Search Results (217)

Silicon oxide/graphite (SiO_x/Gr) anodes are promising candidates for high energy-density lithium-ion batteries. However, their complex multiphysics degradation mechanisms pose challenges for accurately interpreting and predicting capacity fade behavior. In particular, existing multiphysics models typically treat gas generation and solid electrolyte interphase (SEI) growth as independent or unidirectionally coupled processes, neglecting their bidirectional interactions. Here, we develop an electro–thermal–mechanical–gaseous coupled model to capture the dominant degradation processes in SiO_x/Gr anodes, including SEI growth, gas generation, SEI formation on cracks, and particle fracture. Model validation shows that the proposed framework can accurately reproduce voltage responses under various currents and temperatures, as well as capacity fade under different thermal and mechanical conditions. Based on this validated model, a mechanistic analysis reveals two key findings: (1) Gas generation and SEI growth are bidirectionally coupled. SEI growth induces gas release, while accumulated gas in turn regulates subsequent SEI evolution by promoting SEI formation through hindered mass transfer and suppressing it through reduced active surface area. (2) Crack propagation within particles is jointly governed by the magnitude and duration of stress. High-rate discharges produce large but transient stresses that restrict crack growth, while prolonged stresses at low rates promote crack propagation and more severe structural degradation. This study provides new insights into the coupled degradation mechanisms of SiO_x/Gr anodes, offering guidance for performance optimization and structural design to extend battery cycle life. Full article

The development of lithium-ion batteries necessitates cathode materials that possess excellent mechanical and thermal properties in addition to electrochemical performance. As a prominent functional ceramic, the properties of spinel LiMn₂O₄ are governed by its atomic-level structure. This study systematically investigates the impact of Ni doping concentration on the mechanical and thermal properties of spinel LiNi_xMn_2−xO₄ via first-principles calculations combined with the bond valence model. The results suggest that when x = 0.25, the LiNi_xMn_2−xO₄ shows excellent mechanical properties, including a high bulk modulus and hardness, due to the favorable ratio of bond valence to bonds length in octahedra. Furthermore, this optimized composition shows a lower thermal expansion coefficient. Additionally, Ni doping concentration has a very minimal influence on the maximum tolerable temperature of the cathode material during rapid heating. Therefore, from the perspective of mechanical and thermal properties, this composition could be beneficial for improving the cycling life of the battery, since comparatively inferior mechanical properties and a higher thermal expansion coefficient make it prone to microcrack formation during charge–discharge cycles. Full article

The global shift toward renewable energy and circular economy models requires industrial systems that minimize waste and recover value across entire life cycles. This review synthesizes recent advances in by-product recovery technologies supporting renewable energy and circular industrial processes. Thermal, biological, chemical/electrochemical, and biotechnological routes are analyzed across battery and e-waste recycling, bioenergy, wastewater, and agri-food sectors, with emphasis on integration through Life Cycle Assessment (LCA), techno-economic analysis (TEA), and multi-criteria decision analysis (MCDA) coupled to process simulation, digital twins, and artificial intelligence tools. Policy and economic frameworks, including the European Green Deal and the Critical Raw Materials Act, are examined in relation to technology readiness and environmental performance. Hybrid recovery systems, such as pyro-hydro-bio configurations, enable higher resource efficiency and reduced environmental impact compared with stand-alone routes. Across all technologies, major hotspots include electricity demand, reagent use, gas handling, and concentrate management, while process integration, heat recovery, and realistic substitution credits significantly improve life cycle outcomes. Harmonized LCA-TEA-MCDA frameworks and digitalized optimization emerge as essential tools for scaling sustainable, resource-efficient, and low-impact industrial ecosystems consistent with circular economy and renewable energy objectives. Full article

