International Journal of Energy Research最新文献

The joint dip angle has a significant influence on the mechanical behavior of coal, and revealing its influence mechanism is a scientific premise for analyzing mining-induced mechanical behavior of coal mining. However, the geometric shape of coal joints is complex, and the previous research methods of artificially prefabricated cracks are difficult to accurately reshape the initial structural characteristics of coal. Therefore, the realization of the identification of the in situ occurrence of coal joints and the characterization of the distribution law is the basis for revealing the control effect of joint dip angle on mechanical behavior. Through the combination of CT scanning, three-dimensional reconstruction, rock mechanics test and numerical simulation, the equivalent digital rock mass based on geometric probability distribution model is constructed, and on this basis, the control effect of joint dip angle on the mechanical behavior of coal body is studied. The results show that: (1) The average error of joint dip angle and bulk density between the equivalent digital rock mass and the actual coal sample is 2.26%, the error of uniaxial compressive strength is 14.17%, and the error of elastic modulus is 8.45%. The results are relatively consistent. (2) According to the sensitivity coefficient, the joints with an angle in the range of 45°–60° have the greatest influence on the uniaxial compressive strength. The joint angle in the range of 30°–45° has the greatest influence on the tensile and shear strength. (3) The influence degree of joint dip angle on the strength characteristics of digital rock mass is different. According to the sensitivity coefficient, the influence degree from strong to weak is shear strength, compressive strength, and tensile strength. (4) In terms of failure mode, different angles of joints have different control effects on different forms of fracture modes. Joints with angles of 45°–60° and 75°–90° play a major role in controlling the failure modes of model compression and tensile tests, respectively.

While deep learning-based approaches for state of health (SOH) estimation in lithium-ion batteries have been actively studied, most models face deployment constraints in on-device applications due to their high complexity and large number of parameters. Although previous studies have introduced knowledge distillation (KD) for model compression, single-teacher architectures exhibit limited performance improvement due to insufficient knowledge diversity. To resolve this issue, this study proposes a multi-teacher knowledge distillation (MTKD) framework to simultaneously achieve efficient SOH estimation and model compression. From raw charging data, a total of 18 health indicators (HIs) were obtained from diverse perspectives, including temporal information, statistical features, equivalent circuit model (ECM) parameters, and incremental calculation. Key features were selected through Pearson correlation analysis and the maximal information coefficient (MIC), and were utilized as inputs for the deep learning models. Subsequently, large-scale teacher models based on deep neural network (DNN), long short-term memory (LSTM), and one-dimensional convolution neural network (1D CNN) architectures were trained to capture various degradation characteristics, including nonlinear relationships, temporal dependencies, and local patterns. The lightweight student model was then trained using soft targets obtained from the teacher models along with ground truth labels. Experimental results demonstrate that the student model trained with the proposed MTKD achieved a 45.98% reduction in root mean square error (RMSE) and a 15.72% improvement in coefficient of determination (R²) compared to single-teacher KD (STKD). This study successfully extends KD research beyond traditional computer vision and image processing domains, demonstrating practical applicability in battery data-driven applications.

Underground thermal energy storage (UTES) integrated with building foundations is an emerging pathway to decarbonize space conditioning by shifting low-carbon heat across seasons. This review evaluates copper slag, a high-density, thermally stable byproduct of smelting, as a dual-function medium for thermally active foundations. We synthesize evidence on physicochemical, thermal, mechanical, and environmental performance, emphasizing properties most relevant to foundation-integrated sensible heat storage. Reported specific heat capacities of approximately 0.8–1.5 kJ/kg K combined with densities >3000 kg/m³ yield volumetric energy storage that can exceed typical aquifer-based systems, while cycling studies indicate stable round-trip efficiencies (≈80% over ≥100 cycles) and structural tests show that partial slag substitution in concrete (≈50%) can satisfy strength requirements. A comparative life-cycle perspective suggests that meaningful benefits can be achieved: global warming potential (GWP) reductions of 60%–74% relative to natural-gas baseline systems and 15%–20% embodied-energy savings compared to virgin aggregates, contingent upon design and electricity mix. We also identify the principal constraints to deployment, namely, heavy-metal leaching, thermo-mechanical compatibility under cyclic loads, and the absence of explicit code pathways for foundation-integrated storage, and outline mitigation strategies that span pretreatment, mix design, and containment/barrier engineering. Valorizing an industrial residue in building foundations, copper slag UTES links circular-economy objectives with practical, scalable thermal storage. Targeted research on durability, environmental safety, and standards development is now pivotal for translating this to practice.

