Search Results (3,923)

The increasing demand for lithium-ion batteries (LIBs) highlights the need for efficient recycling strategies to recover critical materials and reduce environmental impact. This study presents an optimized hydrometallurgical process for the recovery of lithium (Li) and iron (Fe) from spent lithium iron phosphate (LiFePO₄ or LFP) battery cathodes through selective leaching and precipitation. A factorial experimental design was employed to model the leaching process and to optimize the leaching parameters and the best efficiency was achieved at Acid Conc. (1.5 N), Agitation (350 rpm), and solid-to-liquid ratio (15%) and achieved Li (80%), Al (0.28%), and Fe (76%) recovery. The intensified conditions (H₂SO₄ 3 N, 350 rpm, 20% solid-to-liquid ratio) outside the factorial domain for potential interest in scale-up were also applied to achieve 90.8% Li and 98.0% Fe co-extraction at room temperature within only 30 min, in contrast to the elevated temperatures (60–95 °C) typically required in the literature. To enhance Fe–Li separation, various precipitation agents were tested; among these, oxalic acid at the stoichiometric dosage achieved 98.1% Fe removal while limiting Li loss to 2%, making it the most effective purification method. By combining mild, room-temperature leaching with highly selective Fe removal, this approach reduces the energy and reagent requirements associated with conventional thermally assisted LFP recycling routes. Overall, this work provides an effective approach for recovering valuable components from spent LIBs, reducing material losses and contributing to the development of circular resource recovery strategies. Full article

Ports are progressively evolving from traditional logistics nodes into integrated energy ecosystems, characterised by increasing electrification of maritime and land-based operations, the deployment of renewable energy sources, and the emergence of new and highly variable energy demand profiles. In this context, the main challenge is not only the availability of renewable energy but also the capacity of port energy systems to provide sufficient electrical power, flexibility, and resilience under increasing operational constraints. These issues are particularly relevant in Mediterranean ports, where limited grid capacity, infrastructure constraints, load variability, and interactions with surrounding urban areas strongly influence energy planning strategies. This paper proposes an integrated framework for the development of sustainable port energy hubs based on renewable generation, energy storage, green hydrogen systems, port microgrids, and intelligent energy management strategies (EMS). The main novelty lies in the integration of these energy vectors within a unified framework that explicitly accounts for the specific operational and infrastructure constraints of Mediterranean ports. The proposed approach aims to optimise the interaction between energy production, distribution, storage, and consumption, with particular attention to the role of hydrogen as a long-duration energy storage vector and as an energy carrier for selected port logistics applications. Through a data-driven Port Energy Baseline Assessment (PEBA), port operational characteristics are translated into quantified energy demand and power requirements, providing the basis for power adequacy assessment and the evaluation of alternative transition pathways. An illustrative application to a representative Mediterranean port, characterized by a peak electricity demand of 42 MW, illustrates how the framework quantifies power requirements, assesses power adequacy under infrastructure constraints, and compares alternative transition pathways based on renewable generation, battery storage, and hydrogen. Full article

Zn₂SnO₄ is a promising anode for lithium-ion batteries owing to its high theoretical capacity, yet its practical utilization is severely limited by sluggish reaction kinetics, large volume expansion, and unstable electrode/electrolyte interfaces. Here, we introduce a dimensionality-reduction strategy that simultaneously boosts capacity and cycling stability. Through surfactant-directed crystal growth, acid-etching reconstruction, and hydrothermal carbon coating, compact Zn₂SnO₄ octahedra are controllably transformed into sheet-assembled structures and finally into a core–shell composite with a continuous carbon layer (C@M-Zn₂SnO₄ (H⁺)). The continuous structural evolution shortens Li⁺ diffusion paths, buffers mechanical stress, and stabilizes the solid–electrolyte interface without altering the intrinsic lithium-storage mechanism of Zn₂SnO₄. As a result, the optimized C@M-Zn₂SnO₄ (H⁺) electrode delivers a reversible capacity of 650 mAh g⁻¹ after activation and retains 620 mAh g⁻¹ after 600 cycles at 200 mA g⁻¹, with Coulombic efficiency approaching 100% throughout. This work demonstrates that dimensionality-reduction-assisted structural engineering is an effective strategy for developing high-capacity, long-cycle-life anode materials. Full article

