Search Results (28,599)

Direct current fast charging is a necessary element of the transition to electric vehicles (EVs). Regulatory complexity, capital requirements, and challenging business models hinder charging infrastructure deployment, so focusing on the efficient use of such infrastructure is of paramount importance. A tool to improve this efficiency is an incentive to terminate charging events when charging power drops, the vehicle state of charge rises above some value, or time plugged in exceeds a threshold. A timeseries charging demand model was built based on observed EV population and charging behavior. This was used to explore these three incentive trigger metrics across a range of plausible values, to find their relative impacts on the vehicles charging, those waiting in line to access a cordset, and charging site operators. Results indicate that basing such a trigger on charging power would have little impact if the threshold power is low enough to accommodate older, slower-charging vehicles, but that more restrictive limits based on state of charge or charging duration can decrease wait times, increase vehicle throughput, and increase total energy sales for cordsets serving more than 1000 EVs per year. Full article

The electrification of public transport with battery electric buses (BEBs) poses technical, regulatory, and environmental challenges. This paper analyzes the impact of BEB charging on a Brazilian urban medium-voltage (MV) feeder using a novel methodology to convert utility GIS data into OpenDSS simulation models. The study utilizes Geographic Database of the Distribution Company (BDGD) data from the Brazilian Electricity Regulatory Agency (ANEEL) and OpenDSS simulations. Motivated by Cuiabá’s proposal to electrify its public bus fleet, four realistic scenarios were simulated, incorporating distributed photovoltaic (PV) generation and vehicle-to-grid (V2G) operation. Results show that up to 118 BEBs can be charged simultaneously without voltage violations. However, thermal overload occurs beyond 56 units, requiring conductor upgrades or load redistribution. PV systems can supply up to 64% of the daily energy demand but introduce reverse power flows and overvoltages, indicating the need for dynamic control. V2G operation enables peak shaving but also leads to overvoltages when more than 33 buses inject power concurrently. The findings suggest that while the current infrastructure partially supports fleet electrification, future scalability depends on integrating smart grid features and reinforcing the system. Although focused on Cuiabá, the methodology offers a replicable approach for low-carbon urban mobility planning in similar developing regions. Full article

Electricity is currently essential for the operation of most modern devices, with significant electrification being observed in all areas. This development has led to an increased demand for solutions that enable energy storage appropriate for a given application, which is currently solved by installing batteries with adequate capacity. This article presents an approach utilizing composite materials that combine both structural and energy storage features. The most frequently discussed components of such materials in the literature are compared, divided into those that contribute to the structural functions of the composite and those that provide additional functionality. The methodology for developing our literature analysis and for comparing materials is given. The results of our publication analysis are then presented, based on the type of integration of multifunctional elements, structural materials, resins, electrolytes, and production methods. The influence of these parameters on the mechanical and electrochemical properties of multifunctional composites is examined. The different materials are compared, and the best ones selected based on appropriate criteria. The current state of knowledge regarding simulations of such materials is presented, and the potential applications of multifunctional composites are reviewed. Finally, key research gaps are identified, suggesting directions for future work. Full article

In the aviation industry, momentum for reducing emissions has rapidly increased in recent years. From international systems like the EU ETS and CORSIA, to the introduction of new fuels such as electricity and SAF as alternatives to conventional fuels, various approaches are being considered. Within this context, there is a further movement to reduce aviation emissions through a modal shift from air to high-speed rail. In this research, a Systematic Literature Review is undertaken to detail the nature of the modal shift from air to rail, uncovering energy policy and economic considerations. While research targeting China has increased recently, prior studies focus on Europe, leaving some regions understudied. From an emissions reduction perspective, the power source supplying rail is a critical factor. Capacity constraints on rail are also a key challenge to be addressed. Future research should address the need for additional regional studies. In the age of modal shift movements, the aviation industry is attempting to reduce emissions through the introduction of alternative low-carbon fuels. Policies to reduce emissions must consider this. Discontinuing flights could lead to unintended emissions. A synergistic approach combining modal shift and internal decarbonization is likely to be the most economically feasible and sustainable approach. Full article

