Search Results (9,926)

This review synthesizes recent progress in modern energy storage technologies and proposes a selection-oriented comparison for power-system stabilization. Technologies are grouped into electrochemical, mechanical, chemical, and thermal storage, and evaluated using harmonized criteria (power and energy capability, response time, round-trip efficiency, lifetime, cost proxies, and maturity level). A comparative dataset and use-case mapping are used to link technology characteristics to grid services, with emphasis on voltage support, operational durability, and waste-heat utilization. The analysis highlights pumped-storage hydropower as the most robust option for long-duration, high-capacity applications, while battery energy storage systems are best suited for fast ancillary services, provided that cycle life, safety, and system integration constraints are met. Finally, the review discusses current technology trends (e.g., LFP and sodium-ion deployment, solid-state development, and commercialization barriers for lithium-sulfur) and identifies evidence-based directions for future research and deployment. Full article

With the continuous rise in the energy density of power batteries, battery heat generation has become an increasingly severe issue. Particularly under extreme conditions combining high summer temperatures and high discharge rates, battery thermal safety is facing tremendous challenges. To address this problem, this study proposes a honeycomb liquid cooling–PCM hybrid battery thermal management system (BTMS) based on fin–casing synergistic heat transfer enhancement. We analyzed the effects of the longitudinal fins and thermal conductive casing on the thermal characteristics of the system, further investigated the influence patterns of key factors including fin number, battery spacing and contact thermal resistance on the thermal performance of the honeycomb BTMS, and clarified the action mechanisms of each structure and parameter on battery temperature rise and temperature uniformity. The results show that the fin structure enhances longitudinal heat conduction, improves liquid cooling efficiency, and effectively reduces the maximum battery temperature, while the thermal conductive casing significantly improves battery temperature uniformity. The BTMS with fin–casing synergistic heat transfer enhancement can control the maximum battery temperature and temperature difference within 60 °C and 5 °C, respectively, under extreme operating conditions. Full article

Wind power generation exhibits pronounced volatility and intermittency, and direct grid connection may cause instability in grid frequency. To address this issue, this paper proposes an optimisation strategy for hybrid energy storage systems to mitigate wind power fluctuations, integrating lithium-ion batteries with supercapacitors within wind power systems. Firstly, the grid-connected power of wind turbines and the reference power of the energy storage system are determined through dynamic weight adjustment using a weighted filtering algorithm combining adaptive exponential smoothing and recursive averaging algorithms. Secondly, the fish-eagle optimisation algorithm is employed to refine variational modal decomposition parameters. The modal components derived from decomposing the energy storage system’s reference power are converted into Hilbert marginal spectra. Following determination of the cut-off frequency, high-frequency signal components are managed by supercapacitors, while low-frequency components are handled by lithium-ion batteries. Finally, an optimised configuration model for the hybrid energy storage system is constructed to minimise the annual lifecycle target cost. Case study analysis demonstrates that this approach effectively smooths fluctuations in wind power output while fully leveraging the complementary characteristics of both energy storage types, achieving a balance between system economics and overall performance. Full article

The state of health (SOH) estimation of lithium-ion batteries faces significant challenges under complex operating conditions due to transient disturbances and distribution shifts. This paper proposes a deep learning framework named Conformer-KAN, which integrates a convolution-augmented Transformer (Conformer) with a Kolmogorov–Arnold Network (KAN). The method first constructs a unified input representation by fusing multi-view features including voltage, current, temperature, and incremental capacity. It then employs a Conformer encoder that combines gated local convolution units (GLCU) and multi-head self-attention (MHSA) to achieve joint modeling of local and global features. In addition, learnable spline-based activation functions are introduced within the KAN structure to enhance the model’s capacity for capturing complex nonlinear degradation behaviors. Cross-battery and cross-condition evaluations conducted on two public datasets demonstrate that the proposed method achieves root mean square errors (RMSE) of 0.006 ± 0.001 and 0.003 ± 0.001, and coefficients of determination (R²) of 0.987 ± 0.003 and 0.994 ± 0.002, respectively. These results show that Conformer-KAN significantly outperforms existing mainstream approaches in both robustness and generalization performance. Full article