Accurate state of charge (SoC) estimation is of primary importance in terms of safe and efficient management of energy storage systems (ESSs). In this regard, data-driven frameworks offer the advantage of rapid execution during online operations. Nevertheless, their deployment on resource-constrained embedded systems is often hindered by the strict memory and processing limitations of low-cost hardware. This article proposes a three-stage pipelined SoC estimation framework incorporating a reinforcement learning (RL) primary stage, least squares boosting (LSB) secondary residual corrector, and ultimate linear three-point interpolation (3p-InT) stage. The RL phase utilizes a twin deep delayed deterministic policy gradient neural network (TD3NN)-based agent along with a customized reward function. The inference part of the framework is deployed on two separate embedded platforms, i.e., an STM32F411RE microcontroller (MCU) and Digilent Nexys A7-100T FPGA through automatic C code and hardware description language (HDL) code generation features in MATLAB/Simulink, respectively. The efficacy of the proposed framework is evaluated using a Panasonic 18650 lithium-ion battery (LiB) and a battery-powered drill load profile (BPD-LP). Across the four hardware scenarios, the accuracy of the proposed framework is preserved, with the maximum %RMSE deviation not exceeding 0.08 percentage points. The RMSE value remains within 1.80–1.82% for the LiB dataset and within 0.76–0.84% for the BPD-LP, irrespective of the platform or the arithmetic format. As for the resource footprint, the fixed-point implementation more than halves the FPGA logic with respect to the floating point (27.33% against 65.55% of the LUTs), at the cost of a comparatively higher DSP usage (15% against 7.08%). On the MCU, it trades additional flash memory (31.51% against 25.23%) for a 2.6-fold smaller RAM footprint. The framework’s reward function, hyperparameters, and architecture are kept unchanged across both datasets, indicating that the same configuration can be generalized across both profiles without requiring dataset-specific re-tuning. Moreover, the detailed hardware deployment findings provide a practical insight into the hardware and arithmetic format selection for an accurate embedded SoC estimation framework. Full article

Battery-management systems observe current, terminal voltage, limited temperature measurements, and operating setpoints, but not the internal variables of electrochemical theory, so routine signals generally cannot identify a unique mechanism. We present a human-guided, generative-AI-assisted methodology that translates physics-based expectations into deterministic, auditable screening rules: human scientific authority fixes the physical assumptions, evidence requirements, and permissible claims, artificial intelligence supports development, and runtime evaluation is non-generative. LiFePO₄ is the test case. A Zeng–Bazant current-dependent plateau approximation supplies a physics-based reference, and a bivariate representational precedent motivates a composite, reference-dependent voltage residual that is not identified as thermodynamic work. Eleven observable screens return present, absent within resolution, indeterminate, or unavailable. Four evidence forms are separated. Digitized model curves show the reduced plateau relation tracks its parent phase-field simulation through moderate rates, with a high-rate limitation. Published temperature-conditioned discharge profiles show stable plateau elevation and flattening from 268 to 298 K across 0.5C–2C, with mixed 2C curvature. Measured replicates of a commercial cylindrical cell at two ambient setpoints resolve a within-run surface-temperature depression whose integrated first-law balance is heat-rejection-dominant and compatible with, but not uniquely attributed to, a literature-bounded reversible contribution. Controlled synthetic cases verify deterministic feature recovery and abstention without establishing a mechanism. Full article

Impulsivity is a common trait and is understood as a symptom of various disorders such as Attention-Deficit/Hyperactivity Disorder (ADHD). We previously proposed ImpulsivityBank protocol, which is a standardized discourse protocol for retrieving speech samples that enables the identification of the speech features related to the impulsivity trait in children and adolescents. ImpulsivityBank protocol presents three elicitation methods (recall task, storyboard, picture description) and quantifies general language abilities using a battery of linguistic assessments. In this paper, we analyze the current data from the Impulsivity corpus, which is a corpus that consists of speech samples collected using our protocol. We performed an automated linguistic-feature analysis of the Impulsivity corpus to examine speech features related to the impulsivity trait in children and adolescents. We present the results of both the classification and prediction systems considering diverse experimental scenarios. Our results indicate that the speech features from the storyboard consistently yielded the best-performing classification and prediction systems among the evaluated elicitation methods. We conclude that ImpulsivityBank protocol facilitates the collection of speech samples from which a range of linguistic features can be extracted and explored in relation to the impulsivity trait in children and adolescents. The consistency observed across the classification and prediction models suggests that multiple speech features jointly contributed to the fitted models’ outputs. Our main contribution lies in speech analysis, particularly in the extraction and study of speech features that may be associated with impulsivity. Full article

