Porous electrodes in lithium-ion batteries undergo cyclic mechanical deformation during operation, yet predictive models in the small-to-medium strain regime under functional pressure conditions remain scarce. This work develops and calibrates a constitutive model for porous electrode materials by integrating systematic low-pressure triaxial and uniaxial compression tests with the Drucker-Prager/Cap (DPC) plasticity framework, grounded in traditional powder mechanics. The calibrated model captures particle sliding and porous consolidation via the Drucker-Prager envelope and Deshpande-Fleck cap, respectively, and is readily implementable in commercial finite element platforms. Coupled with reduced-order electrochemical models, it enables accurate prediction of reversible "breathing" and irreversible "swelling" during cycling. Discussion highlights the model's sensitivity to electrochemical inputs, the amplification of small prediction errors in mechanical response, and the practical treatment of diffusion-induced strain as a tunable fitting parameter for industrial use. The approach balances experimental simplicity, computational efficiency, and predictive fidelity, offering a practical tool for battery manufacturing, structural design, and life-cycle management.

Characterizing materials through conventional testing protocols often requires multiple separate tests, making it resource-intensive and introducing inter-specimen variability. This work presents an informatics-enabled approach that frames material characterization as an information utilization problem rather than a standardized testing problem. Instead of adapting specimen design to multiple simple tests, we adapt tests to a single complex, informative specimen, optimally designed to maximize stress state entropy, a quantitative measure of mechanical information. The specimen design methodology employs Gaussian random fields with Karhunen-Lo & egrave;ve expansion as the generative basis for topology optimization, where eigenvalue configurations are tuned via Bayesian optimization to maximize stress state entropy. Applied to orthotropic elasticity and anisotropic plasticity, the framework generates specimens approaching theoretical entropy limits that simultaneously activate all stress states required for accurate characterization within a single uniaxial tension test. To validate this framework, we introduce a physics-informed neural network approach using an Input Convex architecture for learning orthotropic elasticity directly from full-field data while preserving thermodynamic consistency. Comparative analysis demonstrates that parameter identification accuracy scales directly with specimen information content, with the most informative specimen design achieving order-of-magnitude improvement in identification accuracy over specimens with low information content. Additionally, a parametric study reveals that void fraction has a significant influence on the information content of the designed specimen. This work establishes a general framework for informatics-driven test specimen design, enabling efficient material characterization across diverse constitutive models.

Porous electrodes couple electrical, chemical, mechanical, hydraulic, and thermal fields, yet conventional frequency-domain diagnostics interrogate only one of them: electrochemical impedance spectroscopy (EIS) the electrical response and dynamic mechanical analysis (DMA) the mechanical. Each reads a diagonal entry of the multiphysical constitutive matrix and is blind to the cross-couplings that govern structural evolution and degradation. Starting from linear irreversible thermodynamics, we formulate a general theory of multiphysical impedance spectroscopy, in which perturbing one field and measuring the conjugate response of another probes an off-diagonal entry of the constitutive matrix, recovering the static coupling coefficient and resolving its relaxation dynamics across frequency. Specializing to the electro-chemo-mechanical pathway yields a closed-form theory of mechano-electrochemical impedance spectroscopy (MEIS), in which a small harmonic current is applied and the stack stress is measured; the impedance factorizes into a chemical-accumulation term multiplying the sum of a chemo-mechanical and a poro-mechanical kernel. The porosity-accommodation bridge function is derived from a Helmholtz free energy – following from a microstructural stiffness and viscosity rather than a fitted form – and a three-phase (solid-fluid-void) closure interpolates continuously between unsaturated and Biot-saturated limits through a void-accommodation fraction. Non-dimensionalization reduces the spectrum to five groups, identifies the phase angle as the discriminator of the chemo-mechanical parameters, and locates the onset of second-quadrant behavior, which in a full cell arises from the competition between an expanding and a contracting electrode. MEIS emerges as one member of a family of cross-coupled spectroscopies the same framework brings within reach.

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.

