Energy Storage最新文献

On a global scale, significant progress is being made in the field of battery technology for Electric Vehicle (EV) applications, driven by the need to combat carbon emissions and mitigate the effects of global warming. Accurately determining critical parameters, making sure battery storage system diagnosis, and functioning are correct are critical to the feasibility of EVs. However, insufficient supervision and safety measures for these storage systems may lead to serious problems like a thermal runaway, overcharging, overheating, cell imbalances, and fire hazards. To tackle these challenges, the presence of an efficient battery management system becomes paramount. By facilitating accurate monitoring, managing heat dissipation, regulating charging-discharging procedures, guaranteeing battery safety, and offering protection measures, this system is essential to maximizing battery performance. The key intention of this innovative approach is to improve the longevity of EV batteries during extended periods of operation. By assessing vehicle velocity, remaining battery energy, and State of Charge (SoC), the proposed method effectively manages SoC in both the battery and ultracapacitor. This control is accomplished through a two-stage convolutional neural network-based system known as the Charge Sustain-CNN Controller and the Charge Deplete-CNN Controller. These controllers are fine-tuned using the Fractional Latrans-Hunt optimization (FLHO) algorithm to optimize the performance. The evaluation criteria encompass the battery and ultracapacitor's energy efficiency, as well as vehicle velocity. This novel approach significantly improves the energy storage system in EVs, leading to enhanced energy efficiency and prolonged battery life. Ultimately, experimental results validate the practicality and effectiveness of this developed method. Specifically, the proposed approach attained the Battery's SoC of 72.47%, 91.99%, and 82.88% for the different drive cycles including the FTP75, J1015, and UDDS, respectively.

The shift towards electrical vehicles (EVs) can be an important alternative to internal combustion engines for sustainable energy solutions. However, increased EV adoption will increase the charging demand, and there will be a load on the grid electricity. Integrating solar photovoltaic systems with EV charging infrastructure will not only support environmental goals, but also ensure a more resilient and self-sufficient energy system. A standalone PV system is a good option to reduce the stress on the grid for charging EVs. This present work pivots on the design and performance assessment of a solar photovoltaic system customized for an electric vehicle charging station in Bangalore, India. For this purpose, we have used the PVsyst software to design and optimize a standalone PV system with battery energy storage for EV charging stations. The result shows that 51.1 kWp PV system will be sufficient to meet the energy demand of the charging station by producing 98 313 kWh array energy. The proposed system showed a good average performance ratio of 68.90%. This study shows that the integration of standalone solar photovoltaic systems with EV charging stations is crucial in India and other countries to alleviate grid stress and promote sustainable energy use. This approach not only supports the transition to cleaner transportation but also enhances energy security and reduces dependency on fossil fuels.

Accurate prediction of battery temperature rise is very essential for designing efficient thermal management scheme. In this paper, machine learning (ML)-based prediction of vanadium redox flow battery (VRFB) thermal behavior during charge–discharge operation has been demonstrated for the first time. Considering different currents with a specified electrolyte flow rate, the temperature of a kW scale VRFB system is studied through experiments. Three different ML algorithms; linear regression (LR), support vector regression (SVR), and extreme gradient boost (XGBoost) have been used for prediction. The training and validation of ML algorithms have been done by the practical dataset of a 1 kW 6 kWh VRFB storage under 40 , 45, 50, and 60 A charge–discharge currents and 10 L min⁻¹ of flow rate. A comparative analysis among ML algorithms is done by performance metrics such as correlation coefficient (R²), mean absolute error (MAE), and root mean square error (RMSE). XGBoost shows the highest R² value of around 0.99, which indicates its higher prediction accuracy compared to other ML algorithms used. The ML-based prediction results obtained in this work can be very useful for controlling the VRFB temperature rise during operation and act as an indicator toward further development of an optimized thermal management system.

Lithium-ion batteries (LIBs) are extensively utilized in electric vehicles due to their high energy density and cost-effectiveness. LIBs exhibit dynamic and nonlinear characteristics, which raise significant safety concerns for electric vehicles. Accurate and real-time battery state estimation can enhance safety performance and prolong battery lifespan. With the rapid advancement of big data, machine learning (ML) holds substantial promise for state estimation. This paper systematically reviews several common ML algorithms, detailing the basic principles of each and illustrating their structures with flowcharts. We compare the advantages and disadvantages of various methods. Subsequently, we discuss feature extraction techniques employed in recent studies for estimating state of charge (SOC), state of health (SOH), state of power (SOP), and remaining useful life (RUL), as well as the application of these ML methods in state estimation. Finally, we discuss the challenges associated with using ML methods for state estimation and outline future development trends.

