Energy Storage最新文献

The increasing need for effective and sustainable energy storage solutions has spurred the advancement of sophisticated electrode materials for high-performance supercapacitors. This research tackles the fundamental limitations of graphitic carbon nitride (g-C₃N₄ or GCN) specifically its low conductivity and restricted capacitance by engineering VO₂/GCN, Pd/GCN, and Ag/GCN nanocomposites. This research builds on our earlier published studies regarding VO₂/GCN, Pd/GCN, and Ag/GCN composites, which were synthesized through hydrothermal and calcination techniques. Comprehensive structural, chemical, and morphological analyses not only validated successful synthesis but also highlighted critical features that contribute to improved electrochemical performance. X-ray diffraction (XRD) confirmed phase purity and the preservation of the GCN lattice, while FT-IR and Raman spectroscopy demonstrated strong electronic interactions between dopants and GCN, facilitating charge transfer. BET analysis indicated mesoporous structures with a high surface area and optimized pore distribution, which directly enhances ion accessibility. FESEM and TEM illustrated well-dispersed VO₂, Pd, and Ag nanoparticles on GCN nanosheets, creating interconnected conductive networks that promote rapid electron transport. Elemental mapping verified uniform dopant distribution, ensuring compositional stability during cycling. Collectively, these characterizations elucidate the exceptional electrochemical response, as evidenced by cyclic voltammetry (CV), galvanostatic charge–discharge (GCD), and electrochemical impedance spectroscopy (EIS) measurements. VO₂/GCN (3VO/GN) achieved the highest specific capacitance (1416 F/g), the lowest charge-transfer resistance (0.75 Ω), and the maximum double-layer capacitance (4.2 mF), surpassing Pd/GCN (401.1 F/g) and Ag/GCN (195.3 F/g). These findings emphasize the importance of strategic doping methods in optimizing the structural, morphological, and electronic characteristics of GCN to enhance charge storage, positioning VO₂/GCN as a prominent candidate for scalable, next-generation supercapacitor technologies.

The rising concern over environmental pollution has accelerated the adoption of electric vehicles worldwide. However, the increased demand for electric vehicle charging places an additional burden on the power grid, which primarily relies on limited fossil fuel resources. The adoption of solar-assisted EV charging has the potential to reduce fossil fuel dependence, lower greenhouse gas emissions, and promote sustainable urban mobility, yielding significant societal and environmental benefits. This paper presents a Fuzzy Logic Control (FLC)-based Power Management Controller (PMC) for a single-phase grid-connected electric vehicle (EV) charging station powered primarily by solar PV and supported by a Battery Energy Storage System (BESS). The proposed system is capable of operating in both grid-to-vehicle (G2V) and vehicle-to-grid (V2G) modes. The study models various components, including PV arrays, storage batteries, and grid interconnections. The effectiveness of the proposed controller is demonstrated through MATLAB/Simulink simulations across various operational scenarios, highlighting its capability to maintain DC link voltage stability and minimize reliance on the grid. The control strategy is benchmarked against conventional PID and Artificial Neural Network (ANN) controllers. Results show that the proposed controller eliminates voltage overshoot from 9.6% to 0%, reduces settling time from 1.18 to 0.41 s, and shortens rise time from 0.27 to 0.24 s, thereby confirming enhanced voltage regulation and improved transient response. This study is based on simulation analysis, which can be extended through experimental validation for broader applicability.

The current work investigates approaches to augment photovoltaic (PV) panels through phase change material (PCM) systems' efficiency, with the addition of aluminum fins and varying angles of inclination. A two-dimensional numerical simulation using the enthalpy–porosity method was conducted in ANSYS Fluent for modeling PCM melting behavior. This research studied five cases of PCMs (for the same fin area and size) at five angles of inclination (30°, 60°, 90°, 120°, and 150°). The results show good agreement with literature values. PV tilt angle's influence on heat dissipation for conduction and natural convection ratio in PCM is significant. Case 3 configuration (90° inclination) exhibits the best performance of all configurations: lowest PV temperature (318.2 K), highest electrical efficiency (11.83%), maximum thermal efficiency (46.7%), maximum melting fraction transport (0.47), and maximum overall efficiency (58.61%). Case 3 exhibits a temperature improvement of 3.36 K in comparison to the worst-performing setup at 150° (Case 5) values with increases of 1.72% in electrical efficiency, 23.2% in thermal efficiency, 34.3% in melting fraction, and an 18.8% improvement in overall efficiency. Past studies have looked at the geometry of the fins, while this current study looks at the orientation of the fins. The study confirms that the 90° fin angle (which is least obstructive to vertical thermal transfer) is ideal to optimize the PV-PCM systems' efficiency under the current boundary conditions.

In the domain of electric vehicles (EVs) and portable electronics, lithium-ion batteries constitute a vital power source, necessitating precise state estimation within Battery Management Systems (BMS) to determine the State of Charge (SOC), State of Health (SOH), and terminal voltage. Electrical equivalent circuit models (ECMs) calibrated using temperature-dependent Open Circuit Voltage (OCV) profiles and dynamic experimental data are extensively utilized to characterize the nonlinear electrochemical dynamics of the cell. Consequently, a simple and accurate method of OCV estimation is required for real-time implementation in the BMS. Hence, this study introduces an adaptive segmentation approach employing a piecewise linearization method with first-order polynomial fitting for static OCV estimation, thereby facilitating improved segmentation points of the OCV–SOC curve and enhancing estimation accuracy. The OCV estimation accuracy across various operating temperatures is evaluated by comparing it with existing methodologies and examining its impact on dynamic terminal voltage. The Root Mean Square Error (RMSE) value of the proposed method was compared with other methods, including data-driven methods, model-based, high-order polynomials, and Gaussian regression, irrespective of operating temperature. Experimental validation of the proposed methodology was performed using MATLAB Simulation. The performance of the approach was assessed by RMSE and R² estimation metrics, demonstrating an improvement in the OCV-SOC error across all temperature ranges compared with other well-established methods. Furthermore, the RMSE value of the terminal voltage and behaviors of model parameters using this method are comparable to those of the existing model-based approach.