Electric Vehicles (EVs) can contribute significantly to reducing greenhouse gas emissions and addressing climate change problems. Modern EVs are primarily powered by electrochemical batteries such as lead-acid (Pb-acid), nickel-metal hydride (NiMH), sodium-ion (Na-ion), solid-state and lithium-ion (Li-ion) batteries. When compared to other battery types, Li-ion batteries are the most suitable for EV applications due to their practical features such as their high energy density, high charging and discharging efficiency and extended lifetime. However, the main risk of Li-ion batteries is that they are exposed to thermal runaway phenomena, which raises severe concerns about the safety of EV propulsion systems. Thermal runaways should be considered carefully as they cannot be stopped once they start and can lead to battery explosion. One of the main reasons leading to this phenomenon is abusing the state of charge (SoC) of the battery. Therefore, the battery management system (BMS) plays a crucial role in mitigating the stimulation of the thermal runaway process by accurately estimating and properly managing the battery cells. To help researchers and designers with understanding this matter, this paper proposes a review of the most effective SoC estimation methods for EV Li-ion batteries and links these methods with practical energy management systems in the EV market. Full article

Background/Objectives: An extensive body of data suggests that inflammation may contribute to the pathophysiological mechanisms of psychiatric illness. Circumstantial evidence implied that low-dose aspirin (LDA) may enhance the therapeutic efficacy of psychotropic drugs. We examined whether LDA administration with psychotropic medications is associated with medication regimen stability and other therapeutic effects in patients with bipolar disorder (BD), schizophrenia, and schizoaffective disorder (SAD). Methods: This retrospective study analyzed data from Clalit Health Services’ Southern District database in Israel, including 1924 patients treated between 2017 and 2019. The Study Group consisted of patients treated with LDA plus psychotropic medications, whereas the Control Group included patients treated only with psychotropic medications. Study outcomes included suicide attempts and pharmacotherapy-related negative events, defined as psychotropic dose escalation, augmentation, or switching. Results: The study group included 137 patients (55% males, age 63.3 ± 12.3 years), and the control group included 1787 patients (60% males, age 47 ± 16.9 years). Significant differences were observed across nearly all outcomes, favoring the LDA co-treatment group. Patients in the study group exhibited lower rates of medication dosage increase (40 [29%] vs. 726 [40.5%], p = 0.01); fewer changes and/or additions of psychotropic medications (37 [26.9%] vs. 778 [43.5%], p < 0.001); and a non-significantly lower rate of suicide attempts (0 [0%] vs. 16 [0.9%], p = 0.53). Conclusions: Overall, LDA co-treatment was associated with better clinical outcomes among patients with BD, schizophrenia, and SAD. Follow-up large-scale epidemiological studies and prospective randomized clinical trials are needed to examine the therapeutic potential of add-on LDA to psychotropic medications. Full article

Traditional lithium-ion battery recycling relies mainly on pyrolysis or chemical leaching to separate current collectors from electrode materials, inevitably resulting in secondary pollution. In contrast, eddy current separation (ECS) applied to crushed lithium-ion battery residues can substantially reduce the introduction of contaminants while minimizing material losses. However, the heterogeneous composition and diverse surface characteristics of crushed battery products, together with the conductivity degradation of electrode materials after long-term use, make conventional empirical particle–trajectory correlations inadequate for accurate optimization of ECS operating parameters. In addition, the coupling between process parameters and the resultant forces acting on conductive particles, as well as the associated separation trajectories, remain insufficiently understood, which severely limits process controllability. A force–trajectory model was therefore developed for spent current collectors and conductivity-degraded LiFePO₄ to describe their particle dynamics in an alternating magnetic field. The results demonstrate that the trajectory of LiFePO₄ is very similar to that of non-conductive materials, thereby facilitating its effective separation from metallic components in battery scrap. Eddy current separation experiments further confirm the accuracy of the model predictions with respect to separation trajectories and the influence of key process parameters. On this basis, optimization of the operating parameters increased the separation efficiency of the cathode material to above 95.1%. The clarified ECS mechanism for current collectors and electrode materials provides new insights into the mechanical pre-sorting and mechanistic understanding of lithium-ion battery fragments, thereby contributing to reductions in contaminant introduction during battery material recycling. Full article

Recent decades have seen substantial growth in the demand for lithium-ion batteries (LIBs); as a result, the number of gigafactories is increasing. The power systems of these gigafactories are the most important parts of these installations, as they are directly responsible for energy efficiency, operational cost, and sustainable growth of the LIB industry. This necessitates the need for comprehensive studies of power consumption in a gigafactory during the LIB manufacturing process. This paper presents a detailed review of the state-of-the-art of different parts and components of power systems in gigafactories, and power consumption estimation during cell production. This research analyzes the existing components of a power system, including power sources, different power distribution mechanisms, various power equipment, thermal management strategies, failure analysis methods, and several technologies for regenerative functions. The analysis of the above-mentioned components, systems, and technologies will enable us to understand the cumulative power consumption profile of the gigafactory, including the power consumption at each step of the production process, for normal non-production operations, like powering and lighting the facility, and for the complex and highly sophisticated power distribution system. The outcomes of this research paper highlight the importance of an optimized power system for the gigafactory, with maximum possible efficiency and minimal power losses during transmission, distribution, operational stages, and the cell formation process. This paper also helps to understand the shortcomings in existing systems and technologies, suggests improvements, and provides targets for future research directions. Full article

