Energy Storage最新文献

In deregulated power systems, maintaining stable load frequency control (LFC) is increasingly challenging due to the integration of diverse energy sources and the unpredictable nature of demand and generation. Traditional control strategies often struggle to provide robust performance under these dynamic conditions, especially when contract violations occur. Additionally, ensuring frequency stability across multi-area grids with mixed energy profiles especially integration of hybrid energy storage systems, requires advanced and adaptive control mechanisms. This paper proposes a novel application of the Slime Mold Algorithm (SMA) to optimize a novel TIIDN-P controller under a deregulated environment. This paper also proposes a novel three-area hybrid power system. Each area comprises a thermal power plant integrated with a renewable energy source and a dedicated energy storage system (ESS): Area 1 includes a wind turbine, Area 2 features geothermal energy, and Area 3 incorporates biogas. The proposed system is chosen since it has not yet been investigated and those energy sources are easily available in India. By leveraging the adaptive search capabilities of SMA, the proposed TIIDN-P control strategy achieves significantly improved frequency regulation performance compared to other controllers. In comparison to PSO (Particle Swarm Optimization), FA (Firefly Algorithm), GWO (Grey Wolf Optimizer) and MBA (Mine Blast Algorithm), the proposed algorithm reduced settling time significantly up to 40% for different areas' frequency. Simulation results confirm that TIIDN-P controllers offer superior dynamic response, reduced overshoot, and enhanced robustness under varying system conditions. The integration of hybrid ESS configurations not only stabilizes system frequency but also effectively mitigates the impact of uncontracted power fluctuations. Sensitivity analysis further validates the resilience of the proposed approach. The sensitivity analysis proved that the proposed system as well as the controller, is robust enough in various cases. The stability analysis using eigenvalue analysis revealed that the proposed system is stable in various conditions. Moreover, this paper presents an intensive comparison of various types of Energy Storage Systems and their hybrid systems to find out the best ESS and the best hybrid system which is the first-ever of its kind. An investigation reveals that RFBESS and FWESS hybrids are the best option from the case study.

The most prominent transition has all to do with the electric vehicles within the evolving technological environment facing rapid advancement today. Effective thermal performance of an electric vehicle battery pack is of utmost importance, in terms of both safety and performance, longevity. In this review a context of conductive composite materials in battery heat exchange systems (BHES) is explored, thus giving indication into the importance of architecture in optimizing heat transfer within EV battery modules. The ultimate aim is to present up-to-date developments in material science that will improve the thermal performance of battery thermal management systems (BTMS), achieve uniform heat distribution, and increase battery efficiency. In this regard, future aspects' emphasis is put on porous foam composites incorporating phase change materials (PCM), which are highly promising in improving thermal regulation under variable operational conditions. This study is something different from previous reviews because this article particularly highlights conductive composite phase change materials in thermal regulation for EV battery packs and recent advances. The review will also give a comparative account of conventional and advanced cooling methods, the challenges of which mainly lie in material integration and costs and will point toward possible futures of research and development in thermal management strategies for EV batteries.

Charging strategy optimization for lithium-ion batteries is crucial to improve the efficiency of new energy devices. In this study, three conventional methods are compared: constant-current constant-voltage (CCCV) charging is the most efficient but slowest, pulse charging (PC) is the fastest but least efficient, and multistage constant-current (MSCC) is a compromise between speed and efficiency. To this end, we propose a dynamic optimal charging strategy based on model predictive control (MPC) that balances rapid-charging speed with battery safety. By integrating a low-order electrochemical–thermal–aging coupled model with real-time state estimation provided by an extended Kalman filter (EKF), a rolling-horizon framework is established to track both state-of-charge (SOC) and temperature reference trajectories. Experiments show that EKF has stronger initial error robustness (maximum deviation < 2%) than unscented Kalman filter (UKF) and unscented Kalman Bucy filter (UKBF), which provides reliable feedback for MPC. The new strategy achieves an optimal balance between charging efficiency and safety by dynamically adjusting the charging profile and significantly improves the charging speed under closed-loop control compared to the CCCV method, while controlling the temperature rise within 5°C.

The rapid growth of electric vehicle (EV) fleets in Italy is intensifying pressure on the national grid and raising concerns about infrastructure readiness, economic feasibility, and regulatory fragmentation. Energy storage systems (ESS) are central to addressing these challenges, yet their role in fleet electrification remains underexplored in the Italian context. This study investigates the technological, economic, and policy dimensions of energy storage innovations that can support Italy's transition toward sustainable fleet electrification in alignment with EU climate goals. A mixed-methods approach was employed, combining policy and regulatory analysis, 18 expert interviews with fleet operators, energy providers, and policymakers, and a Python-based simulation of EV fleet charging to evaluate technical performance, economic viability, and future deployment scenarios validated by a Delphi panel of 10 experts. The findings reveal that high upfront costs, limited charging infrastructure, and regional policy disparities are the primary barriers to ESS adoption. Simulation results indicate that lithium-ion batteries can reliably meet daily fleet energy demand but are constrained by degradation and grid strain under concentrated charging schedules. Scenario analysis shows that under optimistic conditions, including rapid technology development and expanded infrastructure, fleet-related emissions could be reduced by up to 40% by 2030. Overall, the study underscores that energy storage is a cornerstone of Italy's EV fleet transition and highlights the need for coordinated policy alignment, investment in advanced storage technologies, and expansion of vehicle-to-grid (V2G) services to unlock economic and environmental benefits while positioning Italy as a leader in sustainable mobility.