Accurately predicting the remaining lifespan of lithium-ion batteries (LIBs) is crucial for manufacturing processes and safe, reliable usage. Battery lifespan prediction continues to face major challenges due to varying degradation processes, fluctuating operating conditions, and differences in electrode materials. Here, we combine commercial battery data charged and discharged under different electrodes and temperature conditions to build a data-driven machine learning model for cycle life prediction. The datasets include three types of commercial cathodes: LiFePO₄ (LFP), LiNi_0.86Co_0.11Al_0.03O₂ (NCA), and LiNi_0.83Co_0.11Mn_0.07O₂ (NCM), which were cycled under various conditions and temperatures. The charging and discharging dataset under a single cathode material, trained using the Elastic Net model, shows that the root mean square error (RMSE) reaches over 1528 cycles under different electrodes. Furthermore, our findings reveal that temperature plays a critical role in predictive accuracy, emphasizing the importance of incorporating cycling conditions into prediction models. With both cathode diversity and temperature effects considered during model training, all RMSE values dropped below 200 cycles. Notably, the mean absolute percentage error (MAPE) for NCA decreased from 64% to 27%. These outcomes highlight a promising approach for developing robust machine learning models capable of accurate battery performance prediction across varied conditions, contributing to safer and more reliable battery technology.

In the global pursuit of clean and sustainable energy, hydrogen production from renewable sources has emerged as a promising solution. This study investigates a green hydrogen production system based on a hybrid renewable energy configuration, comprising photovoltaic panels, wind turbines, and a PEM electrolyzer. The system was simulated using Homer Pro software, based on real climatic data for two cities in Iran, Mashhad and Bandar Abbas. Three different configurations of the renewable system were evaluated to determine the optimal model. The novelty of this research lies in providing a comprehensive analytical framework for the simultaneous techno-economic and environmental assessment of green hydrogen production at a daily refueling scale of 24 vehicles. The results indicate that both cities possess significant potential for hydrogen production, while the performance in Bandar Abbas is superior, with a levelized cost of hydrogen (LCOH) of $7.14/kg and a levelized cost of electricity (LCOE) of $0.1424/kWh. Moreover, this system results in an approximate reduction of 9180 tons of CO₂ over the project lifetime. The findings demonstrate that hybrid renewable systems can play a significant role in reducing greenhouse gas emissions and dependence on fossil fuels in the transportation sector.

Hydrogen-driven zero-carbon energy system is one of the effective paths to realize the construction of zero-carbon parks. However, the optimal scheduling of hydrogen-driven integrated energy system (IES) faces significant challenges, including the intermittency of renewable energy, the dynamic characteristics of hydrogen production and storage, and the complex interactions among multiple energy carriers. In this paper, we construct a hydrogen-driven park’s zero-carbon IES in which wind power, photovoltaic power, and hydrogen are considered as energy sources. To achieve the goals of economic and zero carbon, a supply–demand collaborative two-stage robust optimization model is constructed in which heterogeneous energy storage, demand response, and uncertainties are considered. The C&CG algorithm is presented to solve the supply–demand collaborative robust optimization model. The results show that the integration of multienergy storage systems can reduce energy cost by 78.95% and the collaboration between energy supply and demand can reduce energy cost by 47.07%. Moreover, multiple energy storage methods can significantly increase the flexibility to realize zero carbon emission.

Wind power’s intermittent nature presents challenges for grid integration, while hydrogen production via electrolysis offers potential solutions for both energy storage and clean transportation fuel. This study conducted a comprehensive technoeconomic assessment of integrated wind-powered electrolysis systems with compressed hydrogen storage to determine optimal configurations and economic viability. Five system configurations were modeled, combining direct wind-to-electrolysis coupling and grid-connected operation with varying storage capacities. Technical performance was simulated using actual wind data from three geographic locations, while economic analysis employed discounted cash flow methodology, examining multiple revenue streams. Direct-coupled systems achieved 62.4% average efficiency from wind to hydrogen, while grid-connected systems reached 68.7%. The hybrid configuration demonstrated superior economic performance, achieving levelized hydrogen costs as low as $3.39/kg in favorable locations. Grid balancing services reduced production costs by 13.8% for hybrid systems. Carbon abatement costs ranged from $46.3 to 142.7/ton CO₂eq without incentives, decreasing to $5.8–58.2/ton with enhanced policy support. The results indicate that wind-powered electrolysis systems can achieve economic viability in specific markets when implementing revenue stacking strategies. Geographic location significantly impacts performance, with the wind capacity factor being more influential than peak wind speeds. Policy incentives remain critical for near-term deployment, though projected cost reductions suggest competitive hydrogen production without subsidies is achievable by 2030.

