Search Results (3,480)

Ion-conductive-hydrogel strain sensors demonstrate broad application prospects in the fields of flexible sensing and bioelectric signal monitoring due to their excellent skin conformability and efficient signal transmission characteristics. However, traditional preparation methods face significant challenges in enhancing adhesion strength, conductivity, and mechanical stability. To address this issue, this study employed a freeze–thaw cycling method, using polyvinyl alcohol (PVA) as the matrix material, tannic acid (TA) as the adhesion reinforcement material, and lithium chloride (LiCl) as the conductive medium, successfully developing an ion-conductive hydrogel with superior comprehensive performance. Experimental data confirm that the PVA-TA-0.5/LiCl-1 hydrogel achieves optimal levels of adhesion strength (2.32 kPa on pigskin) and conductivity (0.64 S/m), while also exhibiting good tensile strength (0.1 MPa). Therefore, this hydrogel shows great potential for use in strain sensors, demonstrating excellent sensitivity (GF = 1.15), reliable operational stability, as the ΔR/R₀ signal remains virtually unchanged after 2500 cycles of stretching, and outstanding strain sensing and electromyographic signal acquisition capabilities, fully highlighting its practical value in the fields of flexible sensing and bioelectric monitoring. Full article

This paper presents a novel data-driven framework for real-time State of Charge (SOC) estimation in lithium-ion battery systems using a data-driven Prognostics and Health Management (PHM) approach. The method leverages an optimized bidirectional Long Short-Term Memory (Bi-LSTM) network, trained with enhanced datasets filtered via exponentially weighted moving averages (EWMAs) and refined through SHAP-based feature attribution. Compared against a Extended Kalman Filter (EKF) and Unscented Kalman Filter (UKF) across ten diverse drive cycles, the proposed model consistently achieved superior performance, with mean absolute errors (MAEs) as low as 0.40%, outperforming EKF (0.66%) and UKF (1.36%). The Bi-LSTM model also demonstrated higher R² values (up to 0.9999) and narrower 95% confidence intervals, confirming its precision and robustness. Real-time implementation on embedded platforms yielded inference times of 1.3–2.2 s, validating its deployability for edge applications. The framework’s model-free nature makes it adaptable to other nonlinear, time-dependent systems beyond battery SOC estimation. Full article

The energy generated from renewable sources has an intermittent nature since solar irradiation and wind speed vary continuously. Hence, their energy should be stored to be utilized throughout their shortage. There are various forms of energy storage systems while the most widespread technique is the battery storage system since its cost is low compared to other techniques. Therefore, batteries are employed in several applications like power systems, electric vehicles, and smart grids. Due to the merits of the lithium-ion (Li-ion) battery, it is preferred over other kinds of batteries. However, the accuracy of the Li-ion battery model is essential for estimating the state of charge (SOC). Additionally, it is essential for consistent simulation and operation throughout various loading and charging conditions. Consequently, the determination of real battery model parameters is vital. An innovative application of the red-billed blue magpie optimizer (RBMO) for determining the model parameters and the SOC of the Li-ion battery is presented in this article. The Shepherd model parameters are determined using the suggested optimization algorithm. The RBMO-based modeling approach offers excellent execution in determining the parameters of the battery model. The suggested approach is compared to other programmed algorithms, namely dandelion optimizer, spider wasp optimizer, barnacles mating optimizer, and interior search algorithm. Moreover, the suggested RBMO is statistically evaluated using Kruskal–Wallis, ANOVA tables, Friedman rank, and Wilcoxon rank tests. Additionally, the Li-ion battery model estimated via the RBMO is validated under variable loading conditions. The fetched results revealed that the suggested approach achieved the least errors between the measured and estimated voltages compared to other approaches in two studied cases with values of 1.4951 × 10⁻⁴ and 2.66176 × 10⁻⁴. Full article

In this study, we successfully synthesized olivine-type FePO₄ via an in situ oxidation method and further developed two composite cathode materials (o-FePO₄-1/GR-1 and o-FePO₄-1/GR-2) by incorporating graphene. The composites were characterized using scanning electron microscopy (SEM), X-ray diffraction (XRD), and X-ray Photoelectron Spectroscopy (XPS), revealing a three-dimensional porous layered structure with an enhanced surface area and strong interaction between FePO₄ nanoparticles and graphene layers. Electrochemical tests demonstrated that the composite electrodes exhibited significantly improved performance compared to pristine FePO₄, with discharge capacities of 147 mAh g⁻¹ at 1C and 163 mAh g⁻¹ at 0.1C for o-FePO₄-1/GR-2, approaching the level of LiFePO₄. The incorporation of graphene effectively enhanced the electrochemical reaction kinetics, highlighting the innovation of our method in developing high-performance cathode materials for lithium-ion batteries. Full article

