Abstract In situ characterization is used to understand internal short circuit (ISC)-caused thermal runaway of 4-Ah Li-ion cells with graphite anode and LiNi0.8Mn0.1Co0.1O2 (NMC811) cathode under different state-of-charge (SOC), cooling conditions, and electrolyte solvents. While 75% SOC led to thermal runaway, 50% and 25% SOC did not, which can be attributed to the rapid decrease of ISC current. The decrease is due to insufficient energy storage in the 25% SOC cell and due to separator shutdown in the 50% SOC cell. With liquid water cooling, the cell did not experience obvious damage with cooling temperature of 5 °C or 23 °C, but experienced thermal runaway when the cooling temperature was 60 °C. The difference can be attributed to higher ISC current and ISC heating rate at higher operating/cooling temperatures. The ratio of ethylene carbonate (EC) and ethyl methyl carbonate (EMC) in the electrolyte influenced cell capacity and ISC behaviors. EC-free electrolyte led to higher capacity and postponed the onset of thermal runaway, but the resulting thermal runaway is more severe and reached higher maximum temperatures, as compared to electrolyte with EC. The differences can be attributed to higher reactivity, higher conductivity, and lower combustion enthalpy of EC as compared to EMC. These findings demonstrated that in situ characterization can enhance the understanding of ISC and thermal runaway behaviors of Li-ion cells by providing details of the transient and localized phenomena.
It is difficult for existing methods to solve the real-time accuracy problem of battery module-level state of charge (SoC) and the impact of single-battery inconsistency at the same time under dynamic operating conditions. The integration of data-driven technology and traditional algorithms is insufficient, leading to limited error compensation. In view of the accuracy of the SoC estimation of electric vehicle (EV) battery packs under dynamic driving conditions, this paper proposes a hybrid SoC estimation method for battery management system (BMS) based on a cloud master-slave architecture. The hybrid framework combines direct measurement methods (Coulomb counting method, open-circuit voltage method), state estimation algorithms (extended Kalman filtering, traceless Kalman filtering), and data-driven technologies (neural networks, Nonlinear Auto-Regressive Moving Average (NARMA-L2) models), and verifies its effectiveness through hardware-in-the-loop experiments. The research results show that under dynamic operating conditions, the hybrid Coulomb counting and neural network (CC + NN) method achieves the fastest error convergence rate and outperforms other methods. In addition, the proposed cloud master-slave BMS architecture significantly improves system reliability by enabling real-time cross-verification of the SoC data from the advanced algorithms of the on-board BMS (slave device) and the master device. The experiment is based on the Federal Test Procedure (FTP)-75 driving cycle and verifies the high efficiency of this method in practical applications. The final analysis shows that the CC + NN combination exhibits optimal error-suppression performance in complex scenarios and provides a high-precision solution for electric vehicle battery management.
Abstract Lithium-rich layered oxide cathodes, Lix[Ni0.8Mn0.1Co0.1]O2, were synthesized via an oxygen-assisted coprecipitation method followed by calcination at 720 °C, 750 °C, and 780 °C. The introduction of O2 during coprecipitation facilitates in situ oxidation of Mn2+ to higher valence states, leading to Mn-enriched regions within the hydroxide precursor that form Li2MnO3-like domains in the layered structure upon calcination. The primary objective of this study was to systematically investigate the effect of calcination temperature on crystal structure, Li/Ni cation ordering, microstructure, and electrochemical performance. Structural analysis revealed that the 750 °C annealed sample exhibits the lowest Li/Ni disorder, optimal hexagonal ordering, and a porous nanosheet-based morphology, which together promote rapid lithium-ion diffusion. Electrochemical testing shows that this sample achieves the highest initial discharge capacity (∼145 mAh/g), excellent capacity retention (85.4% after 100 cycles), and good rate capability (77.5% retention at 5C). Samples annealed at 720 °C and 780 °C showed reduced performance due to incomplete crystallization and microstructural collapse, respectively. This work demonstrates that optimizing calcination temperature in combination with O2-assisted coprecipitation provides a scalable route to structurally robust lithium-rich NMC cathodes. While the initial capacity is lower than that of commercial NMC811, the study provides valuable insights into the interplay between synthesis conditions, structural ordering, and electrochemical behavior, highlighting design strategies for stable and reversible lithium-ion cathodes.