Computed tomography (CT) imaging has proven to be effective for detecting and visualizing a wide range of inhomogeneities and defects. Applying computer vision (CV)-based image processing enables detailed feature measurements on selected CT image slices, which could be of benefit as cells of the same type often show variations in electrical properties. When combined with electrical testing, CT imaging could provide valuable insights into the battery cell, helping to identify potential sources of electrical deviations. However, it remains unclear to what extent CT-based measurements, especially for larger prismatic cells, e.g., those used in automotive applications, can explain electrical deviations aside from identifying significant or latent defects. Therefore, this study performs a correlative analysis and compares the electrical measurement results with CT-based measurements of the cell’s physical features, specifically the anode and cathode sizes. Electrical and CT measurements from ten lithium iron phosphate/graphite (LFP/C) cells of the same type are analyzed. The results indicate that while CT imaging has the potential to help identify the sources of electrical deviations, it also shows that cell-level CT measurements alone cannot fully explain electrical performance deviations. Measurement uncertainty, the potential overlapping impact of other cell features, and the actual influence of the measured physical properties on the cell’s electrical performance limit the correlation between CT-based measurements and electrical parameters. Full article

Tourism is a fast-growing sector that generates a significant greenhouse gas (GHG) footprint, yet subnational data needed to measure the sector remain scarce. Quantifying tourism-related emissions is essential for effective climate policy and alignment with international targets. This study contributes to quantifying tourism sector GHG emissions using the 2023 Korean National Travel Survey data and a spend-based environmentally extended input–output (EEIO) model. Expenditure data were mapped onto the 33-sector multiregional EEIO framework, estimating a total of 2623 tCO_2eq emissions by region, expenditure type, and industry sector in 2023, where about 73% of the total was attributed to tourism-related sectors with the sample data, 24,282. The results illustrate how tourism emissions are shaped especially by transportation systems and regional context. Provinces that surround metropolitan cities in the mainland, for example, Gyeonggi and Gangwon Provinces near Seoul and Incheon, and Gyeongnam Province neighboring Busan and Ulsan, record higher emissions due to large travel volumes from these metropolitan cities and energy-intensive transportation services. Jeju Island stands out as an outlier, with disproportionately high emissions relative to its size, driven by reliance on aviation, which significantly raises its per-visitor footprint. Sectoral analysis identified transportation services, agriculture, electricity, and gas as key sectors. By providing detailed provincial-level data, this study offers a first empirical foundation to corporate Category 6 of Scope 3 reporting and supports central and local governments in designing region-specific climate strategies associated with tourism-related sectors. Full article

Facing the significant challenges posed by global population growth and urbanization, plant factories, as an efficient closed cultivation system capable of precise environmental control, have become a key direction in the development of modern agriculture. However, high energy consumption, particularly lighting (which accounts for over 50%), remains a major bottleneck limiting their large-scale application. This study systematically explored the effects of dynamic light regulation strategies on lettuce (Lactuca sativa L.) growth, physiological and biochemical indicators (such as chlorophyll, photosynthetic, and fluorescence parameters), nutritional quality, energy utilization efficiency, and post-harvest shelf life. Four different light treatments were designed: a stepwise increasing photosynthetic photon flux density (PPFD) from 160 to 340 μmol·m⁻²·s⁻¹ (T1), a constant light intensity of 250 μmol·m⁻²·s⁻¹ (T2), a three-stage strategy with high light intensity in the middle phase (T3), and a three-stage strategy with sequentially increasing light (T4). The results showed that the T4 treatment exhibited the best overall performance. Compared with the T2 treatment, the T4 treatment increased biomass by 23.4%, significantly improved the net photosynthetic rate by 50.32% at the final measurement, and increased ascorbic acid (AsA) and protein content by 33.36% and 33.19%, respectively. Additionally, this treatment showed the highest energy use efficiency. On the 30th day of treatment, the light energy use efficiency (LUE) and electrical energy use efficiency (EUE) of the T4 treatment were significantly increased, by 23.41% and 23.9%, respectively, compared with the T2 treatment. In summary, dynamic light regulation can synergistically improve crop yield, chlorophyll content, photosynthetic efficiency, nutritional quality, and energy utilization efficiency, providing a theoretical basis and solution for precise light regulation and energy consumption reduction in plant factories. Full article

Accurate state of charge (SoC) estimation is critical for the safety, performance, and longevity of lithium-ion batteries in electric vehicles and energy storage systems. This study investigates the application of Electrochemical Impedance Spectroscopy (EIS) data in conjunction with tree-based ensemble machine learning algorithms—Random Forest, Extra Trees, Gradient Boosting, XGBoost, and AdaBoost—for precise SoC prediction. A real dataset comprising multi-frequency EIS measurements was used to train and evaluate the models. The models’ performances were assessed using Mean Squared Error (MSE), Root Mean Squared Error (RMSE), and the coefficient of determination (R²). The results show that Extra Trees achieved the best accuracy (MSE = 1.76, RMSE = 1.33, R² = 0.9977), followed closely by Random Forest, Gradient Boosting, and XGBoost, all maintaining RMSE values below 1.6% SoC. Predictions from these models closely matched the ideal 1:1 relationship, with tightly clustered error distributions indicating minimal bias. AdaBoost returned a higher RMSE (3.06% SoC) and a broader error spread. These findings demonstrate that tree-based ensemble models, particularly Extra Trees and Random Forest, offer robust, high-accuracy solutions for EIS-based SoC estimation, making them promising candidates for integration into advanced battery management systems. Full article