In this work, graphite anodes and lithium iron phosphate (LFP) cathodes are used to examine the effects of sodium hexafluorophosphate (NaPF₆) and potassium hexafluorophosphate (KPF₆) electrolyte additives on the formation of the solid electrolyte interphase and the performance of lithium-ion batteries in both half-cell and full-cell designs. The objective is to assess whether these additives may increase cycle performance, decrease irreversible capacity loss, and improve interfacial stability. Compared to the control electrolyte (1.22 M Lithium hexafluorophosphate (LiPF₆)), cells with NaPF₆ and KPF₆ additives produced less SEI products, which decreased irreversible capacity loss and enhanced initial coulombic efficiency. Following the formation of the solid electrolyte interphase, the specific capacity of the control cell was 607 mA·h/g, with 177 mA·h/g irreversible capacity loss. In contrast, irreversible capacity loss was reduced by 38.98% and 37.85% in cells containing KPF₆ and NaPF₆ additives, respectively. In full cell cycling, a considerable improvement in capacity retention was achieved by adding NaPF₆ and KPF₆. The electrolyte, including NaPF₆, maintained 67.39% greater capacity than the LiPF₆ baseline after 20 cycles, whereas the electrolyte with KPF₆ demonstrated a 30.43% improvement, indicating the positive impacts of these additions. X-ray photoelectron spectroscopy verified that sodium (Na⁺) and potassium (K⁺) ions were present in the SEI of samples containing NaPF₆ and KPF₆. While K⁺ did not intercalate in LFP, cyclic voltammetry confirmed that Na⁺ intercalated into LFP with negligible impact on the energy storage of full cells. These findings demonstrate that NaPF₆ and KPF₆ are suitable additions for enhancing lithium-ion battery performance in the popular artificial graphite/LFP system. Full article

Polyethylene oxide (PEO) is the most well-studied polymer used in solid polymer electrolytes (SPEs) for lithium ion batteries (Li-ion batteries). However, ionic conductivity is greatly reduced in the low temperature range due to the crystallization of PEO. Therefore, methods to suppress the crystallization of PEO at room temperature by cross-linking or introducing a branched structure are currently being investigated. In this study, we synthesized new comb-type ion-conducting polyethers with two different side chains such as polydimethylsiloxane (PDMS) and polyethylene glycol monomethyl ether (mPEG) segments as flexible and ion-conducting segments, respectively. The introduction of the PDMS segment was found to prevent a decrease in ionic conductivity in the low-temperature region, but led to an ionic conductivity decrease in the high temperature region. On the other hand, the introduction of mPEG segments improved ionic conductivity in the high-temperature region. The introduction of mPEG segments with longer chains resulted in a significant decrease in ionic conductivity in the low-temperature region. Full article

Understanding the early-stage physical interactions between polymeric membranes and supersaturated salt solutions is crucial for advancing membrane-assisted crystallization (MCr) processes. In this study, we employed molecular dynamics (MD) simulations to investigate the short-term morphological response of an isotactic polypropylene (PP) membrane in contact with LiF solutions at different concentrations (5.8 M and 8.9 M) and temperatures (300–353 K), across multiple time points (0, 150, and 300 ns). These data were used as input for computational fluid dynamics (CFD) analysis to evaluate structural descriptors of the membrane, including tortuosity, connectivity, void fraction, anisotropy, and deviatoric anisotropy, under varying thermodynamic conditions. The results show subtle but consistent rearrangements of polymer chains upon exposure to the hypersaline environment, with a marked reduction in anisotropy and connectivity, indicating a more compact and isotropic local structure. Surface charge density analyses further suggest a temperature- and concentration-dependent modulation of chain mobility and terminal group orientation at the membrane–solution interface. Despite localized rearrangements, the membrane consistently maintains a net negative surface charge. This electrostatic feature may influence ion–membrane interactions during the crystallization process. While these non-reactive, short-timescale simulations do not capture long-term degradation or fouling mechanisms, they provide mechanistic insight into the initial physical response of PP membranes under MCr-relevant conditions. This study lays a computational foundation for future investigations bridging atomistic modeling and membrane performance in real-world applications. Full article