A cobalt-containing zeolitic imidazolate framework (ZIF-67) was carbonized by different routes to composite materials (cZIFs) composed of metallic Co, Co₃O₄, and N-doped carbonaceous phase. The effect of the carbonization procedure on the water pollutant removal properties of cZIFs was studied. Higher temperature and prolonged thermal treatment resulted in more uniform particle size distribution (as determined by nanoparticle tracking analysis, NTA) and surface charge lowering (as determined by zeta potential measurements). Surface-governed environmental applications of prepared cZIFs were tested using physical (adsorption) and electrochemical methods for dye degradation. Targeted dyes were methylene blue (MB) and methyl orange (MO), chosen as model compounds to establish the specificity of selected remediation procedures. Electrodegradation was initiated via an intermediate reactive oxygen species formed during oxygen reduction reaction (ORR) on cZIFs serving as electrocatalysts. The adsorption test showed relatively uniform adsorption sites at the surface of cZIFs, reaching a removal of over 70 mg/g for both dyes while governed by pseudo-first-order kinetics favored by higher mesoporosity. In the electro-assisted degradation process, cZIF samples demonstrated impressive efficiency, achieving almost complete degradation of MB and MO within 4.5 h. Detailed analysis of energy consumption in the degradation process enabled the calculation of the current conversion efficiency index and the amount of charge associated with O₂^•−/^•OH generation, normalized by the quantity of removed dye, for tested materials. Here, the proposed method will assist similar research studies on the removal of organic water pollutants to discriminate among electrode materials and procedures based on energy efficiency. Full article

Lithium batteries have emerged as the mainstream technology in the current energy storage field due to their advantages, such as high energy density and long cycle life. However, from a multi-physics coupling perspective, research remains relatively scarce regarding the analysis of dendrite nucleation and growth, as well as their influence on lithium dendrite growth. Based on the phase field theory, this study develops a mechanical-thermal-electrochemical coupling model to systematically investigate the evolution mechanisms and suppression strategies of lithium dendrites induced by relevant physical quantities through the coupled effects of mechanical, thermal, and electrochemical fields. The dynamic behavior of the solid-solid interface is characterized by introducing order parameters. The governing nonlinear partial differential equations are formulated by combining the Cahn-Hilliard and Ginzburg-Landau equations. The present numerical results and the previous results are compared to validate the present model in properly predicting lithium dendrite growth. Numerical simulations are performed to analyze the influence of various physical parameters, such as electric potential, anisotropic intensity and anisotropic modulus, on the morphological evolution of lithium dendrites. These findings provide critical insights for advancing strategies to suppress lithium dendrite growth and enhance battery performance in solid-state lithium batteries under multi-field coupling conditions. Full article

This paper investigates pouch-type lithium-ion battery cells with a nominal voltage of 3.7 V and a nominal capacity of 57 Ah. A numerical model of the cell was developed and implemented using the NTGK method, which accurately predicts electrochemical and thermal processes. The results of numerical modeling matched with the experimental results of battery cell temperature measurements—the average deviation was about 4.5%; therefore, it can be considered reliable for further engineering research and construction of battery modules. In the experimental part of the paper, the battery cell was loaded in various C-rates (from 0.5 to 2 C), using heat flux sensors, thermocouples, and a thermal imaging camera. The studies revealed that the highest temperature is in the tabs area of cells. The temperature on the face of the cell surface exceeds 35 °C already from a load of 1.35 C, which accelerates cell degradation and reduces the number of cycles. Thermal imaging revealed uneven temperature distribution, whereby the top of the cell heats up more than the bottom of the cell and the temperature gradient can reach 2–4 °C. It was observed that during faster charge/discharge modes, the temperature rises from the tabs of the cell, and during slower ones, more in the middle face surface of the cell. The studies highlight the need to apply additional cooling solutions, especially cooling of the upper cell face, to ensure durability and uniform heat distribution. Full article

Vanadium redox flow batteries, as a key technology for energy storage systems, have gained application in recent years. Investigating the thermal behavior and performance of these batteries is crucial. This study establishes a three-dimensional model of a vanadium redox flow battery featuring a serpentine flow channel design. By adjusting key battery parameters, changes in ion concentration and uniformity are examined. The model integrates electrochemical, fluid dynamics, and Physico-Chemical Kinetics phenomena. Electrolyte flow velocity and current density are critical parameters. Results indicate that increasing the electrolyte inlet flow velocity leads to convergence in the battery’s charge/discharge cell voltage, ${\mathrm{VO}}_2^{+}{/\mathrm{VO}}^{2+}$, ${\mathrm{V}}^{2+}{/\mathrm{V}}^{3+}$ and concentration distribution across the carbon felt and flow channels. Coincidently, the uniformity of vanadium ions across all oxidation states improves. Furthermore, the observed ion uniformity and battery cell voltage are shown to be significantly modulated by the system’s State of Charge, which sets the baseline electrochemical environment for flow rate effects. Full article