Although tin disulfide (SnS₂) possesses a theoretical specific capacity (645 mAh g⁻¹) significantly superior to that of commercial graphite, along with the merits of Earth abundance and cost-effectiveness, its commercial application as an anode material for lithium-ion batteries (LIBs) is severely hindered by substantial volume expansion during cycling. Herein, N-doped SnS₂ composites featuring a stacked hexagonal nanosheet architecture were synthesized via a facile one-step hydrothermal strategy. The incorporation of nitrogen significantly bolsters the long-term cycling stability of the electrode during charge/discharge processes. Electrochemical tests results reveal that the composite delivers an initial specific capacity of 500.8 mAh g⁻¹ at a current density of 0.5 A g⁻¹. Following 10 stabilization cycles, the capacity is recorded at 394.9 mAh g⁻¹, and notably, it increases to 481.66 mAh g⁻¹ after 500 cycles, corresponding to a high capacity retention of 96.17%. This superior performance is attributed to the introduced nitrogen, which provides abundant active sites and facilitates the formation of a robust solid electrolyte interphase (SEI) film. Furthermore, density functional theory (DFT) calculations demonstrate that N-doping narrows the band gap of SnS₂, thereby improving electrical conductivity and electron transport efficiency. Full article

High-speed solid particles ejected during battery thermal runaway pose severe safety threats, yet their velocity measurement is hindered by high density, microscopic size, and intense glare. This study proposes a non-intrusive velocimetry framework that integrates an enhanced single-stage object detector with a structural similarity matching algorithm. The detector incorporates specialized feature extraction modules and a high-resolution layer to identify microscopic targets in extreme environments, while the matching algorithm employs adaptive direction constraints to ensure precise trajectory tracking. Experimental validation demonstrates that the framework achieves a mean average precision of 92.7% and supports real-time processing. The method successfully quantifies a three-stage velocity evolution in battery failure events, identifying a peak particle speed exceeding 120 m/s. These findings provide critical kinematic data for optimizing battery safety structures and modeling fire propagation mechanisms. Full article

Biohydrometallurgical processing of spent lithium-ion batteries offers a low-impact route for critical metal recovery compared with conventional hydrometallurgy. In this work, the iron-oxidizing bacterium Acidithiobacillus ferrooxidans was evaluated for the bioleaching of cobalt (Co), nickel (Ni), lithium (Li) and copper (Cu) from pyrolyzed industrial black mass derived primarily from LiCoO₂-based batteries, containing both LiCoO₂ and LiNiO₂ layered oxide phases. Batch experiments were conducted in 9K medium at 30 °C, varying pulp density (1%–2%, w/v), inoculum volume (10–20 mL in 200 mL medium) and initial pH (with and without adjustment). At 1% pulp density and 10% v/v inoculum, metal recoveries after 6–7 days reached about 64%–70% Co, 57%–72% Ni, 52%–60% Li and 81%–100% Cu, with most dissolution occurring in the first 6 days. Higher inoculum loads without initial pH adjustment increased Li recovery up to 79%, but did not further improve Co and Cu, indicating a trade-off between microbial activity, metal toxicity and ferric iron availability. The temporal evolution of pH and metal dissolution is consistent with indirect redoxolysis by biogenic Fe³⁺ and sulfuric acid generated during ferrous iron and elemental sulfur oxidation. Overall, the results confirm the feasibility of A. ferrooxidans-assisted bioleaching as a green option for Co, Ni, Li and Cu recovery from spent LiCoO₂ batteries and provide operating windows for subsequent process optimization and scale-up. Full article

The future growth of alkali metal-based batteries requires an understanding of how ion size affects the exchange mechanisms. In this work, we present a direct, comparative electrochemical study of MXene-based electrodes mechanism vs. lithium (Li⁺), sodium (Na⁺), and potassium (K⁺) ions using the same electrochemical conditions. This controlled method enables an extensive investigation of the size-dependent interactions between the MXene structure and alkali metal ions. X-ray photoelectron spectroscopy and Raman analysis of TMAOH-treated Ti₃C₂T_x MXene electrodes show that delamination and cycling alter vibrational modes and the surface chemistry. Voltage profile study reveals diverse storage behaviors: Li⁺ has a prominent intercalation plateau, Na⁺ shows intermediate properties, and K⁺ displays sloping profiles, indicating surface-dominated adsorption. The significant correlation between ionic radius and electrochemical reversibility is shown by long-term cycling data over 300 cycles, which show greater capacity retention and stability for Li⁺ and progressively lower performance for Na⁺ and K⁺. These findings provide new mechanistic insights into MXene–ion interactions and build the foundation for developing MXene-based materials for specific alkali-ion chemistries in next-generation energy storage devices. Full article

