Search Results (2,090)

State-of-charge (SoC) balancing across multiple battery energy storage systems (BESS) is a central challenge in renewable-rich mini-grids. Heterogeneous battery capacities, differing states of health, stochastic renewable generation, and variable loads create a high-dimensional uncertain control problem. Conventional droop-based SoC balancing strategies are decentralized and computationally light but fundamentally reactive and limited, whereas model predictive control (MPC) is insightful but computationally intensive and prone to modeling errors. This paper proposes a Hierarchical Predictive–Adaptive Control (HPAC) framework for SoC balancing in mini-grids using deep reinforcement learning. The framework consists of two synergistic layers operating on different time scales. A long-horizon Predictive Engine, implemented as a federated Transformer network, provides multi-horizon probabilistic forecasts of net load, enabling multiple mini-grids to collaboratively train a high-capacity model without sharing raw data. A fast-timescale Adaptive Controller, implemented as a Soft Actor-Critic (SAC) agent, uses these forecasts to make real-time charge/discharge decisions for each BESS unit. The forecasts are used both to augment the agent’s state representation and to dynamically shape a multi-objective reward function that balances SoC, economic performance, degradation-aware operation, and voltage stability. The paper formulates SoC balancing as a Markov decision process, details the SAC-based control architecture, and presents a comprehensive evaluation using a MATLAB-(R2025a)-based digital-twin simulation environment. A rigorous benchmarking study compares HPAC against fourteen representative controllers spanning rule-based, MPC, and various DRL paradigms. Sensitivity analysis on reward weight selection and ablation studies isolating the contributions of forecasting and dynamic reward shaping are conducted. Stress-test scenarios, including high-volatility net-load conditions and communication impairments, demonstrate the robustness of the approach. Results show that HPAC achieves near-minimal operating cost with essentially zero SoC variance and the lowest voltage variance among all compared controllers, while maintaining moderate energy throughput that implicitly preserves battery lifetime. Finally, the paper discusses a pathway from simulation to hardware-in-the-loop testing and a cloud-edge deployment architecture for practical, real-time deployment in real-world mini-grids. 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

This study presents a comparative analysis of the performance and modeling differences among lithium titanate oxide (LTO) batteries with three different cathode materials. An evaluation was conducted by performing performance tests over −20 °C to 25 °C at various current rates. Differences in open-circuit voltage curves, as well as charge and discharge capacities under different temperatures and C-rates, were systematically compared. At 25 °C, the NCM cathode enabled superior rate capability, retaining over 90% of its capacity at 8 C discharge, whereas the LCO-based cells exhibited significant capacity fade. Conversely, at −20 °C, the LCO cathode demonstrated better low-temperature performance, delivering almost 80% of its room-temperature capacity at 4 C, compared to less than 5% for the NCM cathode. The batteries were modeled using a second-order equivalent circuit model, and variations in model parameters were analyzed from the perspectives of internal resistance and electrode kinetics. The second-order equivalent circuit model revealed that the NCM-based cells had lower ohmic resistance and faster electrode kinetics. By correlating battery performance with cathode materials, this study evaluates the suitability of LTO batteries with different cathodes for various application scenarios, providing valuable insights for battery application and management. Full article

As a cushion for the volatility of renewable energy, energy storage systems can achieve peak shaving and valley filling, thereby improving the operational efficiency and economic performance of the power grid. In addition, energy storage systems can absorb renewable energy production, thereby enhancing the safety and reliability of the electrical power system. Nowadays, energy storage systems are facing severe problems such as explosions that are caused by overcharging and discharging. The main reason for the overcharging and discharging of energy storage systems is the inconsistency in the state of the electric core in the charging and discharging process, which not only affects the safety of the electric core, but also influences the overall charging and discharging capacity of the energy storage system. To address this inconsistency of energy storage cores, this paper proposes an energy storage consistency monitoring method under the framework of clustering-classification, which adopts the Belief Peaks Evidential Clustering and Evidential K-Nearest Neighbors classification algorithm. This paper proposes a BPEC-EKNN-based method for battery inconsistency detection and localization. The proposed approach first constructs battery performance evaluation coefficients to characterize inter-cell behavioral differences, and then integrates an enhanced k-nearest neighbor strategy to identify abnormal cells. It also identifies and locates inconsistent battery cells by analyzing the magnitude of the confidence level m (Ω), without relying on predefined thresholds. Also, time-series data as opposed to the evaluation of voltage data at a singular point is engaged to realize the detection and localization of energy storage core consistency anomalies under the consideration of time-series data. The proposed algorithm is capable of identifying inconsistencies among energy storage batteries, with the parameter m (Ω) serving as an indicator of the likelihood of inconsistency. Experimental results on battery pack datasets demonstrate that the proposed method achieves higher detection accuracy and robustness compared with representative statistical threshold-based methods and machine learning approaches, and it can more accurately identify inconsistent battery cells. By applying perturbation analysis to real-time operational data, the algorithm proposed in this paper can detect inconsistencies in battery cells reliably. 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