As critical minerals required for the production of new energy batteries, the demand forecasts and trends for Li, Co, Ni, and Mn are of crucial importance for ensuring the energy transition and China’s sustainable development. This study has developed a comprehensive analytical framework, combining dynamic material flow analysis, stock-driven forecasting, and scenario analysis to conduct a life-cycle assessment of the critical mineral resources (2010–2060). (1) Retrospective estimates show that in 2024, demand for Li, Co, Ni, and Mn reached 117 kt, 79 kt, 78 kt, and 45 kt, respectively. By 2060, the maximum demand for these minerals is projected to rise to 651 kt, 1090 kt, 1080 kt, and 757 kt, respectively. (2) Li is the mineral with the highest demand. Under different recovery scenarios, the lithium substitution rates reach 55.4%, 68.3%, and 85.3%, respectively. Given that the demand in cascade utilisation is far lower than the volume of retired new energy vehicle batteries, a cascade utilisation rate exceeding 23% would theoretically suffice to meet the entire relevant market demand. To secure the supply of critical minerals for energy batteries, this study suggests accelerating the construction of standardised recycling systems and implementing strict entry approvals to prevent overcapacity. Full article

Lithium–sulfur (Li–S) batteries are promising next-generation energy-storage systems but are severely hindered by polysulfide shuttling, sluggish sulfur redox kinetics, and unstable Li₂S nucleation/growth behavior. Herein, a multifunctional MXene/carbon nanotube/poly(ethylene oxide) (MX/CNT/PEO)-modified separator is developed to regulate polysulfide behavior and improve interfacial electrochemical stability. In this composite architecture, MXene provides polar sites for lithium polysulfide adsorption, while carbon nanotubes construct interconnected conductive networks and suppress MXene restacking. The incorporation of poly(ethylene oxide) improves interfacial continuity and introduces additional oxygen-containing functionalities within the composite framework. Benefiting from the integrated effects of polar adsorption, conductive pathways, and structural integration, the MX/CNT/PEO-modified separator effectively suppresses polysulfide diffusion, reduces charge-transfer resistance, and promotes more favorable Li₂S nucleation/growth behavior. Electrochemical analysis shows that the charge-transfer resistance decreases from 175.9 Ω for pristine PP to 15.7 Ω for the MX/CNT/PEO@PP separator, corresponding to a 91.1% reduction. Potentiostatic Li₂S deposition analysis further supports favorable Li₂S nucleation/growth behavior on the MX/CNT/PEO-modified interface, after background subtraction. As a result, the Li–S cell with the MX/CNT/PEO@PP separator delivers a high initial discharge capacity of 1613 mAh g⁻¹ at 0.1 C and maintains average capacities of 1331.1 and 803.1 mAh g⁻¹ at 0.1 and 2 C during rate testing, respectively. During long-term cycling at 2 C, the cell retains 319.3 mAh g⁻¹ after 1000 cycles, with an average capacity decay rate of 0.064% per cycle. This work provides a rational composite-interlayer design strategy for multifunctional separator materials in high-performance Li–S batteries. Full article

Layered cathodes (LiNi_xCo_yMn_zO₂, NCM) have emerged as critical materials for batteries and energy storage fields by virtue of their high energy density. However, NCM materials undergo rapid performance degradation and severe capacity fading under harsh conditions of long-term cycling and high voltage. Currently, research regarding spent NCM materials mainly concentrates on failure analysis and modification processes at the macroscopic scale. Nevertheless, the failure mechanisms of NCM, the intrinsic processes during repair and modification, and the fundamental origins of performance improvement are generally embedded in structural evolution at the nanoscale or even atomic scale. This review first discusses the failure mechanisms of NCM. Particularly, the main content focuses on lattice distortion and layered structural instability at the lattice level, migration of nanoscale species together with performance degradation induced by side reactions at the interface level, and generation of nanocracks at the particle level. Moreover, this paper reviews the characterization methods applied at the nanometer scale, and two modification strategies are summarized, namely nanoscale coating and elemental doping. It is expected to provide theoretical references and technical insights for constructing efficient and controllable targeted modification strategies of layered NCM cathodes and developing high-performance ternary cathode materials. Full article