Vehicle to home (V2H) uses bidirectional charging to transfer the energy stored in electric vehicle (EV) batteries for household electricity usage. The usage of energy from EV batteries can lower energy bills by tapping the EV battery during high-cost periods and recharging during low-cost periods. In this work, a V2H optimization model is used to minimize home energy costs considering household electrical and heating demand, EV usage, and generation from rooftop solar under different electricity tariff structures, namely a static three-tier (day/night/peak) and the dynamic tariff structures. The work uses the optimization model to generate 100 representative residential demand profiles assuming V2H usage, which are then used to obtain total aggregate system residential demand assuming widespread use of V2H. The impact of widespread use of V2H on system-level demand profiles under different tariff structures is thus investigated for a case study using data for Ireland. It is shown that the adoption of V2H can give rise to new peaks in residential demand by aligning all charging with hours when electricity costs are low. To mitigate these peaks and flatten the load, nighttime charging constraints can be introduced. Charging constraints that reduce the charging power to 30% of maximum and restrict the minimum EV battery state of charge (SOC) to 50% are shown to be effective in reducing the peak loads by 50%. The impact of adoption of V2H on the availability of up and down flexibility from EV charging is also investigated. It is shown that the use of V2H restricts the available flexibility with down flexibility in particular being largely restricted to nighttime hours. However, the introduction of the load flattening charging constraints results in a better distribution of flexibility over nighttime hours.

This study presents a sustainable approach for mitigating greenhouse gas emissions by converting methane (CH₄) and carbon dioxide (CO₂) into syngas using nickel-based catalysts supported on hexagonal boron nitride (hBN), synthesized through chemical vapor deposition (CVD). Characterization of the 4 wt% Ni/hBN catalyst, conducted using high-resolution techniques such as X-ray photoelectron spectroscopy (XPS) and transmission electron microscopy (TEM), revealed quasi-spherical nickel particles with an average diameter of 37 nm and a calculated dispersion of 10.5%. At 700°C, this catalyst achieved conversions of 78% for CH₄ and 80% for CO₂, outperforming the 12 wt% Ni/γ-Al₂O₃ (FCR-4) catalyst by about 20%. It also displayed excellent coke resistance, with a carbon deposition rate of 2.5 mg C/(g_cat h), half that of FCR-4, and a noteworthy 30% reduction in activation energy, from 21.4 to 15.0 kJ/mol. The hydrogen yield reached 74%, a 37% increase over FCR-4, with an H₂/CO ratio of 0.96, indicating its suitability for Fischer–Tropsch processes. Furthermore, a Fe-Ni/hBN catalyst was developed through selective deposition of 1% Fe onto the Ni/hBN support by establishing a temperature window of 140–200°C, determined by gas chromatography (GC) and confirmed by high-resolution transmission electron microscopy (HRTEM). This catalyst variant demonstrated minimal coke formation at 600°C, achieving CH₄ and CO₂ conversions of 44% and 49%, respectively, comparable to FCR-4, while maintaining superior stability against alternative catalysts. Overall, the low acidity and high thermal stability of hBN, along with CVD control over particle size and dispersion, highlight its potential for efficient dry reforming of methane (DRM) under optimized conditions.

Accurate state of charge (SOC) estimation remains a challenge in lithium-ion battery management systems (BMSs) due to the cells’ complex, nonlinear internal dynamics, and their high sensitivity to operating conditions. While most existing data-driven studies rely on a core input configuration of voltage (V), current (I), and temperature (T), these models often struggle with temperature-induced variations, which typically require complex architectures. This study addresses the challenge of thermal dependency through an innovative feature engineering approach designed to maintain a low computational cost suitable for real-time applications. Recognizing that internal resistance (R) inherently reflects both the cell’s temperature and aging state, we propose replacing T with R as the third input feature, resulting in an [I, V, R] configuration. We systematically compared the performance of four estimation models: DE-long short-term memory (LSTM), DE-gated recurrent unit (GRU), LSTM-unscented Kalman filter (UKF), and GRU-UKF, using both the traditional [I, V, T] and the proposed [I, V, R] input sets. The results validate the superiority of the R-based configuration, which led to significant reductions in mean absolute error (MAE) by 46.02%, 43.31%, 14.02%, and 35.85%, and in root mean square error (RMSE) by 48.95%, 39.87%, 21.90%, and 23.95% for the DE-LSTM, DE-GRU, LSTM-UKF, and GRU-UKF models, respectively. This confirms that substituting T with R captures nonlinear temperature and aging effects more effectively while maintaining low computational complexity.