Search Results (986)

Amid fossil-fuel depletion and worsening environmental impacts, battery electric vehicles (BEVs) are pivotal to the energy transition. Energy management in BEVs relies on accurate motor efficiency maps, yet real-time onboard control demands models that balance fidelity with computational cost. To address map inaccuracy under real driving and the high runtime cost of 2-D interpolation, we propose a driving-cycle-aware, physically interpretable quadratic polynomial-surface framework. We extract priority operating regions on the speed–torque plane from typical driving cycles and model electrical power $P_e$ as a function of motor speed $n$ and mechanical power $P_m$. A nested model family (M3–M6) and three fitting strategies—global, local, and region-weighted—are assessed using $R^2$, RMSE, a computational complexity index (CCI), and an Integrated Criterion for accuracy–complexity and stability (ICS). Simulations on the Worldwide Harmonized Light Vehicles Test Cycle, the China Light-Duty Vehicle Test Cycle, and the Urban Dynamometer Driving Schedule show that region-weighted fitting consistently achieves the best or near-best ICS; relative to Global fitting, mean ICS decreases by 49.0%, 46.4%, and 90.6%, with the smallest variance. Regarding model order, the four-term M4 $\left(+P_m^2\right)$ offers the best accuracy–complexity trade-off. Finally, the region-weighted fitting M4 $\left(+P_m^2\right)$ polynomial model was integrated into the vehicle-level economic speed planning model based on the dynamic programming algorithm. In simulations covering a 27 km driving distance, this model reduced computational time by approximately 87% compared to a linear interpolation method based on a two-dimensional lookup table, while achieving an energy consumption deviation of about 0.01% relative to the lookup table approach. Results demonstrate that the proposed model significantly alleviates computational burden while maintaining high energy consumption prediction accuracy, thereby providing robust support for real-time in-vehicle applications in whole-vehicle energy management. Full article

Unmanned aerial vehicles (UAVs) are playing increasingly important roles in military operations, disaster relief, agriculture, and communications. However, their performance is limited by energy management problems, especially in hybrid systems such as those combining fuel cells with a lithium battery. The potential of deep learning to significantly improve UAV power management is investigated in this work through adaptive forecasting and real-time optimization. We develop smart algorithms that automatically balance energy efficiency and communication performance for heterogeneous wireless networks. The simulation results demonstrate energy consumption savings, optimized flight altitudes, and spectral efficiency improvements compared to Fixed Weight and Fuzzy Logic Weight schemes. At saturated user densities, the model enables up to 42% lower energy consumption and 54% higher throughput. Moreover, predictive models based on recurrent and transformer-based deep networks allow UAVs to predict energy requirements over a variety of mission and environmental contexts, shifting from reactive approaches to proactive control. The adoption of these methods in UAV-aided beyond-5G (B5G) and future 6G network scenarios can potentially prolong endurance times and enhance mission connectivity and reliability in challenging environments. This work lays the foundation for an all-aspect framework to control and manage UAV energy in the 5G era, which takes advantage of not only deep learning but also edge computing and hybrid power systems. Deep learning is confirmed to be a keystone of sustainable, autonomous, and energy-aware UAVs operation for next-generation networks. Full article

The maritime industry faces significant challenges from energy consumption and air pollution. Fuel cells, especially hydrogen types, offer a promising clean alternative with high energy density and rapid refueling, but their slow dynamic response necessitates integration with lithium batteries (energy storage) and supercapacitors (power storage). This paper investigates a hybrid vessel power system combining a fuel cell with a Hybrid Energy Storage System (HESS) to address these limitations. An improved droop control strategy with adaptive coefficients is developed to ensure balanced State of Charge (SOC) and precise current sharing, enhancing system performance. A comprehensive protection strategy prevents overcharging and over-discharging through SOC limit management and dynamic filter adjustment. Furthermore, the Parrot Optimization Algorithm (POA) optimizes HESS capacity configuration by simultaneously minimizing battery degradation, supercapacitor degradation, DC bus voltage fluctuations, and system cost under realistic operating conditions. Simulations show SOC balancing within 100 s (constant load) and 135 s (variable load), with the lithium battery peak power cut by 18% and the supercapacitor peak power increased by 18%. This strategy extends component life and boosts economic efficiency, demonstrating strong potential for fuel cell-powered vessels. Full article