Abstract This study investigates the thermal management performance of a 4S2P lithium-ion battery pack immersed in a dielectric fluid under three distinct flow-field configurations: no fins, circular fins, and triangular fins. A multiphysics simulation framework using ansys fluent was implemented to model fluid flow and heat transfer, with a focus on achieving optimal cooling efficiency and thermal uniformity. The designs were evaluated across multiple volumetric flowrates to determine the conditions required to maintain the optimal pack temperature near 30 °C from the ambient (25 °C). Results indicate that the triangular-fin configuration achieved the optimal battery pack temperature at a comparatively lower flowrate of 5 LPM and pressure drop of 289 Pa, outperforming both the circular-fin and no-fin configurations. Furthermore, the triangular fins produced the most uniform temperature distribution, as evidenced by the lowest thermal gradient of 2.0 °C, compared to circular fins and no-fin configuration. Velocity streamline visualizations confirmed superior coolant dispersion in the triangular-fin design, minimizing stagnant zones and enhancing convective heat transfer. The findings demonstrate that triangular fins offer an optimal trade-off between flowrate efficiency and thermal performance, making them the most effective among the tested configurations for immersion-cooled battery packs.
Lithium-ion battery (LIB) thermal stability is conceivably improvable with various cathode surface coatings. In this study, LiCoO2 (LCO) cathode composites were coated with a thin layer of poly(3,4-ethylenedioxythiophene) (PEDOT) polymer coating, electrochemically charged, carefully transferred to differential scanning calorimetry (DSC) capsules to prevent disturbance to electrolyte and film structure, and evaluated for thermal reactivity. DSC showed that PEDOT reduces the heat release of LCO and electrolyte decomposition reactions between 150 and 400 degrees C by nearly 75%, and this reductivity is mostly unaffected by electrolyte content. The tendency for PEDOT to stabilize LCO thermal reactivity indicates that PEDOT may be an effective thermal runaway suppressant in full cell LIB designs.
Bicarbonate electrolyzers are devices that convert CO2 released in situ from bicarbonate ions into chemicals and fuels without requiring an external source of CO2 gas. Among the CO2-derived chemicals and fuels, methane is an appealing target due to its high heating value (802 kJ/mol CO2). A one-dimensional, steady-state, isothermal multiphysics model has been developed for a copper foam-based cathode electrode of a bicarbonate CO2 electrolyzer aimed at methane production. This model considers species transport due to convection, diffusion, and migration and integrates the catalyzed water-splitting reaction at the interface between the anion exchange layer and the cation exchange layer of the bipolar membrane used in the electrolyzer. The simulated polarization curve and Faradaic efficiencies of methane, hydrogen, and formate are compared with published testing data. The effects of cathode design parameters on the Faradaic efficiencies of hydrogen, methane, and formate production are examined. Simulation results reveal that the Faradaic efficiency for hydrogen production improves with an increase in pore radius, interfacial surface area, and the thickness of the copper foam cathode catalyst layer. Conversely, the Faradaic efficiency for methane production benefits from a smaller pore radius, reduced interfacial area, a thinner cathode catalyst layer or cation exchange membrane layer, and a larger cathode flowrate. For instance, at a current density of 200 mA/cm(2), the Faradaic efficiency of methane increases from 16.4% to 18.5% as the pore radius in the cathode catalyst layer decreases from 5 & micro;m to 1 & micro;m. Similar improvements are observed when the interfacial surface area drops from 12 & times; 10(4) m(-1) to 4 & times; 10(4) m(-1), the thickness of the cathode catalyst layer decreases from 300 & micro;m to 200 & micro;m, and the thickness of the cation exchange layer reduces from 100 & micro;m to 50 & micro;m. In these cases, the Faradaic efficiencies for methane increase from 11.1% to 16.4%, from 15.7% to 17.6%, and from 16.4% to 17.5%, respectively. Increasing the cathode flowrate from 50 ml/min to 110 ml/min slightly increases methane Faradaic efficiency from 16.39% to 16.52%. The simulation further indicates that contact resistance in the cathode does not impact Faradaic efficiencies; instead, it affects the polarization curve.
