Energy Storage最新文献

In this study, the optimization of heat transfer in a metal hydride hydrogen tank to maximize hydrogen storage was investigated. A finite element model of a quarter tank was developed in COMSOL Multiphysics with parameterized geometry. The main objectives were to maximize stored hydrogen mass and minimize tank filling time while maintaining temperature uniformity within the tank. A design of experiments (DOE) approach was used with key geometrical parameters. Compared to the base case, the hydrogen stored mass increased from 0.26 to 0.46 kg, and the tank filling time reduced from over 1100 to 450 s. The optimal design (Design point 15) resulted in an absorbed hydrogen mass of 0.4624 kg, with a charging time of 450 s, showing the most balanced performance in terms of maximizing storage while minimizing filling time and better heat dissipation. This demonstrates the potential of optimizing heat transfer to significantly improve metal hydride hydrogen storage performance. The model can be further improved by exploring different cooling designs and materials.

This study presents the successful synthesis and characterization of polyaniline (PANI), PANI/reduced graphene oxide PANI/rGO (PR), and PANI/rGO/ZnO (PRZ) nanocomposites as electrode materials for supercapacitors. Employing electrospinning and electrospraying techniques, we developed nanofibrous composites with enhanced structural and electrochemical properties. The addition of rGO and ZnO in the PRZ composite significantly improved specific capacitance, stability, and charge-transfer efficiency. Electrochemical analyses, including cyclic voltammetry (CV), galvanostatic charge–discharge (GCD), and electrochemical impedance spectroscopy (EIS), revealed a peak specific capacitance of 845 F g⁻¹ at 0.5 A g⁻¹ for PRZ, outperforming PR (395 F g⁻¹), and PANI (140 F g⁻¹). These enhancements are attributed to the synergistic effects of carbon-based and pseudocapacitive components, resulting in higher conductivity, improved redox activity, and reduced internal resistance. Additionally, the PRZ composite exhibited excellent cyclic stability, retaining 89% of its capacitance over 5000 cycles, underscoring its durability and suitability for long-term energy storage applications.

With the progressive switching from a conventional transportation system to an intelligent transportation system (ITS), the eco-friendly alternative is made possible in metro cities. Moreover, electric vehicles (EVs) gained more attention due to their low charging costs, low energy consumption, and reduced greenhouse gas emissions. However, a single failure or malfunction in an EV's intrinsic components due to poor charging infrastructure can bring about a high tendency of fault occurrence that needs to be diagnosed earlier for efficient safety management. In addition, ensuring the safety and reliability of these EV batteries remains a critical challenge that underscores the importance of an efficient battery fault detection system, pivotal in enhancing battery safety and lifespan. Hence, the research centers on developing a well-structured battery fault detection model leveraging a Search- Survive optimization (SSO) based deep incorporated model. This incorporated model combines Deep Convolutional Neural Network (Deep CNN), Deep Bidirectional Long-Short Term Memory (Deep BiLSTM), and Deep Belief Network (DBN) that assists in extracting the hierarchical representations and the spatial–temporal features associated with the various EV faults. The deep incorporated model is optimized with SSO that aids the model to perform enhanced battery fault detection of EVs. Performance assessment relies on key parameters like accuracy, sensitivity, and specificity, based on the NASA battery dataset. Impressively, the SSO-based Deep Incorporated model attains an accuracy of 96.00%, sensitivity of 96.29%, and specificity of 95.72 for 80% of training. With k-fold 10 validation, the proposed model attained the metric values of 96.31%, 97.29%, and 95.32% respectively using the NASA dataset and surpassed other existing techniques.