Accurately forecasting the operating temperature of lithium-ion batteries (LIBs) is essential for preventing thermal runaway, extending service life, and ensuring the safe operation of electric vehicles and stationary energy-storage systems. This work introduces a unified, physics-informed, and data-driven temperature-prediction framework that integrates mathematically governed preprocessing, electrothermal decomposition, and sequential deep learning architectures. The methodology systematically applies the governing relations to convert raw temperature measurements into trend, seasonal, and residual components, thereby isolating long-term thermal accumulation, reversible entropy-driven oscillations, and irreversible resistive heating. These physically interpretable signatures serve as structured inputs to machine learning and deep learning models trained on temporally segmented temperature sequences. Among all evaluated predictors, the Bidirectional Long Short-Term Memory (BiLSTM) network achieved the highest prediction fidelity, yielding an RMSE of 0.018 °C, a 35.7% improvement over the conventional Long Short-Term Memory (LSTM) (RMSE = 0.028 °C) due to its ability to simultaneously encode forward and backward temporal dependencies inherent in cyclic electrochemical operation. While CatBoost exhibited the strongest performance among classical regressors (RMSE = 0.022 °C), outperforming Random Forest, Gradient Boosting, Support Vector Regression, XGBoost, and LightGBM, it remained inferior to BiLSTM because it lacks the capacity to represent bidirectional electrothermal dynamics. This performance hierarchy confirms that LIB thermal evolution is not dictated solely by historical load sequences; it also depends on forthcoming cycling patterns and entropic interactions, which unidirectional and memoryless models cannot capture. The resulting hybrid physics-data-driven framework provides a reliable surrogate for real-time LIB thermal estimation and can be directly embedded within BMS to enable proactive intervention strategies such as predictive cooling activation, current derating, and early detection of hazardous thermal conditions. By coupling physics-based decomposition with deep sequential learning, this study establishes a validated foundation for next-generation LIB thermal-management platforms and identifies a clear trajectory for future work extending the methodology to module- and pack-level systems suitable for industrial deployment. Full article

Lithium-ion batteries (LIBs) dominate energy storage for electric vehicles (EVs) due to their high energy density, long cycle life, and low self-discharge. However, high costs, complex manufacturing, and the requirement for advanced battery management systems (BMSs) constrain their broader deployment. Therefore, extending the utility of LIBs through reuse is essential for economic and environmental sustainability. Retired EV batteries with 70–80% state-of-health (SOH) can be repurposed in battery energy storage systems (BESSs) to support power grids. Effective reuse depends on accurate and rapid assessment of SOH and state-of-safety (SOS), which relies on precise state-of-charge (SOC) detection, particularly for aged LIBs with elevated thermal and electrochemical risks. This review systematically surveys SOC, SOH, and SOS detection methods for second-life LIBs, covering model-based, data-driven, and hybrid approaches, and highlights strategies for a fast and reliable evaluation. It further examines power electronics topologies and control strategies for integrating second-life LIBs into power grids, focusing on safety, efficiency, and operational performance. Finally, it analyzes key factors within the closed-loop supply chain, particularly reverse logistics, and provides guidance on enhancing adoption and supporting the establishment of circular battery ecosystems. This review serves as a comprehensive resource for researchers, industry stakeholders, and policymakers aiming to optimize second-life utilization of traction LIBs. Full article