Detecting and preventing lithium-ion battery failures remains a critical challenge, as failures degrade performance, damage materials, and pose safety risks. While catastrophic outcomes under extreme impacts or compression-such as short circuits, thermal runaway, and fires-have been extensively studied, batteries in real-world applications are more often exposed to moderate stresses. These conditions produce small internal changes with weak initial electrochemical signatures that can accumulate into severe damage over time. This work investigates how single-impact and compressive forces of varying intensities and orientations affect the structure and electrochemical behavior of 18650 NMC/graphite cylindrical cells. X-ray computed tomography (CT) provides direct visualization of damage to both active and inactive components, while open-circuit potential, impedance spectroscopy, and charge-discharge cycling collectively capture electrochemical consequences. The results reveal immediate yet modest capacity loss even after a single mechanical event, with damaged cells later showing cycling performance comparable to intact cells if only minor damage is introduced. However, repeated loading produces cumulative degradation, further aggravated by subsequent electrochemical cycling. CT imaging highlights structural changes invisible to conventional diagnostics, while electrochemical tests expose early functional impacts. Together, these complementary methods advance understanding of how moderate mechanical damage initiates, evolves, and compromises long-term reliability in lithium-ion batteries.

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.

Electrochemical methods such as voltammetry and impedance spectroscopy have long provided indispensable insight into charge-transfer kinetics, ion transport, and degradation mechanisms in batteries and related electrochemical systems. However, these canonical techniques treat electrodes largely as electrochemically active but mechanically passive media, even though ion insertion, phase transformations, and structural rearrangements inherently couple chemical potentials with stress and strain. This electro-chemo-mechanical coupling governs not only particle-level transformations but also porous-electrode transport, active material utilization, and long-term reversibility. In this talk, I will present recent advances that revisit classical electrochemical techniques through the lens of electro-chemo-mechanical interactions with technical assistance of AI, thereby expanding their diagnostic reach and physical interpretability. I will first introduce mechano-electrochemical impedance spectroscopy (MEIS), a new approach that directly measures the mechanical response of porous electrodes by probing their pressure oscillations under small electrochemical perturbations. MEIS builds on the fundamental observation that ion intercalation induces particle swelling, which in turn modulates internal stresses and interfacial contact patterns. By applying a sinusoidal current perturbation and measuring the resulting oscillatory pressure signals, MEIS “freezes” the electrode in the same sense as conventional EIS but captures a complementary dimension of its dynamics: the rate-dependent chemo-mechanical relaxation of the composite microstructure. The resulting frequency-domain spectra reveal characteristic signatures of particle breathing, porous-electrode compaction, and stress-mediated transport. In our recent work, we derived a theoretical framework linking MEIS spectra to intrinsic properties of active materials, including their chemical expansion coefficients, viscoelastic response, and heterogeneous reaction kinetics. We developed both a simplified linearized model for rapid interpretation and a full porous-electrode model capturing the interplay between ionic intercalation, mechanical stiffness, and porous structural changes. The technique demonstrated high sensitivity to state of charge (SOC) and state of health (SOH) on commercialized pouch cells and prismatic cells, with reproducible spectral shifts corresponding to evolving mechanical stiffness and porosity changes. These findings highlight the ability of MEIS to probe transformations that are invisible to purely electrochemical methods, including microstructural compaction, particle fragmentation, and stress-coupled transport limitations that emerge during aging. Recently, we advanced the MEIS-based analytics with the assistance of state-of-the-art feature extraction and AI techniques to identify the most sensitive frequency range of MEIS for SOC and SOH estimations. This advancement is expected to implement MEIS into a practical technique for functioning in real-world applications. Beyond diagnostics, MEIS offers a platform to validate multi-physics models and to quantify mechanical degradation mechanisms with unprecedented clarity. Building on this foundation, I will briefly introduce our emerging work on cyclic mechano-voltammetry (MV), a new approach that extends classical voltammetry by incorporating dynamic mechanical information. Whereas conventional cyclic voltammetry provides insight into electrochemical reversibility, reaction pathways, and rate capabilities, it cannot directly capture the structural reversibility of porous electrodes—such as changes in particle contact networks, porosity, and stress-mediated transport. In contrast, cyclic MV measures simultaneous voltage, current, and mechanical signals during controlled potential sweeps, enabling a direct link between charge-transfer processes and their mechanical consequences. Early results indicate that MV can reveal hysteresis in mechanical response, rate-dependent compaction and relaxation phenomena, and previously unrecognized mechano-kinetic coupling effects. We expect to report further advances by the time of the symposium, including theoretical and experimental interpretation of MV signals and connections between MV and MEIS as complementary mechano-electrochemical probes. Together, MEIS and MV illustrate a broader paradigm: electrochemical methods augmented by mechanical observables can unlock new understanding of how batteries actually work, age, and fail. These mechano-sensitive techniques provide information at multiple scales, from particle-level swelling to electrode-level compaction and cell-level stress evolution. With the assistance of AI techniques, they also offer new opportunities for model validation, accelerated diagnostics, and real-time monitoring in practical systems. By bridging the electrochemical and mechanical domains, this work aims to establish a unified multi-physics framework that enhances the interpretability and utility of longstanding electrochemical methods. Reference: R. Fang, J. Jiao, W. Li, R. C. Ihuaenyi, M. Z. Bazant, J. Zhu, Joule , 102177 (2025).