Wireless sensing inside rotating industrial machines is challenging due to signal attenuation, multipath propagation, and continuous sensor motion. Smart particles equipped with wireless communication and inertial sensors provide a promising approach for monitoring such systems. However, the reliability of wireless signal transmission under rotational dynamics remains insufficiently understood, and systematic approaches for sensor selection are lacking. This paper presents a link-budget-normalised experimental characterisation of three commercial smart particles, namely MetaMotionS (BLE), WitMotion BLE, and WitMotion Wi-Fi, in a bare 300 mm diameter by 310 mm deep metallic drum fitted with six triangular lifters, at rest and at 16, 18, and 20 RPM, corresponding to 20.7–25.9% of the critical speed and Froude numbers of 0.043–0.067. Because raw received power conflates transmit power with channel behaviour, the comparison is expressed as excess path loss above free space together with second-order fading statistics. Two particles of the same protocol class differ by 23.9 dB, of which at most 4 dB is attributable to the transmission of power across the documented range of both radios, establishing that device implementation rather than protocol class governs the ranking. Two particles logged simultaneously through a single receiver observe one channel realisation, and the correlation between their signal fluctuations is not significantly different from zero at any speed (r = +0.064, −0.098, −0.083), indicating device-specific rather than environmental fading. Under rotation, the Wi-Fi particle holds an RSSI standard deviation of 1.96 dB against 6.27 and 6.58 dB for the two BLE particles (Welch ANOVA, p < 0.001; Games–Howell post hoc, all pairwise comparisons p < 0.001; |Cliff’s δ| > 0.96). A bounded, dimensionless multi-criteria selection procedure over link margin, signal variability, cross-speed consistency, packet delivery, and energy per delivered packet is introduced; the ranking is invariant under weighted-sum and TOPSIS aggregation but inverts once endurance carries a weight above 0.35, which quantifies the trade-off between link quality and battery life. Coupling between the wireless and motion streams is examined by folding both onto rotation phase. A rotation-locked component in RSSI is detected in one of nine sensor–speed combinations, with a maximum modulation amplitude of 0.92 dB. The RSSI logging cadence of approximately 1 Hz resolves the drum fundamental but lies below the Nyquist requirement for the dominant motion band at 0.91–1.04 Hz, so joint wireless–motion studies of rotating machinery require RSSI logging at 5 Hz or above on a clock shared with the inertial unit. Full article

Gel polymer electrolytes (GPEs) have emerged as promising electrolytes for lithium metal batteries owing to their high ionic conductivity, mechanical flexibility, and reduced risk of electrolyte leakage. However, PAN-based GPEs still suffer from limited ion transport caused by the semi-crystalline structure of PAN chains. In this work, polyacrylonitrile (PAN)/poly(methyl methacrylate) (PMMA)/lithium aluminum titanium phosphate (LATP)/lithium bis(trifluoromethanesulfonyl)imide (LiTFSI) gel polymer electrolytes were fabricated via direct ink writing (DIW) 3D printing, where PMMA was introduced to regulate the PAN matrix and enhance Li⁺ transport. The results reveal that PMMA incorporation effectively reduces PAN crystallinity, increases the amorphous fraction, and modifies the local functional-group environment of the polymer matrix, while LATP fillers further improve ionic transport and mechanical stability. The optimized PPM8:2 gel polymer electrolyte delivers a room-temperature ionic conductivity of 4.22 × 10⁻⁴ S cm⁻¹, a Li⁺ transference number of 0.624, and an electrochemical stability window of 4.75 V. When applied in LiFePO₄|Li batteries, it maintains a discharge capacity of approximately 150 mAh g⁻¹ after 100 cycles at 0.1 C with excellent rate capability and cycling stability. This work provides an effective approach to developing PAN-based gel polymer electrolytes for high-performance lithium metal batteries. Full article

In electric vehicles (EVs), the battery management system (BMS) plays a central role in ensuring safe, efficient, and reliable battery operation under varying driving and environmental conditions. The effectiveness of a BMS largely depends on the availability of an accurate battery model, whose performance is strongly influenced by the precision of its identified parameters. However, estimating these parameters remains a difficult nonlinear optimization problem, especially under low state of charge (SOC) operation. Classical identification approaches often have limited robustness under such conditions, while metaheuristic algorithms provide a promising alternative because of their ability to handle nonlinear and multimodal search spaces. Even so, many existing methods still encounter drawbacks related to convergence speed and susceptibility to local optima. Motivated by these challenges, this study investigates the recently introduced Artificial Lemming Algorithm (ALA) for parameter identification of a second-order equivalent circuit model (2RC-ECM) under EV-oriented low-SOC operating conditions. Experimental validation is conducted using two independent dynamic datasets, namely the High Dynamic Profile (HDP) at 25 °C and the Urban Dynamometer Driving Schedule (UDDS) at −5 °C, involving different lithium-ion cells and operating conditions. ALA is benchmarked against nine competing metaheuristic algorithms under identical search boundaries and computational settings. Performance is assessed using RMSE, MAE, MaxAE, bias, convergence behavior, error distributions, execution time, and sensitivity to the number of independent runs, population size, and maximum number of iterations. The results show that ALA achieves the lowest minimum, mean, and maximum RMSE for both datasets, with minimum RMSE values of $0.01075 \mathrm{V}$ for HDP and $0.03534 \mathrm{V}$ for UDDS. Unseen-data validation further yields RMSE and MAE values of $0.0082$ and $0.0061 \mathrm{V}$, respectively, for HDP, and $0.0416$ and $0.0299 \mathrm{V}$, respectively, for UDDS. In addition, convergence, error-distribution, and sensitivity analyses show that ALA maintains competitive and consistent performance across the investigated configurations. Overall, the results demonstrate that ALA provides a favorable balance between estimation accuracy, robustness, convergence behavior, and computational cost for offline lithium-ion battery parameter identification. Full article