Large-scale battery datasets often contain anomalous data due to sensor noise, communication errors, and operational inconsistencies, which degrade the accuracy of data-driven prognostics. However, many existing studies overlook the impact of such anomalies or apply filtering heuristically without rigorous benchmarking, which can potentially introduce biases into training and evaluation pipelines. This study presents a deep learning framework that integrates autoencoder-based anomaly detection with a residual neural network (ResNet) to achieve state-of-the-art prediction of remaining useful life at the cycle level using only a single-cycle input. The framework systematically filters out anomalous samples using multiple variants of convolutional and sequence-to-sequence autoencoders, thereby enhancing data integrity before optimizing and training the ResNet-based models. Benchmarking against existing deep learning approaches demonstrates a significant performance improvement, with the best model achieving a mean absolute percentage error of 2.85% and a root mean square error of 40.87 cycles, surpassing prior studies. These results indicate that autoencoder-based anomaly filtering significantly enhances prediction accuracy, reinforcing the importance of systematic anomaly detection in battery prognostics. The proposed method provides a scalable and interpretable solution for intelligent battery management in electric vehicles and energy storage systems. Full article

This study presents a hybrid method for state-of-charge (SOC) estimation of lithium-ion batteries using LSTM neural networks optimized with genetic algorithms (GA), combined with Coulomb Counting (CC) as an initial estimator. Experimental tests were conducted using medium-voltage (48–72 V) lithium-ion battery packs under standardized driving cycles (NEDC and WLTP). The proposed method enhances prediction accuracy under dynamic conditions by recalibrating the LSTM output with CC estimates through a dynamic fusion parameter $\alpha$. The novelty of this approach lies in the integration of machine learning and physical modeling, optimized via evolutionary algorithms, to address limitations of standalone methods in real-time applications. The hybrid model achieved a mean absolute error (MAE) of 0.181%, outperforming conventional estimation strategies. These findings contribute to more reliable battery management systems (BMS) for electric vehicles and second-life applications. Full article

This study aims to develop a method for analyzing real-world battery degradation in electric vehicles in order to identify the optimal battery management system (BMS) during the early digital phase of vehicle development. Battery management of lithium-ion batteries (LiBs) in electric vehicles is important to ensure a stable output and to counteract degradation and thermal runaway. To design the optimal system, it is most effective to use a 1D (one-dimensional) vehicle system simulation model, which connects each unit model inside the vehicle, due to the system’s complexity. In order to create a long-term degradation simulation in a vehicle system model, it is important to reduce computational load. Therefore, in this paper, we studied a suitable battery degradation calculation for the vehicle system model based on an equivalent circuit model (ECM) and degradation approximation formulas. After implementing these models, we analyzed long-term degradation behavior through the real-world operation of an electric vehicle driver. We first implemented a high-accuracy ECM using transient charge–discharge tests and Bayesian Optimization. Next, we formulated approximation formulas for degradation prediction based on calendar and cycle degradation tests. Finally, we simulated real-world degradation behavior using these models. The simulation results revealed that even for users who frequently use electric vehicles, degradation under storage conditions is the dominant factor in overall degradation. Full article

Lithium-ion batteries (LiBs) are utilized in numerous applications due to advancements in technology, and the recovery of end-of-life (EoL) LiBs is imperative for environmental and economic reasons. Pyrometallurgical and hydrometallurgical methods have been used in the recovery of metals such as Li, Co, and Ni in the EoL LiBs. Hydrometallurgical methods, which have been demonstrated to exhibit higher recovery efficiency and reduced energy consumption, have garnered increased attention in recent research. Inorganic acids, including HCl, HNO₃, and H₂SO₄, as well as organic acids such as acetic acid and citric acid, are employed in the hydrometallurgical recovery of these metals. It is imperative to acknowledge the environmental hazards posed by these acids. Consequently, solvometallurgical processes, which involve the use of organic solvents with minimal or no water, are gaining increasing attention as alternative or complementary techniques to conventional hydrometallurgical processes. In the context of solvent systems that have been examined for a range of solvometallurgical methods, deep eutectic solvents (DESs) have garnered particular interest due to their low toxicity, biodegradable nature, tunable properties, and efficient metal recovery potential. In this study, the leaching process of black mass containing graphite, LCO, NMC, and LMO was carried out in a short time using the ternary DES system. The ternary DES system consists of choline chloride (ChCl), glycolic acid (GLY), and ascorbic acid (AA). As a result of the leaching process of cathode powders in the black mass without any pre-enrichment process, Li, Co, Ni, and Mn elements passed into solution with an efficiency of over 95% at 60 °C and within 1 h. Moreover, the kinetics of the leaching process was investigated, and Density Functional Theory (DFT) calculations were used to explain the leaching mechanism. Full article

