Energy Storage最新文献

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.

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.