The expansion force generated by lithium-ion batteries during charge–discharge cycles is a key indicator of their structural safety and health. Recently, flexible pressure-sensing technologies have emerged as promising solutions for in situ swelling monitoring, owing to their high flexibility, sensitivity and integration capability. This review provides a systematic summary of progress in this field. Firstly, we discuss the mechanisms of battery swelling and the principles of conventional measurement methods. It then compares their accuracy, dynamic response and environmental adaptability. Subsequently, the main flexible pressure-sensing mechanisms are categorized, including piezoresistive, capacitive, piezoelectric and triboelectric types, and their material designs, structural configurations and sensing behaviors are discussed. Building on this, we examine integration strategies for flexible pressure sensors in battery systems. It covers surface-mounted and embedded approaches at the cell level, as well as array-based and distributed schemes at the module level. A comparative analysis highlights the differences in installation constraints and monitoring capabilities between these approaches. Additionally, this section also summarizes the characteristics of swelling signals and recent advances in data processing techniques, including AI-assisted feature extraction, fault detection and health state correlation. Despite their promise, challenges such as long-term material stability and signal interference remain. Future research is expected to focus on high-performance sensing materials, multimodal sensing fusion and intelligent data processing, with the aim of further advancing the integration of flexible sensing technologies into battery management systems and enhancing early warning and safety protection capabilities. Full article

This study presents a comprehensive thermo-economic evaluation of a pumped thermal energy storage (PTES) system based on a supercritical carbon dioxide (s-CO₂) recompression Brayton cycle (RBC). A multiparametric analysis was conducted through systematic parameterization of key design variables, including mass fractions directed to the recompressor during charging and to the high-pressure turbine during discharging, as well as compressor inlet pressure and temperature and turbine inlet temperature. Performance optimization focused on two main indicators: round-trip efficiency (${\eta }_{RT}$) and levelized cost of storage (LCOS), enabling identification of trade-offs between thermodynamic and economic performance. Results show that minimizing LCOS yields 148.72 $/MWh with an ${\eta }_{RT}$ of 57.1%, whereas maximizing efficiency achieves 61.5% at an LCOS of 158.4 $/MWh. Exergy destruction analysis highlights the strategic role of the main compressor and thermal storage tanks in overall irreversibility distribution. These findings confirm the technical feasibility of the s-CO₂ recompression Brayton cycle as a competitive solution for long-duration thermal energy storage. Full article

This study reports the synthesis of an activated nanoporous carbon material from Phyllanthus emblica (Amala)—a biomass material which is an eco-friendly, economical, and sustainable precursor used to prepare activated carbon using zinc chloride (ZnCl₂) activation at various temperatures (500–700 °C) under a nitrogen gas atmosphere. A sample that was carbonized at 700 °C (AmC_Z700) attained a high specific surface area of 1436 m² g⁻¹ and a total pore volume of 0.962 cm³ g⁻¹, and, when used in an electrode, showed excellent supercapacitance performance, attaining a high specific capacitance of 263 F g⁻¹ at a current density of 1 A g⁻¹, followed by 55% capacitance retention at 50 A g⁻¹. Additionally, the assembled symmetric supercapacitor cell, when operated at 1.2 V, delivered an energy density of 8.9 Wh kg⁻¹ at a power density of 300 W kg⁻¹ and exhibited an excellent cycle life of 95% after 10,000 successive charge/discharge cycles, demonstrating the substantial potential of Phyllanthus emblica seed-derived carbon materials for the creation of high-performance supercapacitors. Full article