With the continuous expansion of the electric vehicle market, lithium-ion batteries have also been rapidly developed, but this has brought about concerns over the safety of lithium-ion batteries. Research on the correlation mechanism between the expansion and safety of lithium-ion batteries is a key step in the construction of a battery life cycle safety evaluation system. In this paper, the physicochemical mechanism of early safety faults in batteries was analyzed from three dimensions of electricity, heat, and force. The interactions of electrochemical side reactions, thermal runaway chain reactions, and mechanical fault mechanisms were analyzed, and the core induction of early safety risk was explored. A battery coupling model based on electrical, thermal, and mechanical dimensions was built, and the accuracy of the coupling model was verified by a variety of test conditions. Based on the coupling model, the stress distribution of the battery under different safety boundary conditions was simulated, and then the average expansion force of the battery surface was calculated through the stress distribution results. Through this process, a multi-parameter database based on the test and simulation data was obtained. According to the data of battery parameters at different times, an early safety classification method based on the battery expansion force was proposed, and a classification model between battery dimension data and safety level was proposed based on the nonlinear dynamic sparse regression method, and the classification accuracy was validated. From the perspective of fault warning, by establishing a multi-physical coupling model of electrical, thermal, and mechanical fields, the space-time evolution law of battery expansion under different working conditions can be dynamically monitored, and the fault criterion based on the expansion force can be established accordingly to provide quantitative indicators for safety risk classification warnings, and improve the battery’s reliability and durability. Full article

The rise in thermal runaway events within electric vehicle (EV) battery systems requires anticipatory models to predict critical safety failures during operation. This investigation develops a multi-physics digital twin framework that links electrochemical, thermal, and structural domains to replicate the internal dynamics of lithium-ion packs in both normal and faulted modes. Coupled simulations distributed among MATLAB 2024a, Python 3.12-powered three-dimensional visualizers, and COMSOL 6.3-style multi-domain solvers supply refined spatial resolution of temperature, stress, and ion concentration profiles. While the digital twin architecture is designed to accommodate different battery chemistries and pack configurations, the numerical results reported in this study correspond specifically to a lithium NMC-based 4S3P cylindrical cell module. Quantitative benchmarks show that the digital twin identifies incipient thermal deviation with 97.4% classification accuracy (area under the curve, AUC = 0.98), anticipates failure onset within a temporal margin of ±6 s, and depicts spatial heat propagation through three-dimensional isothermal surface sweeps surpassing 120 °C. Mechanical models predict casing strain concentrations of 142 MPa, approaching polymer yield strength under stress load perturbations. A unified operator dashboard delivers diagnostic and prognostic feedback with feedback intervals under 1 s, state-of-health (SoH) variance quantified by a root-mean-square error of 0.027, and mission-critical alerts transmitting with a mean latency of 276.4 ms. Together, these results position digital twins as both diagnostic archives and predictive safety envelopes in the evolution of next-generation EV architectures. Full article

Corrosion of carbon steel in acidic media remains a critical challenge during acidizing operations. This study evaluates carrot and rosemary extracts—individually and in combination—as green corrosion inhibitors for carbon steel in 1 M HCl. Inhibition performance was assessed using weight loss, potentiodynamic polarization (PDP), electrochemical impedance spectroscopy (EIS), SEM/EDS, and adsorption isotherms. Weight-loss measurements showed inhibition efficiencies of 59.5% (carrot) and 85.7% (rosemary) at 800 ppm, while their 30/70 mixture achieved a markedly higher efficiency of 99.6%. PDP results confirmed this trend, with corrosion current density decreasing from 892 μA/cm² (blank) to 13.4 μA/cm² for the mixture, corresponding to 98.5% efficiency. In addition, EIS analysis revealed a substantial increase in charge-transfer resistance from 41.1 ohm·cm² (blank) to 174.9 ohm·cm² (carrot), 266.9 ohm·cm² (rosemary), and 1868.1 ohm·cm² for the 30/70 mixture, confirming superior barrier formation. Moreover, temperature-dependent tests showed only a 5% efficiency loss for the mixture and an average 6% decrease for the single extracts between 25–45 °C, indicating good thermal stability. Also, SEM images demonstrated severe surface damage in the blank sample, while carrot-, rosemary-, and mixture-treated surfaces showed progressively smoother morphologies. EDS analysis confirmed this trend, with Fe content increasing from 65.78% (blank) to 90.16% (carrot), 91.88% (rosemary), and 94.59% for the mixture. Furthermore, FTIR and GC–MS identified oxygenated functional groups and major phytochemicals responsible for adsorption. Adsorption data followed the Langmuir model, and Gibbs free energy values from −25 to −31 KJ/mol indicated spontaneous mixed physisorption–chemisorption. Overall, the 30/70 carrot–rosemary mixture consistently achieved the highest corrosion protection across all tests, confirming strong synergistic adsorption and demonstrating its potential as a high-performance, eco-friendly inhibitor for acidic environments. Full article