The integration of photovoltaic systems with heat pumps can significantly influence primary energy consumption indicators and therefore plays a particularly important role in the low-energy construction sector. This study provides a simulation-based assessment of the impact of selected photovoltaic panel parameters on the primary energy (PE) index in a low-energy building equipped with an air-source heat pump. The building, located in the relatively cold climate of north-eastern Poland, was analyzed in two insulation variants of the building envelope. In each variant and system configuration, the total amount of energy produced by the panels (E_PV) and used by the system (E_used), as well as the degree to which the system’s electricity demand was covered by the photovoltaic panels (η_cov) and their self-consumption degree (η_self), were assessed. The results showed that, in the baseline scenarios, photovoltaic panels were able to generate 5586 kWh of electricity, covering an average of 60–63% of the system’s demand and achieving a self-consumption of approximately 59%. The E_PV, E_used, and η_cov are inversely proportional to the η_self and PE index. The PE index, η_cov, and η_self ranged from 22.6 to 80 kWh/m², 25.3 to 77.5%, and 23.9 to 100%, respectively, depending on the variant and configuration. The wide range of the obtained results confirms that the analyzed factors have a significant impact on the performance of building-integrated photovoltaic panels. In addition, the use of ASHP and PV instead of a gas boiler and grid electricity reduced both the EP index and CO₂ emissions by 59–67%. Full article

Compared with pure pumped storage, hybrid pumped storage plants (HPSPs) face more complex challenges in electricity markets, such as multi-time-scale decision-making and coupled market mechanisms. Existing mid- to long-term curve decomposition strategies often lead to deviations from actual spot prices and compressed bidding space, limiting profitability and sustainable development. To address this, this study introduces Contracts for Difference (CfDs) to enhance revenue and operational flexibility. A bi-level optimization model is developed for joint participation in spot and frequency regulation markets under CfDs: the upper level maximizes HPSP revenue through capacity allocation and bidding, while the lower level maximizes social welfare via joint energy and ancillary service market clearing. The model is solved using a commercial solver and NSGA-II. Simulations show that CfDs increase spot market revenue by 33.2% and improve bidding alignment with price fluctuations, demonstrating strong market adaptability. Full article

This study introduces a health-aware fuzzy logic (FL) energy management strategy (EMS) for fuel cell electric commercial vehicles (FCECVs) that aimed to improve energy efficiency and extending fuel cell system (FCS) lifespan. The FL-based EMS was developed using vehicle power demand and battery state of charge (SOC) as inputs, with the FCS power change rate as the output, aiming to mitigate degradation induced by abrupt load transitions. A multi-objective optimization framework was established to optimize the fuzzy logic controller (FLC) parameters, achieving a balanced trade-off between fuel economy and FCS longevity. The non-dominated sorting genetic algorithm-II (NSGA-II) was utilized for optimization across various driving cycles, with average Pareto-optimal solutions employed for real-time application. Performance evaluation under standard and stochastic driving cycles benchmarked the proposed strategy against dynamic programming (DP), charge-depletion charge-sustaining (CD-CS), conventional FL strategies, and a non-optimized baseline. Results demonstrated an approximately $38\%$ reduction in hydrogen consumption (HC) relative to CD-CS and over $75\%$ improvement in degradation mitigation, with performance superior to that of DP. Although the strategy exhibits an average $17.39\%$ increase in computation time compared to CD-CS, the average single-step computation time is only $2.1$ ms, confirming its practical feasibility for real-time applications. Full article