The electricity–hydrogen–electricity conversion chain offers an effective solution for integrating clean energy into the grid while addressing multiple grid control requirements. Moreover, multiregional, interconnected, and integrated energy systems (IESs) can significantly increase overall energy utilization efficiency and operational flexibility through spatiotemporal coordination among diverse energy sources. However, few researchers have considered these two aspects in a unified framework. To address this gap, a low-carbon economic dispatch model and control strategy for a multiregional hydrogen-blended IES are proposed in this work. The model is constructed based on a system architecture that incorporates electricity–hydrogen–electricity conversion links while accounting for source–load uncertainties and peak shaving requirements. We solve the resulting distributed nonconvex nonlinear optimization problem using the alternating direction method of multipliers (ADMM). Furthermore, we analyze how uncertainty factors and peak shaving needs affect the maximum allowable hydrogen blending ratio in the gas grid, as well as the corresponding dynamic blending strategy. Our findings demonstrate that the proposed multiregional hydrogen-blended integrated energy system, with dynamic hydrogen blending control, significantly enhances the capacity for clean energy integration and reduces carbon emissions by approximately 12.3%. The peak-shaving demand is addressed through a coordinated mechanism involving electrolyzers (ELs), gas turbines (GTs), and hydrogen fuel cells (HFCs). This coordinated mechanism enables hydrogen fuel cells to double their output during peak hours, while electrolyzers increase their power consumption by approximately 730 MW during off-peak hours. The proposed dispatch model employs conditional risk measures to quantify the impacts of uncertainty and uses economic coefficients to balance various cost components. This approach enables effective coordination among economic objectives, risk management, and system performance (including peak shaving capability), thereby improving the practical applicability of the model. Full article

In terms of their high efficiency and low environmental impact, proton exchange membrane fuel cells (PEMFC) are becoming increasingly essential in the development of hydrogen electric vehicles. Despite these advantages, optimizing hydrogen consumption remains difficult because of the highly nonlinear behavior of PEMFC systems and their sensitivity to variations in operating conditions. This article outlines an intelligent control approach based on extremum seeking control (ESC), based on an artificial neural network (ANN) model, to improve hydrogen utilization in hydrogen electric vehicles. Experimental data on current, voltage, and temperature are collected, preprocessed, and used to train the ANN model of the PEMFC. The ESC algorithm uses this predictive ANN model to adjust the fuel cell current in real time, ensuring voltage stability while reducing hydrogen consumption. The simulation results demonstrate that the ANN-based ESC system provides voltage stability under dynamic load variations and achieves approximately 2.7% hydrogen savings without affecting the experimental current profile, validating the efficacy of the suggested strategy for effective hydrogen management in fuel cell electric vehicles. Full article

Cheesemaking is an energy-intensive process that relies heavily on heating and cooling operations traditionally powered by fossil fuels and electricity from the national grid. Reducing this dependence and integrating renewable energy sources are essential to align the sector with European decarbonization targets. This study presents the development of a simulation tool for optimizing the energy management of a cheese production facility by integrating solar, wind, and biomass systems. The model evaluates techno-economic and environmental performance under different climatic conditions and operational scenarios. Experimental validation was carried out using a prototype installed at the Polytechnic Institute of Beja (Portugal), achieving a deviation of only 2.3% in renewable energy contribution between simulated and measured data. Results demonstrate that renewable integration can reduce non-renewable energy consumption, achieving weekly profits up to 0.019 €/kg of cheese and carbon emissions as low as 0.0109 kg CO₂e/kg. The proposed approach provides a reliable decision-support tool for small- and medium-scale cheese producers, promoting both environmental sustainability and economic competitiveness in rural regions. Full article

The adoption of proton exchange membrane fuel cells (PEMFCs) in unmanned aerial vehicles (UAVs) offers a sustainable pathway to zero-emission propulsion, supporting aviation decarbonization by replacing battery or fossil fuel systems with efficient hydrogen technology. This work presents the development, validation, and application of a comprehensive dynamic model of a 1 kW open-cathode PEMFC system, including complete balance of plant (BOP) and control logic for four cooling fans, a purge valve, and a short-circuit unit (SCU). The model was validated through extensive experiments with step, triangular, and real-world UAV current profiles. Under steady-state conditions, it reproduces stack voltage with a <1 V average error and a temperature of 2.5 °C. Dynamic modeling accurately predicts fan behavior, purge/SCU events, and transient voltage drops. Applied to a 25 min UAV flight, the model quantifies reactant-management impacts: purge events increase H₂ usage by 4.8%, with SCU raising total to 5.6% above stoichiometric consumption. Altitude analysis shows ambient temperature reduction dominates the oxygen partial pressure effects, yielding net cell voltage increase under current-based fan control. These insights underscore explicit BOP and ambient modeling for accurate UAV endurance estimation and strategy optimization, providing a basis for future altitude-chamber validation. By enabling precise BOP dynamics simulation and H₂ optimization, this model advances the achievement of affordable clean energy, facilitating an extended endurance with minimal environmental impact. Full article