To address lithium extraction from clay-type lithium ore from the Qaidam Basin, this study identified key controlling factors through particle fractionation, acid-leaching–roasting experiments, and mineral characterization. The results demonstrate that particle size optimization enriched the lithium to 87.65 ppm, where a 74% leaching rate was achieved with 65 ppm extraction, which established intermediate-sized samples as optimal. During acid leaching, adsorbed lithium ions with a phyllosilicate interlayer were released via the ion exchange process instead of mineral dissolution, as verified by the Li-O/S-O peak shifts in the FTIR spectra. The roasting induced hydroxyl elimination, carbonate decomposition, and silicate restructuring but triggered lithium encapsulation via mineral phase reorganization, which caused a sharp leaching rate decline. Effective lithium extraction requires integrated particle size screening, acid-leaching optimization, and roasting-induced phase encapsulation disruption. This study established theoretical foundations for clay-type lithium ore exploitation. Full article

Surface microstructure of grains vastly decides the electrochemical performance of nickel-rich oxide cathodes, which can improve their interfacial kinetics and structural stability to realize their further popularization. Herein, taking the representative LiNi_0.8Co_0.15Al_0.05O₂ (NCA) materials as an example, a surface heterojunction structure construction strategy to enhance the interface characteristics of high-nickel materials by introducing interfacial ZnO sites has been designed (NCA@ZnO). Impressively, this heterointerface creates a strong built-in electric field, which significantly improves electron/Li-ion diffusion kinetics. Concurrently, the ZnO layer acts as an effective physical barrier against electrolyte corrosion, notably suppressing interfacial parasitic reactions and ultimately optimizing the structure stability of NCA@ZnO. Benefiting from synchronous optimization of interface stability and kinetics, NCA@ZnO exhibits advanced cycling performance with the capacity retention of 83.7% after 160 cycles at a superhigh rate of 3 C during 3.0–4.5 V. The prominent electrochemical performance effectively confirms that the surface structure design provides a critical approach toward obtaining high-performance cathode materials with enhanced long-cycling stability. Full article

Incorporation of lanthanide (Ln) and actinide (An) ions into the human body poses significant chemotoxic and radiotoxic risks, necessitating effective decorporation strategies. This study investigates the displacement of biologically relevant ligands from trivalent ions of europium, Eu(III), and curium, Cm(III), in artificial biofluids by various complexing agents, i.e., ethylenediaminetetraacetic acid (EDTA), ethylene glycol-bis(β-aminoethyl ether)-N,N,N′,N′-tetraacetic acid (EGTA), diethylenetriaminepentaacetic acid (DTPA), and spermine-based hydroxypyridonate chelator 3,4,3-LI(1,2-HOPO) (HOPO). Utilizing a modified unified bioaccessibility method (UBM) to simulate gastrointestinal conditions, we conducted concentration-dependent displacement experiments at both room and body temperatures. Time-resolved laser-induced fluorescence spectroscopy (TRLFS) supported by ²H nuclear magnetic resonance (NMR) spectroscopy and thermodynamic modelling revealed the complexation efficacy of the agents under physiological conditions. Results demonstrate that high affinity, governed by complex stability constants and ligand pK_a values, is critical to overcome cation and anion competition and leads to effective decorporation. Additionally, there is evidence that cyclic ligands are inferior to linear ligands for this application. HOPO and DTPA exhibited superior displacement efficacy, particularly in the complete gastrointestinal tract simulation. This study highlights the utility of in vitro workflows for evaluating decorporation agents and emphasizes the need for ligands with optimal binding characteristics for enhanced chelation therapies. Full article

Fast charging accelerates lithium-ion battery operation but increases the risk of lithium (Li) plating—a process that undermines efficiency, longevity, and safety. Here, we introduce a predictive modeling framework that captures the onset and severity of Li plating under practical fast-charging conditions. By integrating an empirically parameterized SOC threshold model with time-dependent kinetic simulations and Arrhenius based thermal analysis, we delineate operating regimes prone to irreversible Li accumulation. The framework distinguishes reversible and irreversible plating fractions, quantifies energy losses, and identifies a critical activation energy (0.25 eV) associated with surface-limited deposition. Visualizations in the form of severity maps and voltage-zone risk classifications enable direct application to battery management systems. This approach bridges electrochemical degradation modeling with real-time charge protocol design, offering a practical tool for safe, high-performance battery operation. Full article