Proton exchange membrane fuel cells and electrolyzers rely on carbon fiber gas diffusion layers (GDLs) for effective reactant transport, water management, and mechanical support. The mechanical integrity behavior of the carbon fiber substrate and the microporous layer (MPL) is critical during assembly due to compression-induced stresses. In this brief, a coupled mechanical model is used that captures the inhomogeneous stress and displacement distributions in the fiber/microporous layer composite structure under compression. The fiber substrate is modeled using 1D beam theory, while the MPL is represented through a 3D finite element method. An artificial composite structure is generated based on microstructural parameters. The model captures localized deformation and stress concentration phenomena consistent with experimental observations. Results reveal that the inhomogeneities in mechanical stiffness due to fiber clustering and MPL intrusion into fiber pores can play a significant role in the overall cell mechanics. This work advances the understanding of GDL’s mechanical behavior and offers insights into improving fuel cell performance and longevity through more robust component design.
Vanadium pentoxide (V2O5) is a promising cathode material for sodium-ion batteries due to its high capacity and layered structure that accommodates Na+ intercalation. Despite this potential, how electrode processing, particularly calendering, affects its electrochemical performance remains insufficiently understood. Here, we systematically investigate V2O5 cathodes with and without calendering using complementary electrochemical and structural characterization. Calendering improved electrode compactness, reduced microcracking, and lowered ohmic resistance, collectively yielding higher initial specific capacity. It slightly improves performance from the second cycle onward but does not substantially mitigate the first-cycle capacity loss. Although calendering reduces charge-transfer resistance upon cycling, calendering exceeding an optimal pressure restricts Na+ transport, reflecting the tradeoff between densification and ion diffusion. Overall, our results demonstrate that calendering is not universally beneficial: while it enhances mechanical integrity and initial electrochemical performance, excessive densification raises interfacial resistance and compromises ionic transport. Careful optimization of calendering conditions is therefore essential for unlocking the full potential of next-generation sodium-ion batteries.
Due to the elimination of solvent removal and recovery, dry electrode processing has enabled significant reductions in energy consumption and cost for lithium-ion battery (LIB) manufacturing. However, transferring this promising manufacturing approach to sodium-ion batteries (SIBs), of which cost is the primary driver for technological development, has been surprisingly rare, with clear knowledge gaps in understanding the process-structure-performance relationship. Here, we investigate the effects of each step (mixing, calendaring, and laminating) during dry processing of an O3-type NaNi0.33Fe0.33Mn0.33O2 (NFM) cathode on the microstructure and electrochemical performance. We highlight the critical role of appropriate mixing for fabricating a high-performance dry-processed electrode. Insufficient mixing may lead to nonuniform distribution of the polytetrafluoroethylene (PTFE) in the electrode composite, which, upon laminating, can migrate to the electrode surface, leading to poor wetting between the liquid electrolyte and the cathode, while excessive mixing can lead to surface degradation of the cathode active materials due to the reduction of Ni. As the wetting ability of liquid electrolyte on the electrode is not a serious concern for LIBs, our work provides novel insights that are specific to sodium cathodes for the development of scalable, low-cost, sustainable dry processes for SIB manufacturing.