Accurate state-of-charge (SOC) estimation of lithium iron phosphate (LiFePO₄) batteries is challenging because the voltage feedback used for correction does not provide constant SOC-related information under different operating conditions. Conventional extended Kalman filters (EKFs) usually apply measurement correction based on predefined statistical assumptions, while overlooking variations in voltage-domain observability and innovation reliability. This paper proposes a hybrid estimation framework, termed observability- and innovation–reliability-regulated EKF with reliability-scaled residual learning (OIR-EKF-RSRL). The proposed method retains a first-order RC model and EKF as the physical estimation backbone, while regulating voltage correction according to local OCV-SOC sensitivity and normalized innovation reliability. A reliability-scaled residual learning module is further introduced after physical filtering to compensate for remaining SOC deviations rather than directly predicting SOC. The learned residual correction is modulated by a reliability-dependent scaling coefficient before fusion with the OIR-EKF estimate, after which the final SOC estimate is constrained to the physical interval [0, 1]. The framework is evaluated using the CALCE A123 LiFePO₄ dataset under a frozen temperature-disjoint train–validation–holdout protocol. On the independent holdout set, OIR-EKF-RSRL reduces the RMSE from 3.331 percentage points for the conventional EKF to 2.942 percentage points. The results demonstrate that reliability-aware measurement utilization and reliability-scaled residual compensation provide an interpretable solution for LiFePO₄ SOC estimation under varying voltage-information quality. Full article

The efficient operation of lithium-ion battery management systems (BMSs) depends on accurate state-of-charge (SOC) estimation. However, the performance of conventional model-based SOC estimation methods may progressively worsen owing to parameter uncertainty and nonlinear battery dynamics. This study proposes a hybrid SOC estimation framework termed DO-EKF_Res, comprising two sequential stages. In the first stage, the process and measurement-noise covariance matrices are optimized offline using a data-driven strategy. In the second stage, a Bidirectional Long Short-Term Memory (BiLSTM) residual learning network is employed to compensate for the remaining SOC estimation errors. The proposed framework was evaluated using two complementary validation protocols: a synthetic Monte Carlo experiment and a Leave-One-Battery-Out (LOBO) cross-validation framework based on the NASA Prognostics Center of Excellence (PCoE) lithium-ion battery dataset. In the synthetic validation, DO-EKF_Res achieved an RMSE of 0.803%, corresponding to reductions of 48.83% and 26.84% relative to the EKF and DO-EKF, respectively. In the NASA LOBO evaluation, the proposed framework achieved a macro-averaged RMSE of 11.534%, corresponding to reductions of 49.25% and 9.68% relative to the EKF and DO-EKF, respectively. These results demonstrate that integrating offline covariance optimization with BiLSTM-based residual learning improves estimation accuracy, robustness, and cross-battery generalization, providing a practical solution for lithium-ion battery SOC estimation in battery management systems. Full article

Accurate identification of equivalent circuit model parameters of lithium-ion cells is essential for reliable prediction of voltage fluctuation dynamics in traction and stationary energy storage applications. This paper presents a novel parameter identification approach for a Thévenin equivalent circuit-based model (ECM) of an LFP (LiFePO₄) battery cell using results of the Hybrid Pulse Power Characterization (HPPC) test. A major limitation of the classical HPPC-based method arises from the mismatch between the short duration of test current pulses and the long time constants characteristic of LFP cells, which significantly reduces identification accuracy. While increasing pulse duration could improve identification, it tends to affect the cell’s state of charge and thermal equilibrium. To overcome this problem, a two-component identification method is proposed that simultaneously exploits the voltage response during the current pulse and the voltage relaxation transient, recorded after pulse termination. Parameter identification is performed using a particle swarm optimization (PSO) algorithm for equivalent circuits with two and three RC pairs. The proposed approach is experimentally validated using a Winston Thundersky LFP040AHA cell. The obtained cell equivalent circuits are verified under a Charge-Depleting Cycle (CDC) test by comparing measured and simulated voltage responses. The proposed two-component approach provides an effective and practical solution for high-fidelity modeling of LFP cells, supporting model-based design, simulation of battery-powered systems, and testing and validation of BMS. Full article