Developing smart fast charging strategies can effectively balance the charging efficiency and battery performance. The current mainstream method is to optimize fast charging protocols based on electrochemical models that quantitatively detect lithium deposition. This paper utilizes a high-fidelity battery digital twin model to extract the triggering potential of lithium deposition and thus construct a lithium deposition curve. Based on this, a health-aware fast charging strategy (FCS) is developed to analyze the electro-thermal aging behavior of batteries under various health factors. Subsequently, the influence of the number of steps in the charging protocol on battery aging is examined, leading to the development of a real-time health-aware FCS. The results show that although capacity loss caused by side reactions gradually increases with the number of charging steps, the time required to complete the fast charging process is significantly reduced. Furthermore, compared to the 2 C constant current charging strategy, the proposed health-aware variable current profile (VCP) charging strategy with a health factor of 20% demonstrates excellent performance in reducing capacity loss caused by lithium deposition, achieving up to a 17.59% reduction in capacity loss and effectively reducing capacity loss due to side reactions by 15.4%. Full article

Battery health monitoring is essential for ensuring the safety, longevity, and efficiency of energy storage systems, particularly in critical applications where reliability is important. Traditional methods for assessing battery degradation, such as Electrochemical Impedance Spectroscopy (EIS), are effective but impractical for large-scale deployment due to their time-intensive nature. This study introduces a novel model-based approach for estimating a critical indicator of battery aging, the internal resistance. Using the NASA battery dataset, specifically focusing on battery numbers 5 and 7 with NCA chemistry, a comprehensive framework that integrates advanced predictive models, i.e., the Random Forest Regressor (RF), the XGBoost Regressor (XGBR), the Gated Recurrent Unit (GRU), and the Long Short-Term Memory (LSTM) networks, was developed. The models were evaluated using common regression metrics, while hyperparameter tuning was performed to optimize performance. The results demonstrated that recurrent neural networks, particularly GRU and LSTM, effectively capture the temporal dependencies inherent in battery aging, offering more accurate state-of-health (SOH) predictions. This approach significantly improves computational efficiency and prediction accuracy, paving the way for practical applications in Battery Management Systems (BMSs). Full article

Energy storage systems (ESSs) and electric vehicle (EV) batteries depend on battery management systems (BMSs) for their longevity, safety, and effectiveness. Battery modeling is crucial to the operation of BMSs, as it enhances temperature control, fault detection, and state estimation, thereby maximizing efficiency and preventing malfunctions. This paper thoroughly examines the most recent advancements in battery and BMS modeling, including data-driven, thermal, and electrochemical methods. Advanced modeling approaches are explored, including physics-based models that incorporate mechanical stress and aging effects, as well as artificial intelligence (AI)-driven state estimation. New technologies that facilitate data-driven decision-making, real-time monitoring, and simplified systems include digital twins (DTs), cloud computing, and wireless BMSs. Nonetheless, there are still issues with cost optimization, cybersecurity, and computing efficiency. This study presents key advancements in battery modeling and BMS applications, including defect diagnostics, temperature management, and state-of-health (SOH) prediction. A comparison of machine learning (ML) methods for SOH prediction is given, emphasizing how well neural networks (NNs) and transfer learning function with real-world datasets. Additionally, future research objectives are described, with an emphasis on next-generation sensor technologies, cloud-based BMSs, and hybrid algorithms. Distinct from existing reviews, this paper integrates academic modeling with industrial benchmarking and highlights the convergence of hybrid physics-informed and data-driven techniques, multi-physics simulations, and intelligent architecture. For high-performance EV applications, this analysis offers insight into creating more intelligent, adaptable, and secure BMSs by addressing current constraints and utilizing state-of-the-art technologies. Full article