The rapid increase in end-of-life lithium-ion batteries demands sustainable recycling routes for lithium recovery. This work presents a novel integrated hydrometallurgical–electrodialysis process designed specifically for recovering lithium from off-specification NMC cathode materials while enabling full reagent recyclability. Selective leaching with oxalic acid was optimised by setting the water-to-oxalic acid dihydrate ratio (H₂O/OA·2H₂O) to 7.3:1 w/w, achieving 81% lithium extraction at room temperature within 2 h while limiting the co-dissolution of Ni, Co and Mn to 0.2%, 1.6% and 1.7% by weight, respectively. The resulting leachate was processed in a four-chamber electrodialysis cell equipped with two Nafion 117 cation-exchange membranes and one Neosepta AMX-fmg anion-exchange membrane operating at −1.6 V versus Ag/AgCl, enabling 96% lithium recovery and 98% oxalic acid recovery. The regenerated oxalic acid stream (41.8 g L⁻¹) was fully restored to its initial concentration and reused in successive cycles without performance loss. Subsequent precipitation of lithium with Na₂CO₃ yielded 99.3%-pure Li₂CO₃. This combined leaching–electrodialysis–precipitation presents a high selectivity, low-waste, circular recovery system, offering a scientifically original approach that integrates reagent regeneration with high-purity lithium production. Full article

The rapid expansion of lithium-ion battery (LIB) applications and the imminent surge in end-of-life batteries have intensified the demand for efficient, scalable recycling technologies. Physical separation of cathode and anode materials is a crucial pretreatment step that enables high-value metal recovery and direct material regeneration. This review critically examines recent advances in three major physical separation technologies—magnetic separation, gravity separation, and flotation—for processing spent LIB electrodes. Rather than offering a descriptive summary, the review systematically analyzes separation mechanisms, key controlling parameters, and pretreatment strategies across representative cathode chemistries, including LiFePO₄ (LFP), LiCoO₂ (LCO), and Ni–Co–Mn (NCM) systems. Particular emphasis is placed on emerging flotation-enhancement strategies, such as nanobubble-assisted and ultrasonic-enhanced flotation, and their underlying mechanistic roles in improving selectivity and recovery. Comparative evaluation indicates that magnetic separation has reached industrial maturity for LFP–graphite systems but remains chemistry-specific. Gravity separation is effective for coarse particles and centrifugal-assisted graphite recovery yet shows limited selectivity for fine particles. Flotation has become the dominant research focus for complex, fine-particle separations due to its tunable surface chemistry. Despite significant laboratory progress, challenges remain, including incomplete binder removal, limited understanding of electrode surface reconstruction during pretreatment, fine-particle entrainment, and the gap between bench-scale research and industrial implementation. Future research priorities include green reagent development, intelligent separation control, and integration with direct regeneration routes to advance closed-loop LIB recycling towards sustainable development. Full article

This paper presents the design and experimental validation of a hardware-enforced charging profile selection framework for low-voltage electric all-terrain vehicles (ATVs), implemented on a programmable Delta battery charger operating within a voltage range of 0–120 V and a current range of 0–30 A. Unlike conventional programmable chargers that rely primarily on software-defined configuration or battery management system (BMS)-negotiated parameter setting, the proposed system enforces predefined constant-current–constant-voltage (CC–CV) charging profiles at the hardware execution layer. Vehicle identification is performed using CANopen-based identifiers, while relay-based selection, controlled via Modbus RTU, physically routes the charger output to fixed CC–CV control paths, thereby structurally reducing the risk of misconfiguration and unintended parameter changes. The system integrates layered control using embedded ESP32 nodes, a redPLC supervisory controller, and NodeRED-based orchestration, combined with real-time measurement, logging, and visualization using a time-series database and Grafana dashboards. Experimental validation is conducted using lithium-ion battery packs configured at four nominal voltage levels (24 V, 48 V, 60 V, and 72 V). The results confirm correct automatic profile selection, deterministic relay-based routing, and stable CC–CV charging behavior across repeated charging sessions. Rather than proposing a new charging algorithm, this work contributes a safety-by-design execution-layer charging architecture that complements higher-level smart charging and management protocols and is particularly suited for closed, heterogeneous fleet environments where deterministic behavior, robustness against configuration errors, and transparent verification of charging processes are critical. Full article