Morphology, material property, and mechanical constraint jointly govern the chemo-mechanical behavior of ion-intercalation particles, yet their coupled effects remain insufficiently understood. Here we establish a thermodynamically consistent single-particle framework and combine analytical solutions with multiphysics simulations to determine how these factors regulate lithiation and stress generation. We study hollow spherical, cylindrical, and ellipsoidal particles with isotropic or transversely isotropic material properties under fully constrained, inner-free, or unconstrained boundary conditions. We show that the transient lithiation pathway and the associated stress and strain fields are governed not by morphology, property, or constraint alone, but by their coupled interaction: isotropic particles are sensitive to the mechanical constraint, whereas transversely isotropic particles exhibit persistent heterogeneous lithiation dominated by anisotropic diffusivity. Flux decomposition analysis reveals that the mechanical contribution to Li flux is negligible in spheres but dominant in ellipsoids. Correlation analysis further shows that Li concentration and volumetric strain exhibit strong anti-correlation in unconstrained particles but weak correlation under full constraints. Bayesian optimization of hollow ellipsoids identifies Pareto-optimal morphologies that balance lithiation capacity against peak tensile stress. These results provide a unified framework for the morphology-property interplay in intercalation particles and offer morphology design principles for chemo-mechanical stability.

We introduce mechano-electrochemical impedance spectroscopy (MEIS) as a technique that complements electrochemical impedance spectroscopy (EIS) by probing coupled mechanical-electrochemical dynamics in batteries. MEIS leverages electrode expansion and contraction during ion intercalation, which induces measurable pressure fluctuations under mechanical constraint. By applying a small sinusoidal current and recording the pressure response, MEIS defines its spectrum as the frequency-domain ratio of pressure to current. Experiments across multiple chemistries reveal distinct MEIS features that depend strongly on state of charge (SOC) and are sensitive to state of health (SOH), underscoring its diagnostic potential. An idealized analytical model links semicircles to mechanical stiffness and vertical features to intercalation-induced pseudo-damping, while a porous-electrode model incorporating a poro-viscoelastic bridge explains counterintuitive behaviors such as phase reversals and quadrant shifts. By connecting particle-scale deformation to electrode-level responses, MEIS opens new avenues for SOC estimation, degradation analysis, and health diagnostics in energy storage systems.

The increasing complexity of advanced engineered systems, such as batteries, poses significant challenges to sustainability. Complex systems are harder to interpret and manage, complicating efforts to design for sustainable practices like lifetime extension, rejuvenation, and reuse. Moreover, assessing system status becomes more difficult, hindering sustainable end-of-life decisions. Addressing these challenges requires scientific advances to decode complexity for sustainability. A major challenge in battery sustainability is the lack of reliable assessment techniques that can efficiently obtain the status of aged or spent batteries. In the current industry, safety inspection is usually performed by human labor, which is not only costly but also has a high risk of exposing inspectors to chemical hazards. Health assessment heavily depends on high-quality data from electrochemical and thermal measurements during cycling. When historical data is absent, at least one slow-rate cycle is needed for assessment, which takes hours to complete. To bridge these gaps, we propose exploring a new perspective by developing mechano-electrochemical approaches to assess batteries. Mechanical behavior is an intrinsic property of batteries, similar to electrochemical and thermal behaviors, but it is often overlooked or viewed as a side effect in the regular life cycle. In fact, mechanical measurements convey a significant amount of information that can be used for assessment. This presentation highlights our group’s work in developing electro-chemo-mechanical methodologies to enhance the sustainability of batteries. Two techniques will be introduced: differential pressure analysis (DPA) and mechano-electrochemical impedance spectroscopy (MEIS), inspired by their electrochemical counterparts—the IC-DV analysis and EIS. These mechano-electrochemical approaches will serve as an important supplement to existing electrochemical approaches. This is because mechanical signals provide direct insights into the microstructural changes during degradation, which are typically absent in or have only an indirect influence on electrochemical signals. Moreover, compared to electrochemical tests, mechanical tests have the advantages of lower measuring costs and faster responding time, which is ideal for the rapid assessment of a large volume of spent batteries. Cost-effective and scalable, these pressure-based approaches show strong potential for industrial applications in battery status monitoring and lifetime estimation.

