoming these interfacial issues calls for a high-fidelity computational model that could be used for exploring the physical mechanisms involved in degradation and for identifying promising remedies through informed synthesis or operating conditions. Conventional computational approaches based on finite element methods (FEM) have enjoyed great success over the past decades, but their applicability in modeling the complexity of ASSB cells is challenged by the multi-physics, multi-scale, and multi-phase nature of the system. The fundamental difficulty stems from the tradeoff between the abundance of data and the adequacy of physical laws. At the microscale, physical laws can usually be observed, but data are expensive and limited; at the macroscale, physical laws are often hidden in the big data that are hard to decipher. Physics-based or first-principle-based theories are robust but suffer from the “curse of dimensionality” as the number of variables and degrees of freedom increases. Recently, many data-driven approaches particularly machine learning have shown advantages in dealing with high-dimensional problems, but they are usually agnostic and prone to unphysical failure. Our MIT-NREL joint team is working in the NASA Transformational Tools and Technologies (TTT) program on integrating physics-based theories with data-driven approaches to characterize the electro-chemo-mechanical behavior of Li-metal ASSB systems for the prediction of cycling performance and degradation mechanisms. In this short presentation, we will show one specific example of using Deep Operator Learning for the characterization of battery physics governed by gradient flow. Gradient flow entails finding and constructing an appropriate potential energy and inner product to incorporate the kinetics into a variational framework. Gradient flows can be applied to a large variety of physics, including diffusion, phase separation, microstructure evolution, etc., where the governing partial differential equations (PDEs) can be eventually obtained. Conventional numerical methods, such as FEM, have been proven to be effective in solving PDEs. However, it is still challenging for systems with high dimensionality and nonlinearity. Recently, the concept of scientific machine learning was proposed and widely used by many research groups to solve variational problems. One such approach is approximating the solutions with ML models and training them by minimizing the energy functional, instead of solving a large set of non-linear equations. In a previous study, our team successfully developed an energy-based neural network method for structural mechanics problems. Recently, operator learning (OL) started to gain increasing attention. Instead of approximating the solution, OL models the mapping from one functional space to another. It has the potential to incorporate solutions with different initial conditions into one algorithm. In this study, we proposed a general variational method-based operator neural network framework for dynamics systems governed by gradient flows. To validate the proposed framework, we investigated several dynamics systems that commonly exist in energy materials, including linear relaxation kinetics, Allen-Cahn dynamics, and phase-field dynamic fracture.

We propose a conservative energy method based on neural networks with subdomains for solving variational problems (CENN), where the admissible function satisfying the essential boundary condition without boundary penalty is constructed by the radial basis function (RBF), particular solution neural network, and general neural network. The loss term is the potential energy, optimized based on the principle of minimum potential energy. The loss term at the interfaces has the lower order derivative compared to the strong form PINN with subdomains. The advantage of the proposed method is higher efficiency, more accurate, and less hyperparameters than the strong form PINN with subdomains. Another advantage of the proposed method is that it can apply to complex geometries based on the special construction of the admissible function. To analyze its performance, the proposed method CENN is used to model representative PDEs, the examples include strong discontinuity, singularity, complex boundary, non-linear, and heterogeneous problems. Furthermore, it outperforms other methods when dealing with heterogeneous problems.

The mechanically induced internal short circuit (ISC) is one of the major safety concerns of lithium-ion batteries. Mechanical abuse tests are often performed to evaluate the integrity and safety of lithium-ion batteries under mechanical loadings. Except for the widely explored compression-dominated indentation tests, bending is another typical real-world loading condition that is tension-dominated. To investigate the mechanical damage and ISC behavior of batteries under bending, we carried out controlled three-point bending tests in four progressive steps on prismatic battery cells with maximum deflections ranging from 38% to 76% of the cell thickness. None of the tested cells experienced an ISC. We then conducted 3D X-ray computed tomography (CT) scanning on the bent cells after unloading. X-ray CT images showed three out of the four tested cells have extensive cracking in the electrode layers at the bottom side (opposite to the loading head). This indicates that cracking does not necessarily lead to an ISC under bending. Electrochemical impedance spectroscopy was also measured on the bent cells and substantial changes were observed. Both the bulk resistance and charge-transfer resistance increased significantly after bending, which could influence the battery performance and lifespan. We then developed a detailed finite (FE) element model to further investigate the mechanical deformation and failure mechanisms. The FE model successfully predicts the load-displacement response and reproduces the deformation patterns. The findings and the FE model developed in the present study provide useful insights and tools for the battery structure and crash safety design.