Lithium-ion batteries (LiBs) are the leading choice for powering electric vehicles due to their advantageous characteristics, including low self-discharge rates and high energy and power density. However, the degradation in the performance and sustainability of lithium-ion battery packs over the long term in electric vehicles is affected due to the elevated temperatures induced by charge and discharge cycles. Moreover, the thermal runaway (TR) issues due to the heat generated during the electrochemical reactions are the most significant safety concern for LiBs, as inadequate heat dissipation can be potentially hazardous, leading to explosions and fires. Considering the safety of EVs and for better performance, understanding the mechanism of TR is of paramount importance. This review provides a comprehensive analysis of the TR phenomenon and underlying electrochemical principles governing heat accumulation during charge and discharge cycles. Furthermore, the article explores the cell modeling and thermal management techniques intended for both individual lithium-ion battery cells and larger battery packs, with a particular emphasis on enhancing fire prevention and safety measures. The main goal of this review paper is to offer new insights to the developing battery community, assisting in the development of efficient battery thermal management systems (BTMS) using enhanced cooling methodologies. This article could also support the advancement of next-generation electric vehicle battery packs equipped with built-in safety features to improve the cycle life of LiBs and prevent thermal runaway accidents.

Metal oxide semiconductors, known for their exceptional optical transparency, high carrier mobility, and stability, have found extensive use in emerging technologies such as optoelectronics and energy storage devices. Among all metal oxide semiconductors, nickel oxide (NiO) stands out as a highly favorable candidate due to its p-type conductivity along with its substantial band gap (3.5–4 eV) for the broad range of applications, including gas sensors, high-rate Lithium-ion batteries, high-performance supercapacitors, and photovoltaic devices. In light of these versatile applications, our current study presents a comprehensive comparative analysis of the structural and optoelectronic properties of NiO and potassium (K)-doped NiO nanocrystals. The nanocrystals were synthesized using the co-precipitation route and subsequently annealed at 500°C under ambient conditions. The effect of K doping on the structural and optoelectronic characteristics was systematically examined using various techniques, including x-ray diffraction, UV–visible spectroscopy, Raman spectroscopy, and Hall effect measurements. To explore the structural characteristics, XRD measurements were performed, which confirm the FCC structure of nanocrystals. The optical property analysis suggested that the formation of the energy level can contribute to reduction of the band gap. A sharp peak at 397 cm⁻¹ is associated with NiO bond in FTIR spectra which verifies the formation of nanocrystals. Moreover, the incorporation of K increases the intensity of the Raman peaks, which provides evidence for the higher degree of crystallinity in doped samples. These results of Raman scattering are in good agreement with XRD outcomes. In addition, the resistivity of NiO nanocrystals decreases monotonically with the increasing K concentration. The results of temperature-dependent resistivity further demonstrate that electrons required more energy to jump from one polaron state to another in the case of x = 0.01 M and 0.03 M doped Ni_0.5-xK_xO samples. The combination of a diminished band gap and enhanced conductivity makes these materials exceptionally promising for applications in optoelectronics and energy storage.

Indeed, as India progresses towards its mission on green hydrogen production and the adoption of alternative fuels, the development of stand-alone systems supporting the integration of hydrogen energy becomes imperative. Optimal sizing of standalone hybrid systems presents a significant challenge to meet power reliability, technical and economic viability. The present study explores the topology of hybrid energy storage systems in the stand-alone scenario and assesses its technical and economic feasibility through an optimization approach. The objective is to size the components of the standalone system which includes PV generator, wind generator, hydrogen energy storage unit, battery storage unit (BES), electrolyzer and fuel cell rendering low value of loss of power supply probability (LPSP). Also, finest-fitting storage system for the Solar-wind Hybrid Stand-Alone Microgrid (HSAM) is identified. Modeling, simulation, and optimization of the HSAM are carried out using HOMER PRO. A load of 20.46 kW_p and average yearly energy consumption of 165.44 kWh/day was considered for the technical and economic feasibility-based evaluation. Scaled annual average values of input metalogical data such as wind speed, temperature and solar irradiance is considered as sensitivity cases. Based on the simulation results, it can be observed that the proposed HSAM system with HyESS exhibits the lowest values for NPV, O&M Cost, and LCOE compared to other system configurations. From sensitivity analysis it is observed that with variation of resource based inputs like irradiance, wind speed and temperature, there is a fluctuation of nearly 10% in the NPV and LCOE of the HSAM system. The LCOE for this system is estimated to be $0.289/kWh, while the NPV is projected to be $274 470. The Internal Rate of Return (IRR) for the 25-year project is calculated to be 6.1%, indicating a favorable return on investment. Additionally, the Simple Payback Period is determined to be 8.7 years. Furthermore, validation of the optimal LPSP through HOMER is achieved through creation of an objective function employing a non-linear least square approach. The proposed HyESS outperforms the standard BES with an LPSP of 3.1 × 10⁻⁶ over 4.3 × 10⁻⁴. The suggested system achieves a high level of dependability through LPSP values that are significantly closer to zero, ensuring a reliable operation.