Rechargeable batteries using electrodes based on intercalation chemistry exhibit notable cyclability, yet their performance still suffers from chemomechanical degradation. In this study, by combining a suite of operando microscopy methods, we explored electrode strain evolution and observed intricate particle cluster rearrangement under electrochemical stimuli. We show that early-stage strain accumulation in intercalation cathodes occurs during the period of interparticle charge transfer and redox reactions stemming from asynchronous coupling and decoupling between chemical (de)intercalation and physical grain motion. This interplay drives heterogeneous redox activity, localized charge equilibration, and multiscale strain cascades that propagate through an asynchronous network of chemical-mechanical interactions. Together, these findings reveal how collective particle dynamics and hierarchical strain transmission dictate electrode deformation and degradation in intercalation cathodes.

A central challenge in materials science is characterizing chemical processes that are elusive to direct measurement, particularly in functional materials operating under realistic conditions. Here, we demonstrate that mechanical strain fields contain sufficient information to reconstruct hidden chemical kinetics in coupled chemomechanical systems. Our partial differential equation-constrained learning framework decodes concentration-dependent diffusion kinetics, thermodynamic driving forces, and spatially heterogeneous reaction rates solely from mechanical observations. Using battery electrode materials as a model system, we demonstrate that the framework can accurately identify complex constitutive laws governing three distinct scenarios: classical Fickian diffusion, spinodal decomposition with pattern formation, and heterogeneous electrochemical reactions with spatial rate variations. The approach demonstrates robustness while maintaining accuracy with limited spatial data and reasonable experimental noise levels. Most significantly, the framework simultaneously infers multiple fundamental processes and properties, including diffusivity, reaction kinetics, chemical potential, and spatial heterogeneity maps, all from mechanical information alone. This method establishes a paradigm for materials characterization, enabling accurate learning of chemical processes in energy storage systems, catalysts, and phase-change materials where conventional diagnostics prove difficult. By revealing that mechanical deformation patterns serve as information-rich fingerprints of the underlying chemical processes, this work follows the pathway of inversely learning constitutive laws, with broad implications in materials science and engineering.

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.

Efficient and accurate learning of constitutive laws is crucial for accurately predicting the mechanical behavior of materials under complex loading conditions. Accurate model calibration hinges on a delicate interplay between the information embedded in experimental data and the parameters that define our constitutive models.The information encoded in the parameters of the constitutive model must be complemented by the information in the data used for calibration. This interplay raises fundamental questions: How can we quantify the information content of test data? How much information does a single test convey? Also, how much information is required to accurately learn a constitutive model? To address these questions, we introduce mechanics informatics, a paradigm for efficient and accurate constitutive model learning. At its core is the stress state entropy, a metric for quantifying the information content of experimental data. Using this framework, we analyzed specimen geometries with varying information content for learning an anisotropic inelastic law. Specimens with limited information enabled accurate identification of a few parameters sensitive to the information in the data. Furthermore, we optimized specimen design by incorporating stress state entropy into a Bayesian optimization scheme. This led to the design of cruciform specimens with maximized entropy for accurate parameter identification. Conversely, minimizing entropy in Peirs shear specimens yielded a uniform shear stress state, showcasing the framework's flexibility in tailoring designs for specific experimental goals. Finally, we addressed experimental uncertainties, demonstrated the potential of transfer learning for replacing challenging testing protocols with simpler alternatives, and extension of the framework to different material laws.

