Energy Storage最新文献

Phase change materials (PCMs) have been incorporated into textiles to provide thermoregulation and temperature buffering effects on the human body. From this point of view, the aim of this study was to develop the phase change material (PCM) incorporated into the yarns for the production of textiles with a thermo-regulating function. In the study, two types of capsules poly(methyl methacrylate-co-methacrylic acid) (P(MMA-co-MAA)) walled and 1-tetradecanol core, and gelatin-gum Arabic walled and n-octadecane core were synthesized and applied to cotton textile fibers using an alternative application method developed by the authors. PCM dispersion with 6% concentration was incorporated into cotton ring spun yarns at 62.5 and 80 mL/h feeding rates. Morphological and thermal properties of the capsules and spun yarns were investigated. Thermoregulation properties of fabricated yarns were detailed evaluated by segmenting thermal history (T-history) curves into four phases and logarithmic and linear trendlines were applied to the temperature change data for unloaded and PCM incorporated yarns. Data including temperature range (°C), R² (coefficient of determination or regression factor), rate coefficient (a) and duration of phase (s) were analyzed for both capsule types and feeding rate values. The results indicated that PCM capsules with ideal spherical morphology and enough energy storage capacity were successfully applied into the cotton fibers. All cotton yarns with PCM additives exhibited lower surface temperature values greater than 2°C which is considered sufficient for the thermoregulation effect although with some distinct variations in their temperature profiles and rate coefficients. Compared to untreated cotton ring spun yarn, the temperature difference for 1-tetradecanol core@P(MMA-co-MAA) walled capsules was found to be around 4.29°C–4.56°C, whereas it was around 8.2°C–9°C for n-octadecane core@gelatin-gum Arabic walled capsules. With respect to all the results, obtained novel heat storage cotton yarn is a promising material for thermal energy storage and desirable thermal comfort applications.

The prevention of uncontrollable growth of zinc dendrites on the zinc electrodes is one of the key challenges, hindering the widespread commercialization of zinc-based energy storage technologies. Herein, we report a facile method to mitigate dendrite growth on a copper surface by an initial in situ coating of a Cu–Zn alloy layer, before zinc deposition. During further zinc deposition, the initially formed Cu–Zn alloy layer provides uniform nucleating sites and promotes homogeneous zinc deposition. A symmetrical cell, assembled using a Cu–Zn/Cu electrode could be stably cycled for over 1000 cycles in ZnSO₄ solution, at a current density of 30 mA/cm² and a coulombic efficiency of 99.9%. A zinc–air cell, assembled using Zn@Cu–Zn/Cu as the anode and rGO/Co₃O₄ composite as the cathode, exhibited a very stable performance at a high current density of 50 mA/cm² and coulombic efficiency of ~93%, for over 400 cycles. After cycling experiments, the X-ray photoelectron spectroscopy and the X-ray diffraction analysis confirmed the formation of the Cu–Zn alloy layer. Hence, the present method provides an easy route for fabricating a dendrite-free zinc electrode, for a wide range of zinc anode-based batteries.

In this paper, quasi-solid electrolytes (QSEs) “PEO + xwt.% BMIMPF₆” for x = 0–20 were prepared by the immobilization of ionic liquid (IL), 1-butyl-3-methylimidazolium hexafluorophosphate (BMIMPF₆) to the PEO polymer matrix by solution casting technique. These quasi-solid electrolytes (QSEs) are in the thin film form of good mechanical integrity. The QSEs are characterized by X-ray diffraction (XRD), Attenuated total reflectance Infrared (ATR-IR) spectroscopy, differential scanning calorimetry (DSC)/thermogravimetric analysis (TGA), field emission scanning electron microscopy (FESEM), impedance spectroscopy, and electrochemical techniques. XRD/DSC results confirm an increase in the flexibility (and hence polymer chain mobility) with the increasing amount of IL in the QSEs, as confirmed by the analysis of degree of crystallinity (X_c). The maximum room temperature ionic conductivity ~1.32 × 10⁻⁵ S. cm⁻¹ is obtained for the 20 wt.% IL (BMIMPF₆) added QSEs. The interaction/complexation between the dopant IL-cation BMIM⁺ with the ether oxygen (i.e., COC bond of PEO) has been confirmed by FTIR spectroscopic analysis. FESEM results confirm the appearance of crystalline spherical grains (spherulites), whose size decreases with the increasing amount of IL in the membranes and shows overall semicrystalline microstructures. The TGA analysis confirmed that the onset decomposition temperature of the QSEs is found to be ~175°C, which is the sufficient temperature range of operation for the solid-state electrochemical devices. The electrochemical performances of the QSEs were examined by fabricating the symmetrical electrical double-layer capacitor (EDLC) device. The fabricated EDLC cell with optimized QSE “PEO + 20 wt.% BMIMPF₆” with biomass-based honeycomb activated carbon (HCAC) electrodes offers specific energy ~5.8 Wh kg⁻¹ at power density ~ 79.9 W kg⁻¹. It also displays excellent cycling stability with 81.3% of the initial specific capacitance after 2500 charge–discharge cycles.

Accurate estimation of state of health (SoH) of the battery over long-term is a critical challenge for the battery management systems in electric vehicles. This is due to the challenges in accurately modeling the accelerated aging and degradation phenomena caused by diverse operating conditions of the battery. This paper presents a cascaded recurrent neural networks (RNN) with long short-term memory (LSTM) to estimate the internal resistance and SoH, taking account of various abnormal operating conditions of the battery. A datasheet-based degradation model of the battery is developed using fade equations. The training and validation data set for LSTM-RNN are generated by subjecting the battery model to various factors that cause accelerated degradation, such as fast charging, varying operating temperatures, overutilization, and cell imbalance. The cascaded LSTM-RNN is trained to estimate SoH only once after the completion of every charge–discharge cycle. The training error index parameters of the proposed SoH estimator are well within 1%, demonstrating the reliability and robustness of the estimator to diverse operating conditions of the battery.

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.