Solar energy's abundant availability in India makes it a potential solution to meet increasing energy needs without harming environment. Solar water heating (SWH) systems are effective in converting solar radiation into heat for domestic and industrial applications. However, their inability to provide hot water during nighttime or off-sunshine hours due to the intermittent nature of solar energy presents a challenge. Thermal energy storage, particularly using phase change materials (PCMs), has emerged as a solution. In this study, spherical ball-type encapsulated PCM, specifically RT60, was incorporated into a solar water heater tank under variable atmospheric conditions. The PCM stores excess energy during daylight and releases it when the water temperature drops below the PCM's melting point. The results reveal a significant reduction in the temperature drop of water from 4.5°C to 1.4°C when utilizing PCM compared to conventional storage water tanks without PCM. Additionally, energy storage capacity is enhanced by 5.13% with PCM incorporation. Furthermore, modifying the encapsulation geometry to a rectangular shape enhances heat transfer and reduces temperature drop even further to 0.9°C, making it a promising approach to improving SWH system performance. This study highlights the possibility of enhancing encapsulation shape and applying PCM to enhance SWH system performance. The results highlight the possibility of increasing solar thermal systems' energy efficiency and usefulness, which will support sustainable energy sources.

In this work, we report on CoMnSi half-Heusler alloy as a cathode material for secondary lithium-ion batteries using ab initio methodology based on the density functional theory. The first-principle calculations have been performed via the Wien2k package, which utilizes the full potential linearized augmented plane wave (FP-LAPW) method to estimate the stability of the proposed structures and electronic characteristics while considering the exchange and correlation effects within the generalized gradient approximation. This alloy is found structurally stable with better electronic properties and with the alloying of lithium (Li) into the host lattice of CoMnSi, the metallic character is attained for Li_xCo_1−xMnSi (0.125 ≤ x ≤ 1). We propose possible reactions at electrodes during the electrochemical lithiation. The addition of Li in place of the Co atom is found to be an endothermic process. With the increase in lithium concentration, a substantial change in the total and atom projected density of states around the Fermi level is observed. The theoretical maximum specific capacity (C_M) and theoretical open circuit voltage (OCV) increase with the increase of lithium concentration in CoMnSi. The C_M and OCV values attain a maximum value of around 297 mAh/g and 2.4 Volts for x ≥ 0.75, which means towards the complete conversion of CoMnSi into LiMnSi. The LiMnSi exhibits a similar structure as CoMnSi, which is also advantageous for the overall performance of lithium-ion batteries to avoid any volumetric change during the charging and discharging cycles. Hence, the proposed half-Heusler alloys have great potential to be used as a cathode material for lithium-ion batteries.

Phase change material (PCM)-based heat storage systems utilize the absorption or release of latent heat during a phase change of the storage material to store thermal energy. Nevertheless, the effectiveness of these systems is restricted by the shape and structure of their confinement, as well as the heat conductivity of the storage material. This work investigates a novel method to enhance the effectiveness of PCM systems by concurrently utilizing two techniques: the inclusion of nanoparticles and the alteration of the system's geometry. Introducing nanoparticles enhances the thermal conductivity of the storage medium while altering the shape, which improves heat transfer efficiency by adjusting the surface area available for heat exchange. RT-35 was tested for use in latent heat thermal energy storage systems for space heating and cooling. With a melting point of 35°C, RT-35 was chosen to moderate building temperatures by storing and releasing thermal energy for space heating and cooling. The results indicate that using nanoparticles and adjusting shape can greatly enhance the effectiveness of PCM systems. By incorporating Al₂O₃ nanoparticles, the melting time of the PCM was reduced by 20% compared to the pure PCM, and it is more efficient than the best case of shape modification. These findings indicate that including nanoparticles and modifying the shape are effective methods to improve the performance of heat storage devices. This technology's potential surpasses this study's limits and can be utilized in diverse applications, including solar thermal energy storage, district heating and cooling, and industrial process heat.