Thermal energy storage (TES) systems are becoming increasingly crucial as viable alternatives for effective energy utilization from various sources, such as solar power plants and waste heat from different industrial sectors. The present work focuses on latent heat TES system optimization for solar thermal power plant applications. This study aims to assess the impact of different thermal processing factors on the efficiency of TES systems. Parametric analysis determines a TES system's charging and discharging durations that use latent heat storage material. Thermal processing conditions were selected as input parameters, such as the heat transfer fluid inlet temperature, flow rate, and number of phase change material (PCM) capsules. Experiments were planned to use the L₉ orthogonal array of the Taguchi method, and response measures, such as charging time (CT) and discharging time (DT), were monitored. A signal-to-noise ratio analysis was used to evaluate the significance of the thermal processing parameters on the response measures. Response surface methodology (RSM) postulates the mathematical relationships between process conditions and responses. Finally, the multi-objective Jaya optimization algorithm (MOJOA) was used to optimize the parametric combination to minimize CT and maximize DT simultaneously. A heat transfer fluid inlet temperature of 65°C, flow rate of 2 L/min, and 40 PCM capsules were determined as the optimal parametric conditions by MOJOA for predicting the combined CT and DT. The verification test results substantiate the enhanced responses of the latent heat TES system, specifically in the CT and DT. Utilizing the integrated Taguchi method, RSM-MOJOA is advantageous for examining, modeling, and predicting PCM-based TES systems.

Effect of multiwalled carbon nanotubes (MWCNTs) addition on thermoelectric properties of polycrystalline LSCCO (La_1.98Sr_0.02Cu_0.94Co_0.06O₄) has been examined. The samples have been synthesized via the solid-state reaction technique. Micro-structural and surface morphology of the synthesized pellets have been investigated using X-ray diffraction and Field Emission Scanning Electron Microscopy, respectively. The electrical resistivity and Seebeck coefficient of investigated pellets have been measured using a custom-built apparatus between 300 and 450 K. Nevertheless, the transient heat transfer technique has been adopted for thermal conductivity measurement. The addition of MWCNTs significantly enhances the electrical conductivity and reduces the thermal conductivity of LSCCO. This results in a remarkable improvement in the figure of merit in spite of the reduction in Seebeck coefficient with MWCNTs addition. The maximum ZT value ~0.07 is achieved at 323 K for 0.05 wt% MWCNTs-loaded LSCCO, which is ~28 times that of pristine LSCCO. The enhanced thermoelectric performance is attributed to the increased carrier concentration, reduced grain size, and improved interface phonon scattering due to MWCNTs addition. Our results demonstrate the potential of MWCNTs as an effective additive to enhance the thermoelectric properties of LSCCO-based materials.

This study proposes a system to store waste heat as liquid hydrogen using a proton exchange membrane electrolyzer (PEME) and a mixed refrigerant hydrogen liquefaction cycle. The novelty of this study lies in proposing a waste heat recovery system that stores electricity as liquid hydrogen, consuming less power due to the improved exergy efficiency of the components. The proposed system is analyzed to achieve better efficiency in terms of thermal and exergy efficiencies. Waste heat is used to generate power by an organic Rankin cycle (ORC), produced electricity is utilized in the PEME unit and compressors of liquefaction cycle to produce and liquefy hydrogen, respectively. Codes are written in EES software to simulate the system. Thermodynamic analysis is done in order to achieve better thermal efficiency for the proposed model. Membrane potential at different values of current density is calculated and compared with validate the simulated model. The exergy efficiency of the liquid hydrogen production process is 57%. The exergy efficiency, rate of power produced in ORC, and rate of hydrogen production by the electrolyzer increase significantly by increasing the isentropic efficiency of the turbine. At a temperature of 340 K for the evaporator, the thermal efficiency of ORC is obtained at 8.5%, which is approximately 3% higher compared with that of the previous similar process.

Syngas rich in hydrogen, generated through renewable-powered co-electrolysis of water (H₂O) and carbon dioxide (CO₂) using solid oxide electrolysis cells (SOEC), have gained significant attention due to its high efficiency and conversion rates. This method offers a promising solution for mitigating global warming and reducing CO₂ emissions by enabling the storage of intermittent renewable energy. This study investigates solar-integrated co-electrolysis of H₂O and CO₂ via SOEC to produce hydrogen-rich syngas, which is then utilized for methanol synthesis through a series of heat exchangers and compressors. Parabolic dish solar collectors supply thermal energy, while photovoltaic modules provide electricity for SOEC operation. CO₂ from industrial processes is captured and combined with steam at the SOEC inlet for co-electrolysis. The proposed system is modeled using engineering equation solver software, incorporating mass, energy, and exergy balance equations. The system's performance is analyzed by varying key parameters such as direct normal irradiance, heat exchanger effectiveness, current density, cell temperature, and pressure. The proposed system achieves a solar-to-fuel efficiency of 29.1%, with a methanol production rate of 41.5 kg per hour. Furthermore, an economic analysis was conducted to determine the levelized cost of fuel.