Appropriate stack pressure improves the solid-to-solid interfacial stability and prolongs cyclability of the all-solid-state lithium metal batteries (ASSLBs). A low stack pressure is required for the commercialization of ASSLBs, and the key to reduce stack pressure is to regulate the volume variation of the components during charging/discharging. This study establishes a three-dimensional electrochemical-mechanical coupled model to investigate the underlying mechanism of the volume variation of ASSLBs comprised of LiNi0.8Co0.1Mn0.1O2 cathode, Li6PS5Cl solid electrolyte, and lithium metal anode, considering the electrochemical kinetics, Li diffusion, elastic-plastic deformation, and their interplays. Results reveal that lithium plating at the lithium anode surface significantly increases the volumetric strain (>60%) and dominates the overall volume expansion of ASSLBs during charging. Increasing the thickness of the lithium metal anode is beneficial to accommodate the Li deposition through the deformation of soft lithium, reduce the expansion in the thickness direction, and lower the external pressure. The carbon black-binder domain (CBD) with a lower Young's modulus can be used as a buffer layer to adjust the volume change in the cathode domain, which can also effectively alleviate the mechanical stress to reduce possible damage. Increasing the volume fraction of NCM particles can induce a larger volumetric strain of the cathode, resulting in an increase in the expansion of ASSLBs. The developed multiphysics model reveals the underlying electrochemical-mechanical coupled volume variation mechanism of the ASSLBs to provide guidance on the design and fabrication of next-generation ASSLBs under low pressure.
This study describes the efforts taken to determine the variables that are critical to obtain consistent and repeatable results to initiate thermal runaway in Li-ion multicell packages and modules. In this study, lithium primary cells were also studied under similar conditions. A series of tests was conducted to evaluate the effects of various parameters that need to be considered when designing experimental studies to investigate the thermal runaway behaviors of Li-ion batteries. The results show that a heating rate of 5-10 degrees C/min produced the most stable and consistent results compared to higher or lower heating rates, but 10 degrees C/min was chosen as the standard for a majority of the tests. The control thermocouple positioned 5 mm from a heater provided more accurate measurements of the heating rate of the cell avoiding excessively high temperatures near the heater. The comparison between two heating methods, a heater and an oven, showed that the heater offered better control of the heating rate but can be the cause of other unexpected reactions due to venting of semisolids from the cell if the temperatures exceeded a certain limit, whereas the oven heating provided uniform heating across the cell surface by convective heating. The states-of-charge (SOCs) greatly affected the propagation of thermal runaway between Li-ion cells. The results highlight the importance of test configuration and settings which may impact the outcome of the tests.
The performance and stability of all-solid-state batteries (ASSBs) are critically dependent on the mechanical contact at their solid-solid interfaces. Poor contact between electrodes and the solid electrolyte limits the real area available for charge transfer, leading to increased interfacial impedance and performance degradation during cycling. This perspective establishes the current understanding of the fundamental principles of contact mechanics and their application to the dynamic, evolving electrochemical interfaces unique to ASSBs. We summarize recent advances in modeling and characterization techniques that diagnose contact loss and its evolution, which often manifests as a distinct constriction resistance in impedance measurements. Finally, we identify key knowledge gaps and outline future research directions required to overcome these interfacial challenges and advance ASSB development.
Proton exchange membrane fuel cells (PEMFCs) have gained growing attention due to their high energy efficiency and environmental benefits. However, their long-term performance is challenged by cation contaminants such as Co2+ and Fe2+. These species transport into the membrane electrode assembly and competitively occupy sulfonic acid sites in the ionomer, leading to chemical and structural degradation of both the membrane and catalyst layer (CL). Such interference affects ion conductivity, water management, oxygen transport, and consequently the overall fuel cell performance. This review presents a comprehensive overview of cation contaminant sources—including catalyst dissolution, trace impurities, radical scavengers, and leaching from system components—as well as their effects and transport mechanisms within the ionomer phase. Furthermore, this work discusses state-of-the-art mitigation strategies, including material design approaches aimed at restricting cation access, immobilizing cation contaminants, and reducing cation transport rate through the membrane and CL. This review provides a mechanistic foundation for future strategies to enhance the long-term performance of PEMFCs.