The energy consumption imbalance among electric vehicles (EVs) within a fixed platoon primarily originates from the distinct aerodynamic drag forces at different positions. This imbalance further causes practical challenges, such as inconsistent battery degradation rates and divergent charging durations. To tackle these challenges, dynamically adjusting the platoon formations during the journey is essential, which requires identifying the optimal vehicle sequences at designated re-sequencing points. In this research, we formulate the Optimal Re-Sequencing (ORS) problem as a multi-objective optimization problem that minimizes the imbalance of energy consumption, ensures a minimum remaining state of charge (SOC) for energy security, and penalizes excessive formation changes to maintain stability. To solve this optimization problem, we propose a deep reinforcement learning (DRL) framework based on Bootstrapped Deep Q-Networks. This framework integrates multi-head Q-value estimation and prioritized experience replay mechanisms to improve exploration efficiency and learning stability. Through simulation experiments based on a modeled Suzhou–Nanjing route, our proposed approach achieves a final SOC standard deviation of 0.00524, representing a 47% reduction compared with existing works, demonstrating superior efficiency and effectiveness in achieving fair energy consumption across EV platoons. Full article

This study proposes a novel framework for analyzing Vehicle-Integrated Photovoltaic (VIPV) systems, integrating optical, thermal, and electrical models. The model modifies existing fixed PV methodologies for VIPV applications to assess received irradiance, PV module temperature, and energy production, and is available as an open-source MATLAB tool (VIPVLIB) enabling simulations via a smartphone. A key innovation is the integration of meteorological data and real-time driving, dynamically updating vehicle position and orientation every second. Different time resolutions were explored to balance accuracy and computational efficiency for optical model, while the thermal model, enhanced by vehicle speed, wind effects, and thermal inertia, improved temperature and power predictions. Validation on a minibus operating within the University of Palermo campus confirmed the applicability of the proposed framework. The roof received 45–47% of total annual irradiation, and the total yearly energy yield reached about 4.3 MWh/Year for crystalline-silicon, 3.7 MWh/Year for CdTe, and 3.1 MWh/Year for CIGS, with the roof alone producing up to 2.1 MWh/Year (c-Si). Under hourly operation, the generated solar energy was sufficient to fully meet daily demand from April to August, while during continuous operation it supplied up to 60% of total consumption. The corresponding CO₂-emission reduction ranged from about 3.5 ton/Year for internal-combustion vehicles to around 2 ton/Year for electric ones. The framework provides a structured, data-driven approach for VIPV analysis, capable of simulating dynamic optical, thermal, and electrical behaviors under actual driving conditions. Its modular architecture ensures both immediate applicability and long-term adaptability, serving as a solid foundation for advanced VIPV design, fleet-scale optimization, and sustainability-oriented policy assessment. Full article

Conventionally, tomography is an inspection technique in which tomographic images are intended for human perception and interpretation. In this work, we shift this paradigm by transforming tomography into an autonomous estimator of industrial reactor states, enabling fully automated process control. Alcoholic fermentation was employed as an example of a controlled process in the current study. The work presents an original concept utilizing transfer learning in conjunction with a ResNet-type artificial neural network, which converts electrical measurements into a sequence of values correlated with the conductivity of pixels constituting the cross-section of the examined biochemical reactor. The conductivity vector is transformed into a parameter determining substrate concentration, enabling dynamic process regulation in response to signals generated from EIT (Electrical Impedance Tomography). Within the scope of the described research, calibration of the conductivity vector against substrate concentrations was performed, and a Matlab/Simulink-based dynamic Monod kinetics model was developed. The obtained results demonstrate high accuracy in substrate concentration estimation relative to reference values throughout a forty-six-hour process. The same signals enable energy-efficient process control, in which cooling and mixing intensity are regulated according to energy prices and renewable energy availability. This strategy may possess particular application in facilities where fermentation installations are co-located with bioenergy production units. Full article

The use of semi-transparent photovoltaic (Solar PV) glass in buildings is an effective strategy for integrating renewable energy generation, solar control, and thermal comfort. However, conventional simulation models rely on global optical properties, neglecting spectral radiation and its propagation within the material. This limits the accurate assessment of thermal comfort, light distribution, and performance in complex systems such as multi-layer glazing. This study presents the development, implementation, and experimental validation of a numerical model that reproduces the thermal, electrical, and optical behaviour of semi-transparent Solar PV glass, explicitly incorporating radiative transfer. The model simultaneously solves the conduction, convection, and electrical generation equations together with the radiative transfer equation, solved via the finite volume method across two spectral bands. The refractive index and extinction coefficient, derived from manufacturer-provided optical data, were used as inputs. Experimental validation employed 10% semi-transparent a-Si glass, comparing surface temperatures and electrical power generation. The model achieved average relative errors of 3.8% for temperature and 3.3% for electrical power. Comparisons with representative literature models yielded errors between 6% and 21%. Additionally, the proposed model estimated a solar factor of 0.32, closely matching the manufacturer’s 0.29. Full article