E-mobility, especially electric cars, has been scaling up rapidly because of technological advances in lithium-ion batteries (LIBs). However, LIBs degrade significantly with service life cycles. With the current increase in the adoption of electric vehicles (EVs), a large volume of retired LIB packs, which can no longer provide satisfactory performance to power an EV, will soon appear. Various end-of-life (EOL) options are under development, such as recycling and recovery. Recently, stakeholders have become more confident that giving the retired batteries a second life by reusing them in less-demanding applications, such as stationary energy storage, may create new value pools in the energy and transportation sectors. In this perspective, we evaluate the feasibility of second-life battery applications, from economic and technological perspectives, based on the latest industrial reports and technical publications.

Enabling accurate prediction of battery failure will lead to safer battery systems, as well as accelerating cell design and manufacturing processes for increased consistency and reliability. Data-driven prediction methods have shown promise for accurately predicting cell behaviors with low computational cost, but they are expensive to train. Furthermore, given that the risk of battery failure is already very low, gathering enough relevant data to facilitate data-driven predictions is extremely challenging. Here, a perspective for designing experiments to facilitate a relatively low number of tests, handling the data, applying data-driven methods, and improving our understanding of behavior-dictating physics is outlined. This perspective starts with effective strategies for experimentally replicating rare failure scenarios and thus reducing the number of experiments, and proceeds to describe means to acquire high-quality datasets, apply data-driven prediction techniques, and to extract physical insights into the events that lead to failure by incorporating physics into data-driven approaches.

Understanding the relationships between microstructural characteristics and multiphysics properties is key to designing battery electrode materials for desired properties. Significant efforts have been made to achieve a quantitative understanding of the relationship between mass transport properties and Li-ion microstructure characteristics [1-3] in literature, but it is still challenging to predict the coupled mechanical and electrochemical behaviors based on microstructure characteristics. We seek to further this understanding through an investigation of how deformation affects the transport properties of the Li-ion battery graphite anode microstructure, which features packed particles and irregular pore networks. We propose a statistical Microstructure Characterization and Reconstruction (MCR) approach to characterize statistical microstructure features from 2D microscopic images and then reconstruct 3D stochastic microstructures. MCR is an effective tool for material property prediction [4] and microstructure-mediated material design [5]. The proposed MCR approach generates microstructure designs that are beyond the scope of empirical data. By exploring the input microstructure feature space, 3D microstructure samples are reconstructed and simulated to investigate relationships between the microstructural characteristics and properties of Li-ion battery graphite anodes. The selection of microstructure characteristics is informed by an external open access battery microstructures library. For each microstructure reconstruction, compression and transport simulations are conducted to determine the Young’s modulus and to understand the transport properties (e.g. diffusivity) of the undeformed and deformed microstructures. Convergence studies are conducted to establish a Representative Volume Element (RVE) size. With the simulation dataset, the microstructure-property relationship is examined. A feature selection algorithm is used to examine the size of the effects of microstructural characteristics on multiphysics properties. Machine learning models are established to predict the microstructure-property relationship. References: [1] Lu, X., Bertei, A., Finegan, D.P., Tan, C., Daemi, S.R., Weaving, J.S., O’Regan, K.B., Heenan, T.M., Hinds, G., Kendrick, E. and Brett, D.J., 3D microstructure design of lithium-ion battery electrodes assisted by X-ray nano-computed tomography and modelling. Nature Communications, 11(1) (2020) pp.1-13. [2] Stephenson, D.E., Walker, B.C., Skelton, C.B., Gorzkowski, E.P., Rowenhorst, D.J., Wheeler, D.R., Modeling 3D Microstructure and Ion Transport in Porous Li-Ion Battery Electrodes, Journal of The Electrochemical Society 158(7) (2011) A781. [3] Xu, H., Usseglio-Viretta, F., Kench, S., Cooper, S.J. and Finegan, D.P., Microstructure reconstruction of battery polymer separators by fusing 2D and 3D image data for transport property analysis. Journal of Power Sources, 480 (2020) p.229101. [4] Xu, H., Bae, C., Stochastic 3D microstructure reconstruction and mechanical modeling of anisotropic battery separators, Journal of Power Sources 430 (2019) 67-73 [5] Liu, Y., Greene, M.S., Chen, W., Dikin, D.A., Liu, W.K., Computational microstructure characterization and reconstruction for stochastic multiscale material design, Computer-Aided Design 45(1) (2013) 65-76 Figure 1