With the growing popularity of electric vehicles (EVs) driven by environmental concerns, an effective and efficient battery thermal management system (BTMS) is necessary. For effective BTMS, the design plays a critical role which decides the thermal behavior of a battery pack. This study presents two design structures (D1 and D2) for pouch-type battery thermal management systems. The critical design aspects of D1 and D2 differ in how the heat carrier plate is designed and arranged within the battery pack. This plate plays an important role in transferring heat from the cell to the cooling medium through the thermal pad. The influence of parameters such as charging/discharging rate (C-rate), ambient temperature (T_amb), and convective coefficient (h) on the battery pack's maximum temperature and the maximum temperature difference (∆T_max) is evaluated using the Newman, Tiedemann, Gu, and Kim (NTGK) battery model. The Taguchi method is used to minimize the number of simulations. Also, the change of input parameters' contribution to the thermal behavior of the battery pack due to design change is studied. It is observed that the D2 battery pack structure is more effective in maintaining lower temperatures and better temperature uniformity compared to D1. ∆T_max for D1 is observed as 5.33 K, while D2 is 4.52 K, with the T_amb, C-rate, and convective heat transfer coefficient as 318 K, 9, and 5 W/m²K. Also, up to 3 C-rate, D1 provides nearly similar thermal performance as D2, hence D1 can be preferred over D2 due to its compact size. However, D2 should be preferred for higher C-rates as the difference in thermal performance increases significantly.

The safe construction and efficient operation of underground gas storage (UGS) facilities are of great significance for enhancing peak-shaving capacity and ensuring national energy security. The breakthrough pressure of gas (H₂, CH₄, CO₂, etc.) in caprock is a critical parameter for evaluating the sealing performance of UGS systems. In this study, typical caprock core samples, including mudstone and sandy-mudstone, were drilled from the depleted gas reservoirs located in central and northwestern China. The experimental apparatus was developed to conduct breakthrough pressure tests on caprock samples under various temperature conditions. Breakthrough pressures of CH₄, CO₂, and H₂ were experimentally measured and comparatively analyzed to investigate the influencing factors. In addition, numerical simulations of gas breakthrough pressure on multicomponent gas were carried out to further explore the evolution characteristics and controlling factors of breakthrough pressures for CH₄, CO₂, and H₂. The results showed that, for the same gas, the breakthrough pressure decreased with increasing temperature, and under identical temperature conditions, the values followed the order H₂ < CH₄ < CO₂. The CH₄ content significantly enhanced the breakthrough pressure and the rise of the initial water saturation also led to an increase in breakthrough pressure. This study presented innovative insights into gas breakthrough pressure behavior by integrating experimental results with numerical modeling, providing theoretical support and practical guidance for subsurface hydrogen storage, natural gas storage, and CO₂ geological sequestration.

As the capacity and volume of energy storage batteries in energy storage power stations continue to increase, significant thermal non-uniformity has emerged in prismatic lithium iron phosphate batteries during charge and discharge processes. Considering temperature as a critical factor influencing the reaction rate of electrochemical reactions within lithium-ion batteries, this research proposes a novel zonal thermal-electrical coupling model. By implementing zonal processing based on the actual physical structure of lithium-ion batteries, thermal and electrical models are constructed separately. Temperature data from each zone of the battery are collected to parameterize the model, thereby achieving thermal-electrical coupling. Validation results demonstrate that this model accurately describes the temperature gradient within the lithium-ion battery, with an average error of less than 0.63°C in the predicted temperature under complex operating conditions. Furthermore, it accurately reflects the parameter changes during the operation of the battery's electrical model, with an end-voltage error within 58 mV, and the model's single-step computation time is only 0.36 ms. This research aims to provide a low-computational-cost and accurate method for battery temperature field simulation, offering significant reference value and support for the technological development in the field of energy storage power stations.

Demand for effective and cost-effective energy management solutions has increased due to residential settings' raising reliance on energy storage systems and renewable energy sources; however, integrating these systems seamlessly while preserving balanced grid interaction and financial benefits is a major challenge. This paper proposes an optimal integration strategy for renewable energy and energy storage in Home Energy Management Systems (HEMS) to enhance grid interaction and maximize economic benefits. The proposed approach uses Hiking Optimization (HO) to improve the Peak-to-Average Ratio (PAR) and minimize energy expenditures by integrating renewable energy sources and sophisticated optimization techniques into the HEMS. The HO method is employed to optimize the HEMS by minimizing daily energy costs and reducing the PAR through efficient utilization of energy storage systems and renewable energy sources. The proposed method is implemented on the MATLAB platform and contrasted with existing methods, including Particle Swarm Optimization (PSO), Genetic Flower Pollination Algorithm (GFPA), and Deep Neural Network (DNN). The comparison demonstrates the proposed method's improved performance, which achieves a cost of 440 cents. In contrast, the PSO approach yields 550 cents, the GFPA method achieves 666 cents, and the DNN method reaches 688 cents. This comparison illustrates the performance of the proposed strategy in optimizing HEMS performance for cost reduction in residential applications, outperforming traditional energy management techniques.