In the rapidly evolving landscape of energy storage technologies, sodium-ion batteries (SIBs) have emerged as promising alternatives to conventional lithium-ion batteries. SIBs exhibit moderate to high specific energy ranging from approximately 70 to 170 Wh/kg, ensuring suitability for diverse applications. Furthermore, with their abundance of raw materials and potential for lower costs, sodium-ion batteries are attracting significant interest from researchers, manufacturers, and investors. This heightened interest is evidenced by the exponential growth in the number of patents filed for SIBs, totalling 142 648 patents. This surge in patent filings underscores the growth pattern of SIBs as promising alternatives in the energy storage landscape. In this dynamic environment, securing and maintaining a robust patent portfolio is imperative for companies and innovators to establish a competitive edge, enabling them to capitalize on the increasing market demand. This perspective examines the strategies involved in building, protecting, and managing a robust patent portfolio as well as provides intellectual property challenges and patent filing opportunities in SIB technologies.

A hybrid energy system, comprising a diesel engine as the prime mover, electrical and absorption chillers, a backup boiler, and a multi-effect distillation through thermal vapor compression (MED-TVC) unit, has been utilized to meet the requirements of a residential complex. This study focuses on redesigning and optimizing the system to enhance environmental conditions, reduce pollutants, and minimize the use of fossil energy. The feasibility and design of renewable energy systems, including wind turbines (WTs), photovoltaic panels (PVs), and flat plate collectors (FPCs), have been examined. Genetic algorithm (GA) has been employed for optimization. The hybrid system employs 21 design variables, with 24 design variables chosen for optimization alongside renewable energies. The total annual cost (TAC), encompassing investment, operation, and pollution emission fines, has been chosen as the objective function for minimization. The results indicate that the use of WTs has not been cost-effective, and solar energy can enhance the system's performance in Bandar Abbas, Hormozgan province in Iran. In the case of using a combined system, the objective function value was 2.0472 × 10⁶ $/year, and when using renewable energies, the objective function became 1.6795 × 10⁶ $/year. Thus, the proposed combined-renewable system has reduced the objective function by 17.96%.

Solar thermal energy is crucial in our transition to renewable energy sources. Recent studies have focused on enhancing the efficiency of solar collectors by minimizing thermal energy loss during absorption. A promising approach involves an innovative design that integrates phase change materials (PCMs) and rotating tubes to capture thermal energy more effectively. Advanced nitride-based salt hydrates, with boron-arsenide additives, enhance thermal performance of the collector. In a flat plate collector using composite PCMs, radiative heat loss decreases from 250 to 210 W (a 6% reduction) with tube rotation, while convective heat loss drops from 225 to 195 W (a 4% decrease). The decomposition rate of the novel PCMs is low, measuring only 0.5% at a maximum temperature of 850°C, with a specific heat capacity of up to 4.50 W/m K. This unique blend, including the Sn₃N₄-LiNO₃-KNO₃/boron arsenide mixture, enhances thermal conductivity by 30%, significantly improving thermal absorption rates. The exergy efficiency achieved with the Nano-enhanced phase change materials (NEPCM) and tube rotation reaches an impressive 90%. With tube rotation at 3 rad/min, the flat plate collector's efficiency improves by 22%, reaching an overall efficiency of 90% at a fluid flow rate of 25 kg/h. Simulations using Anaconda Jupyter Notebook and Python validate the effectiveness of both tube rotation and NEPCM in enhancing collector efficiency.