The automotive industry's transition to green mobility has increased the focus on electric vehicles (EVs) due to their low emissions and reduced reliance on conventional carbon-based fuels. To support wider EV adoption, enhancing the efficiency and performance of their energy storage systems—particularly, by increasing power and energy density—is essential. Although various advanced energy storage systems (ESSs) are available, batteries remain the most viable option for meeting the average power demands of EVs. However, challenges such as excessive heat generation, limited power density, and reduced operational lifespan hinder optimal performance when relying solely on batteries. A hybrid energy storage system (HESS) presents a promising solution by enabling more efficient power management. Key functions of HESSs include supporting vehicle acceleration, capturing energy during regenerative braking, and reducing stress on the battery. By integrating a battery with a supercapacitor (SC), HESSs can meet instantaneous power demands and facilitate energy recovery during deceleration. This study evaluates the performance of a standalone battery and an HESS by using MATLAB–Simulink simulations. The results show that the SC absorbs most peak current loads over short durations, while the battery supplies the average current. The HESS reduces average battery current demand and extends vehicle range by 38%. Additionally, hybridization allows for a reduction in battery power capacity, resulting in a 33 kg weight decrease. These findings demonstrate the potential of HESS to enhance EV performance, promote energy efficiency, and support the broader adoption of sustainable transportation.

To address the challenge of directly and accurately measuring the internal temperature of lithium-ion power batteries in electric vehicles, this paper proposes an online precise estimation method for battery internal temperature based on the Aquila Optimizer-optimized Adaptive Strong Tracking Extended Kalman Filter (AO-ASTEKF). Building on a battery equivalent thermal model with parameters identified using the Genetic Algorithm (GA), the Aquila Optimizer (AO) is employed to optimize the initial noise covariance settings of the traditional Extended Kalman Filter (EKF), thereby mitigating the impact of improper initialization. To resolve the estimation deviation caused by fixed noise covariance in EKF, the Sage-Husa adaptive filtering technique is introduced to enable adaptive adjustment of noise covariance values. Furthermore, to counteract the estimation accuracy degradation of the filter due to sudden temperature changes in high-temperature environments, the Strong Tracking (ST) filter is incorporated to enhance the tracking capability of the EKF. Through co-simulation in AMESim and MATLAB/Simulink, the accuracy of the proposed AO-ASTEKF algorithm in estimating battery internal temperature is validated under different ambient temperatures and operating conditions. Experimental results demonstrate that the AO-ASTEKF algorithm improves estimation accuracy by at least 58.46% compared to both the traditional EKF and the Strong Tracking Extended Kalman Filter (STEKF). This method effectively overcomes the limitations of conventional algorithms in accurately estimating battery internal temperature, holding significant importance for ensuring battery safety and enhancing battery performance.

This study numerically investigates the thermal performance enhancement of a PCM-filled annular thermal energy storage (TES) system using rotational walls, considering both the solidification and melting processes. Various rotational configurations are explored. These configurations are separate rotation of the inner and outer walls, simultaneous rotation of both walls with different angular velocities, and rotations in both clockwise (CW) and counterclockwise (CCW) directions. The results show that the rotation of the walls induces forced convection which enhances heat transfer and accelerates PCM melting. The molten layers in proximity to the rotating walls show increased velocity causing improved flow effects. Higher rotational speeds result in stronger fluid flow and enhanced convection within the PCM enclosure, resulting in a larger volumetric fraction of PCM undergoing the melting process. Furthermore, the rotation of the walls promotes a more uniform distribution of heat and a homogeneous distribution of the molten PCM throughout the system. Simultaneously rotating both the inner and outer walls of the PCM enclosure reduces the total melting time which increases the overall efficiency of the TES system. Regarding the solidification process, the rotation of the PCM enclosure walls accelerates the typically slow solidification stage. Increasing the angular velocities causes a higher solidification rate because of enhanced mixing within the system. At the highest angular velocity, rotating the outer wall alone, reduces melting time by 49.06%, while rotating both walls (inner CW–outer CCW) leads to a maximum reduction of 62.15%. In solidification, outer wall rotation decreases the total solidification time by 55.91%, and rotating both walls (CW–CW) achieves up to 56.53% reduction. Overall, the findings of this study show the significant thermal performance enhancement achieved through the rotation of walls in PCM-filled annular TES systems.

Efficient, safe, and compact solid-state materials are critical for overcoming hydrogen storage challenges. This study introduces a novel class of materials, the hexagonal Zintl-phase hydrides SnMSiH (M = Al, Ga), and establishes their exceptional potential through first-principles density functional theory (DFT) calculations. The key superiority of these materials lies in their unique semimetallic electronic structure, which significantly enhances hydrogen interactions by reducing the activation energy for desorption, enabling efficient and reversible cycling—a critical improvement over insulating or wide-bandgap hydrides. Structurally, the primitive hexagonal framework (space group P3m1) provides optimal diffusion pathways for hydrogen. We report a high gravimetric capacity of 0.58 wt% for SnAlSiH with a near-ambient desorption temperature of 310.69 K, markedly superior to many complex hydrides. SnGaSiH offers a capacity of 0.47 wt% at an even lower desorption temperature of 254.15 K, indicating easy hydrogen release. Thermodynamically, both compounds exhibit significant thermal expansion and high heat capacities, ensuring resilience at operating temperatures. Mechanically, they are highly anisotropic; SnAlSiH's higher compressibility may facilitate volume changes during cycling, while SnGaSiH demonstrates superior mechanical stability (higher elastic constants). This combination of favorable desorption thermodynamics, intrinsic structural stability, and robust mechanical properties distinguishes SnMSiH hydrides as premier candidates for application. This work provides a foundational strategy for further performance enhancement through alloying and defect engineering.

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.