Rechargeable Na-based batteries have received tremendous attention as a post-lithium energy storage device due to their lower cost and competitive energy density for grid-storage and transportation applications. However, the practical performance of Na-based batteries suffers from severe chemo-mechanical instabilities in electrode materials associated with structural and interfacial deformations during cycling. Mitigating these instabilities is crucial for achieving higher performance in Na-based batteries for widespread commercialization and industrial utility. This perspective focuses on the state-of-the-art in Na metal and Na-ion batteries, with an emphasis on chemo-mechanical phenomena in electrode materials. The perspective points out the current challenges in hard carbon, alloy, and Na metal anodes as well as transition metal oxides, Prussian blue analogs, and polyanion-type cathodes together with issues related to cathode-anode electrolyte interphase. Advanced characterization techniques are briefly discussed to shed light on the complex instability mechanisms in Na-based electrodes. We also delve into several material-based strategies to design next-generation Na electrodes for improved performance.
Lithium-sulfur (Li-S) batteries combine high specific energy with complex interfacial chemistry, where the dissolution and precipitation of electronically insulating sulfur species define charge transfer pathways. Although transport, shuttle effects, and morphology have been extensively studied, the mechanisms that govern charge transfer through and around precipitates remain poorly understood. This viewpoint integrates experimental and theoretical understanding to explain how substrate-electrolyte interactions, nucleation dynamics, and evolving Li2S topology govern the transition from passivating two-dimensional films to percolated three-dimensional networks that sustain reaction fronts. Electronic defect transport through polaronic conduction and solution-phase electron shuttling by redox mediators are identified as key mechanisms that preserve activity within insulating deposits. Operating conditions and electrolyte composition further affect precipitation modes, determining local kinetics and overall charge transfer resistance. These insights provide a mechanistic framework for quantifying and controlling precipitation-induced charge transfer resistance and morphological evolution in Li-S batteries.
Li-ion batteries' design parameters and material properties, such as porosity, electrode thickness, and solid-phase diffusivities, typically vary substantially due to different design goals as well as variations and defects introduced during manufacturing. Many methods have been used for the parametrization of battery cells to enable accurate simulations using physics-based models, including machine learning (ML)-based methods that have become increasingly popular. However, there are inherent limitations to the type and number of parameters that can be estimated using these methods if only standard charge/discharge protocols are utilized. In this work, the typical battery parameters in a continuum-level physics-based model, namely single particle model (SPM), are estimated using the time-series data generated by simulating constant current-constant voltage (CC-CV) charging at different C-rates. These data are first used to train a long short-term memory neural network (LSTM NN) model and then to predict parameters categorized into three groups: electrode design, transport, and kinetics. The parameters are estimated individually (i.e., only one parameter at a time), concurrently (i.e., multiple parameters at a time), and by combining them into one effective parameter. We demonstrate that physics-informed, targeted weighting of selected segments of time-series data, a task for which ML-based parameter estimation is particularly well suited, can substantially improve the identifiability of specific parameters. We find that simultaneous estimation of multiple parameters can yield acceptable agreement in terms of measurable outputs, but the error in internal states must be paid attention to, especially if internal states are to be used for control purposes within a battery management system. We show that discretization errors arising from the numerical solution of partial differential equations can influence model-generated training data. This can ultimately influence the accuracy of the machine learning model which underscores the importance of an appropriately designed grid convergence study.
In recent decades, cathode materials, significant in both liquid and solid-state lithium-ion and beyond-lithium batteries, are essential for global sustainability due to their unique redox and ionic transport properties. The mass production of cathodes to keep pace with electrochemical energy storage demand has increasingly come under scrutiny. However, the environmental impacts, specifically emissions and waste produced during the synthesis and surface treatment of these materials, have largely been overlooked, even in laboratory settings. This perspective addresses this gap by discussing the importance of adopting entirely dry, waste-free processes for cathode material production. We summarize recent advances in both physical and chemical dry processing techniques and outline potential future research directions in this domain, emphasizing their significance for sustainable battery manufacturing.