Rechargeable zinc–air batteries (ZABs) are promising candidates for sustainable energy storage owing to their high theoretical energy density, safety, and environmental compatibility. However, their practical application is hindered by sluggish oxygen evolution reaction (OER) kinetics and the high charging voltage required, which reduce energy efficiency and accelerate electrode degradation. Here, we report for the first time the beneficial role of potassium iodide (KI) as a reaction modifier in ZABs employing manganese dioxide (MnO₂) as a bifunctional catalyst. MnO₂ not only exhibits remarkable electrocatalytic activity toward the oxygen reduction reaction (ORR) but also catalyzes the iodide oxidation reaction (IOR), which proceeds at significantly lower potentials than the OER. As a result, KI-modified MnO₂ ZABs achieve a remarkably low charging voltage of ≈1.8 V and an energy efficiency of 69.9% at 5 mA/cm². Although the IOR is not fully reversible in alkaline media and its effectiveness depends on the iodide concentration in the electrolyte—which may decrease upon repeated discharge–charge cycling—the suppression of electrode degradation enables stable operation for more than 200 charge–discharge cycles. These findings demonstrate the synergistic effect of KI and MnO₂ in enabling an efficient ORR/IOR pathway, providing a sustainable and cost-effective alternative to noble metal catalysts and opening new perspectives for the practical development of high-performance ZABs. Full article

As electronic technologies continue to advance, the demand for high-performance and safe batteries has steadily increased. However, silicon-based anode materials experience severe volume expansion and poor structural stability during cycling, which limits their practical application. In this study, we synthesized hollow mesoporous silica to develop an anode material with long-term cycling stability. Electrochemical analysis revealed that the material exhibited low-capacity decay, decreasing from 125 mA·h·g⁻¹ to 120 mA·h·g⁻¹ at a C-rate of 20 C, and retained a 49 mA·h·g⁻¹ after 500 charge–discharge cycles at a C-rate of 10 C. Furthermore, electrochemical impedance spectroscopy and Scanning Electron Microscopy analysis confirmed that the hollow mesoporous silica structure is long-term cycling stability in the anode. Full article

The extensive application of synthetic dyes in various industries and potential accidental uncontrolled discharge into natural water bodies have led to significant environmental challenges and a need for effective treatment. In this study, UiO-66 metal–organic framework/activated carbon (MOF/AC) composites were used to evaluate the photocatalytic degradation of Congo Red dye (CR) in aqueous solution under natural solar irradiation. The degradation efficiency of CR was determined using UV-Vis spectroscopy, while material characterization and additional insight into the reaction mechanism were obtained by XRD, FTIR, and Raman analysis. For a 50 ppm CR solution, within a 2 h reaction time, pure MOF achieved 57.2% and 26.3% degradation under solar irradiation and dark conditions, respectively, while the 75/25 MOF/AC composite reached 74% and 38.3% under the same conditions. These results confirm the synergistic interaction between MOF and AC, where AC acts as an electron sink, preventing charge recombination and enhancing photocatalytic activity. Chemisorption occurred simultaneously with photocatalytic degradation on the MOF surface. Reusability tests showed that pure MOF retained the highest stability over repeated cycles. Overall, the combination of MOF and AC enhances catalytic performance, which represents a sustainable approach for treating dye-contaminated wastewater under natural solar conditions. Full article

Tungsten oxide (WO₃) nanostructures have emerged as promising electroactive materials due to their high pseudocapacitance, structural versatility, and chemical stability, while reduced graphene oxide (rGO) provides excellent electrical conductivity and surface area. The strategic combination of these nanomaterials in hybrid electrodes has gained attention for enhancing the energy storage performance of supercapacitors. In this work, we report the fabrication and electrochemical performance of nanostructured multilayer films based on the electrostatic Layer-by-Layer (LbL) self-assembly of poly (amidoamine) (PAMAM) dendrimers alternated with tungsten oxide (WO₃) nanofibers dispersed in reduced graphene oxide (rGO). The films were deposited onto indium tin oxide (ITO) substrates and subsequently subjected to electrochemical reduction. UV-Vis spectroscopy confirmed the linear growth of the multilayers, while atomic force microscopy (AFM) revealed homogeneous surface morphology and thickness control. Electrochemical characterization by cyclic voltammetry (CV) and galvanostatic charge–discharge (GCD) revealed a predominantly electrical double-layer capacitive (EDLC) behavior. From the GCD measurements (PAMAM/rGO-WO₃)₂₀ films achieved an areal capacitance of ≈2.20 mF·cm⁻², delivering an areal energy density of ≈0.17 µWh·cm⁻² and an areal power density of ≈2.10 µW·cm⁻², demonstrating efficient charge storage in an ultrathin electrode architecture. These results show that the synergistic integration of PAMAM dendrimers, reduced graphene oxide, and WO₃ nanofibers yields a promising strategy for designing high-performance electrode materials for next-generation supercapacitors. Full article