Cell-level battery models, most of which rely on the successful porous electrode theories, effectively estimate cell performance. However, pinpointing the contributions of individual components of an electrode remains challenging. In contrast, particle-level models based on real microstructures describe active material characteristics but do not accurately reflect performance under cell-level operating conditions. To bridge this modeling gap, we propose a microelectrode modeling framework that considers each component of a composite electrode. This framework enables us to analyze the complex electrochemo-mechanical relationships within the composite electrode. The realistic 3D microstructure of the LiNi0.7Mn0.15Co0.15O2 composite electrode is reconstructed from focused ion beam-scanning electron microscopy images. By applying the intrinsic properties of every component, the composite microelectrode model achieves more than 98% accuracy in terms of the voltage profile compared to the measurement on coin cells. This model allows us to identify three important mechanisms that contribute to the discrepancy between cell and particle levels, i.e., reduced reaction area, increased diffusion length, and insufficient amount of electrolyte. Simulations under excessive electrolyte conditions reveal a significant improvement in rate capability with 94% capacity retention at 4C. In addition, the model considers the role of conductive materials and binders as well as the viscoplasticity of the polymeric binder, enabling the study of degradation mechanisms involving the stability of the binder-particle connection.

As the demand for the Li-ion battery (LIB) surges, ensuring battery safety becomes critical. Traditionally, voltage, current, and other electrical characteristics are monitored to evaluate battery performance. However, relying solely on these signals may not provide sufficient early warning, due to the rapid onset of thermal runaway in LIBs. Battery-state changes are reflected in coupled alterations in the battery electrical, thermal, and mechanical properties. This study investigates vibrational characteristics to monitor the health of LIBs. Vibration signals generated spontaneously by a LIB during normal charging/discharging cycles are collected using sensors placed on the surface of LIB cells, as well as during artificially induced overcharging and overdischarging states. Time series are preprocessed and advanced data processing techniques are used for data classification to identify deviations from normal battery health condition. This work observes vibration signal changes while LIB cells charging at multiple key voltages corresponding to different phase transitions. Vibration signals from LIBs in healthy, previously overdischarged and previously overcharged states demonstrate three distinct clusters, based on extracted features from various algorithms, each corresponding to a specific health state. These findings prove the applicability of employing vibration signals to effectively monitor battery health in a nondestructive way.

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

Due to the increasing volume of Electric Vehicles in automotive markets and the limited lifetime of onboard lithium-ion batteries (LIBs), the large-scale retirement of LIBs is imminent. The battery packs retired from Electric Vehicles still own 70%-80% of the initial capacity, thus having the potential to be utilized in scenarios with lower energy and power requirements to maximize the value of LIBs. However, spent batteries are commonly less reliable than fresh batteries due to their degraded performance, thereby necessitating a comprehensive assessment from safety and economic perspectives before further utilization. To this end, this paper reviews the key technological and economic aspects of second-life batteries (SLBs). Firstly, we introduce various degradation models for first-life batteries and identify an opportunity to combine physics-based theories with data-driven methods to establish explainable models with physical laws that can be generalized. However, degradation models specifically tailored to SLBs are currently absent. Therefore, we analyze the applicability of existing battery degradation models developed for first-life batteries in SLB applications. Secondly, we investigate fast screening and regrouping techniques and discuss the regrouping standards for the first time to guide the classification procedure and enhance the performance and safety of SLBs. Thirdly, we scrutinize the economic analysis of SLBs and summarize the potentially profitable applications. Finally, we comprehensively examine and compare power electronics technologies that can substantially improve the performance of SLBs, including high-efficiency energy transformation technologies, active equalization technologies, and technologies to improve reliability and safety.

Accurate calibration of constitutive models is vital for predicting the mechanical behavior of engineering materials under various loading conditions. Traditionally, the calibration process involves a series of experiments on specimens with simple geometries to capture the complexities in the constitutive models. Each single test conveys a small amount of information that a well -trained human brain can handle, resulting in a large number of experiments needed for a complete calibration. Therefore, traditional calibration approaches are usually costly and time-consuming. With recent advancements in computational techniques, there is an emerging opportunity to leverage geometrically complex specimens in experiments to obtain a larger amount of information for computers to learn and calibrate the model. Despite some initial success, the most important question remains unsettled: How much information does a mechanical test convey? In this work, we answer this question by incorporating information entropy as a quantitative measure in the design of mechanical test specimens. We demonstrate the viability of the proposed approach by comparing the performance of selected test specimens for learning the plasticity model of sheet metal, e.g., the Hill48 anisotropic elastic -plastic model in this case. An optimal entropy criterion is proposed for selecting the appropriate heterogeneous test specimen for inverse calibration, depending on the cardinality of the stress state space considered in the model. Finally, Bayesian optimization is applied to uniaxial and biaxial tension specimens, using the stress state entropy as an objective function, to investigate the general principles of designing specimens with maximum information for learning constitutive models.