The lifetime of lithium-ion batteries (LiBs) is critically influenced by mechanical constraints imposed during module assembly, yet the coupled roles of preload pressure and buffer pads in electrochemical-mechanical degradation remain insufficiently understood. Here, we systematically investigate how preload pressure (0.1-2.0 MPa) and buffer-pad configurations affect the aging behavior of lithium iron phosphate/graphite pouch cells under three representative conditions: fast charging (3C/1C), conventional cycling (1C/1C), and high-temperature calendar storage (60 degrees C, 100% state of charge). A custom force-sensing fixture was employed to monitor expansion force in real time, allowing irreversible mechanical growth to be decoupled into contributions from solid electrolyte interphase (SEI) formation, electrode stiffness increase, and viscoelastic relaxation. Results show that the impact of preload is strongly aging-mode dependent. Under fast charging, low preload maintained the highest capacity retention, whereas excessive preload induced severe stress accumulation due to lithium plating and thus rapid fade. At medium preload, buffer pads redistributed stresses and suppressed irreversible force growth, delaying capacity loss. In contrast, under conventional cycling and calendar aging, preload and buffering exerted only a minor influence on capacity retention, though mechanical relaxation became the dominant process at high preload, particularly in the presence of soft pads. Across all conditions, a medium-to-low preload combined with buffer pads emerged as the most favorable configuration, balancing dynamic cycling stability with static storage durability. These findings highlight the synergistic interplay of SEI growth, stress accumulation, and relaxation in governing battery aging. The results provide actionable design guidance for optimizing preload and buffer-pad selection in pouch-cell modules, supporting safer, longer-lived, and fast-charging-capable LiB systems.
Accurate estimation of the state-of-health (SOH) and remaining useful life (RUL) of lithium-ion batteries is critical for ensuring their reliability, safety, and efficient utilization in energy storage systems in electric vehicles. In this study, a systematically regularized long short-term memory (LSTM)-based model (Model-A) was first developed, integrating dropout, L2 regularization, adaptive learning rate scheduling, and early stopping to mitigate overfitting and enhance model stability. Building upon this foundation, an optimized LSTM framework (Model-B) was proposed, employing randomized grid-search-based hyperparameter optimization to further improve prediction accuracy and generalization. The performance of the proposed model was evaluated using three benchmark NASA battery datasets under various train-test split ratios. The results revealed that Model-B consistently outperformed Model-A, achieving significant reductions in prediction errors and demonstrating robust learning behavior across all configurations. The model achieved the most balanced performance at intermediate split ratios, reflecting an optimal tradeoff between training sufficiency and testing reliability. The predicted RUL values are closely aligned with experimental observations. Moreover, the model maintains stable SOH prediction accuracy under 5% Gaussian noise, indicating robustness to measurement uncertainty and suitability for real-world battery monitoring. Combined with offline training and lightweight online inference, the approach requires minimal computational resources, making it practical for real-time deployment on embedded battery management system edge devices.
Surface-enhanced Raman spectroscopy (SERS) provides orders-of-magnitude signal enhancement, making it a highly sensitive technique for identifying chemical species across a wide range of applications. Over the past decade, SERS has been increasingly applied to study the thin and highly heterogeneous layers of chemical species that form on the surface of electrodes in Li-O-2 and Li-ion batteries. Performing in situ/operando SERS enables direct probing of these interfacial species that are otherwise difficult to detect, providing valuable insight into their formation mechanisms and their impact on battery performance. Thus, this review aims to advance understanding of SERS in Li-O-2 and Li-ion batteries by discussing the fundamental mechanisms underlying signal enhancement, the modifications required to adapt batteries for SERS measurements, and the key insights gained from these investigations.