Sodium-ion batteries (SIBs) have arisen as a potential alternative to lithium-ion batteries (LIBs) as a result of the abundant availability of sodium resources at low production costs, making them in line with the United Nations Sustainable Development Goals (SDGs) for affordable and clean energy (Goal 7). The current review intends to comprehensively analyse the various modification techniques deployed to improve the performance of cathode materials for SIBs, including element doping, surface coating, and morphological control. These techniques have demonstrated prominent improvements in electrochemical properties, such as specific capacity, cycling stability, and overall efficiency. The findings indicate that element doping can optimise electronic and ionic conductivity, while surface coatings can enhance stability in addition to mitigating side reactions throughout cycling. Furthermore, morphological control is an intricate technique to facilitate efficient ion diffusion and boost the use of active materials. Statistically, the Cr-doped NaV_1−xCr_xPO₄F achieves a reversible capacity of 83.3 mAh/g with a charge–discharge performance of 90.3%. The sodium iron–nickel hexacyanoferrate presents a discharge capacity of 106 mAh/g and a Coulombic efficiency of 97%, with 96% capacity retention over 100 cycles. Furthermore, the zero-strain cathode Na₄Fe₇(PO₄)₆ maintains about 100% capacity retention after 1000 cycles, with only a 0.24% change in unit-cell volume throughout sodiation/desodiation. Notwithstanding these merits, this review ascertains the importance of ongoing research to resolve the associated challenges and unlock the full potential of SIB technology, paving the way for sustainable and efficient energy storage solutions that would aid the conversion into greener energy systems. Full article

In this study, a battery case was developed using a 3D (three dimensional)-printed composite of hexagonal boron nitride (hBN) and polylactic acid (PLA) to enhance the thermal performance of lithium-ion battery (LiB) modules. A 10 wt.% amount of hBN was incorporated into the PLA matrix to improve the composite’s thermal conductivity while maintaining electrical insulation. A 3S2P (3 series and 2 parallel) battery configuration was initially evaluated based on the results of a baseline study for comparison and subsequently subjected to a newly developed test procedure to assess the thermal behavior of the designed case under identical environmental conditions. Initially, X-ray diffraction (XRD) and scanning electron microscopy (SEM) analyses were utilized for material characterization, and their results verified the successful integration of hBN by confirming its presence in the hBN-PLA composite. In thermal tests, experimental results revealed that the fabricated hBN-PLA composite battery case significantly enhanced heat conduction and reduced surface temperature gradients compared to the previous baseline study with no case. Specifically, the maximum cell temperature (T_max) decreased from 48.54 °C to 45.84 °C, and the temperature difference (ΔT) between the hottest and coldest cells was reduced from 4.65 °C to 3.75 °C, corresponding to an improvement of approximately 20%. A 3S2P LiB module was also tested under identical environmental conditions using a multi-cycle charge–discharge procedure designed to replicate real electric vehicle (EV) operation. Each cycle consisted of sequential low and high discharge zones with gradually increased current values from 2 A to 14 A followed by controlled charging and rest intervals. During the experimental procedure, the average ΔT between the cells was recorded as 2.38 °C, with a maximum value of 3.50 °C. These results collectively demonstrate that the 3D-printed hBN-PLA composite provides an effective and lightweight passive cooling solution for improving the thermal stability and safety of LiB modules in EV applications. Full article

As a critical energy component in electric vehicles, energy storage systems, and other applications, the accurate estimation of the State of Health (SOH) of lithium-ion batteries is crucial for performance optimization and safety assurance. To this end, this paper proposes a hybrid model named Transformer–KAN, which integrates Transformer architecture with Kolmogorov–Arnold Networks (KANs) for precise SOH estimation of lithium-ion batteries. Initially, five health features (HF1–HF5) strongly correlated with SOH degradation are extracted from the historical charge–discharge data, including constant-voltage charging duration, constant-voltage charging area, constant-current discharging area, temperature peak time, and incremental capacity curve peak. The effectiveness of these features is systematically validated through Pearson correlation analysis. The proposed Transformer–KAN model employs a Transformer encoder to capture long-term dependencies within temporal sequences, while the incorporated KAN enhances the model’s nonlinear mapping capability and intrinsic interpretability. Experimental validation conducted on the NASA lithium-ion battery dataset demonstrates that the proposed model outperforms comparative baseline models, including CNN–LSTM, Transformer, and KAN, in terms of both RMSE and MAE metrics. The results indicate that the Transformer–KAN model achieves superior estimation accuracy while exhibiting enhanced generalization capabilities across different battery instances, indicating its strong potential for practical battery management applications. Full article