This study presents the design, real-time implementation, and full-scale experimental validation of a rule-based Energy Management Strategy (EMS) for a dual-stack Fuel Cell Hybrid Electric Vehicle (FCHEV) developed on a Jeep Wrangler platform. Unlike previous studies, predominantly focused on simulation-based analysis or single-stack architectures, this work provides comprehensive vehicle-level experimental validation of a deterministic real-time EMS applied to a dual fuel cell system in an SUV-class vehicle. The control algorithm, deployed on a National Instruments CompactRIO embedded controller, ensures deterministic real-time energy distribution and stable hybrid operation under dynamic load conditions. Simulation analysis conducted over eight consecutive WLTC cycles shows that both fuel cell stacks operate predominantly within their optimal efficiency range (25–35 kW), achieving an average DC efficiency of 68% and a hydrogen consumption of 1.35 kg/100 km under idealized conditions. Experimental validation on the Wrangler FCHEV demonstrator yields a hydrogen consumption of 1.67 kg/100 km, corresponding to 1.03 kg/100 km·m² after aerodynamic normalization (Cd·A = 1.624 m²), reflecting real-world operating constraints. The proposed EMS promotes fuel-cell durability by reducing current cycling amplitude and maintaining operation within high-efficiency regions for the majority of the driving cycle. By combining deterministic real-time embedded control with vehicle-level experimental validation, this work strengthens the link between EMS design and practical deployment and provides a scalable reference framework for future hydrogen powertrain control systems. Full article

This study rigorously evaluated the integration of energy-harvesting systems within electric vehicles to prolong battery service life. A laboratory-scale system was configured utilizing a scale electric vehicle with a 12.6 V lithium-polymer (Li-Po) battery alongside an automated control platform to precisely estimate the real-time State of Charge (SoC) through monitoring of current, voltage, and temperature of the vehicle battery under three distinct driving conditions: (A) constant velocity at 30 km/h, (B) variable velocities exhibiting a sawtooth profile, and (C) random speed variations. Wind energy was harvested employing Savonius rotor microturbines, with assessments conducted on efficiency losses and drag coefficients to determine the net power yield for each operational profile, which was found to be marginally positive. Considering the energy consumption of electric vehicles based on 2017 U.S. EPA fuel economy data, the maximal recovered energy corresponded to 0.0833% of auxiliary system demand, while the minimal recovery was 0.0398%. These results substantiated the necessity for continued research into sustainable energy management frameworks for electric vehicles. They emphasized the critical importance of optimizing the incorporation of renewable energy technologies to mitigate the environmental ramifications of the transportation sector. Full article

The heating and cooling sector accounts for nearly half of Europe’s energy consumption and remains heavily dependent on fossil fuels, emphasizing the urgent need for decarbonization. Simultaneously, the global shift toward renewable energy is accelerating, alongside growing interest in decentralized energy systems where prosumers play a significant role. In this context, district heating and cooling networks, serving nearly 100 million people, are strategically important. In next-generation systems, thermal prosumers can feed-in locally produced or industrial waste heat into the network via bidirectional substations, allowing energy flows in both directions and enhancing system efficiency. The complexity of these networks, with numerous users and interacting heat flows, requires advanced predictive models to manage large volumes of data and multiple variables. This work presents the development of a predictive model based on artificial neural networks (ANNs) for forecasting excess thermal renewable energy from a bidirectional substation. The numerical model of a substation prototype designed by ENEA provided the physical data for the ANN training. Thirteen years of simulation results, combined with extensive meteorological data from ECMWF, were used to train and to test a multi-step ANN capable of forecasting the six-hour thermal power feed-in horizon using data from the preceding 24 h, improving operational planning and control strategies. The ANN model demonstrates high predictive capability and robustness in replicating thermal power dynamics. Accuracy remains high for horizons up to six hours, with MAE ranging from 279 W to 1196 W, RMSE from 662 W to 3096 W, and R² from 0.992 to 0.823. Overall, the ANN satisfactorily reproduces the behavior of the bidirectional substation even over extended forecasting horizons. Full article

The study analyzed the energy flow of a second-generation Toyota Mirai FCEV under Real Driving Conditions (RDC) in ECO and Normal driving modes. The results demonstrated significant operational differences between the two modes. The ECO mode reduced the maximum motor torque from 286.5 Nm to 187.6 Nm (−51%) but increased the high-voltage (HV) battery State of Charge swing (ΔSOC = 17.26% vs. 10.59%, +63%). Regenerative energy recovery rose by ~19.8% overall and by 25.7% in urban driving. The ECO mode exhibited higher HV battery cycling (4.03 Wh vs. 3.27 Wh) and slightly higher fuel cell energy use in urban conditions (+8.5%). The average fuel cell power was 36% higher in Normal mode, whereas the HV battery output was 11.4% higher in ECO mode. Hydrogen consumption in Normal mode was two times higher in urban and highway phases and three times higher in rural driving compared to ECO mode. In summary, the ECO mode enhances regenerative energy utilization and reduces total onboard energy consumption, at the expense of peak torque and increased battery cycling. These results provide valuable insights for optimizing energy management strategies in fuel cell electric powertrains under real driving conditions. The study introduces an independent methodology for high-resolution (1 Hz) electric energy-flow monitoring and quantification of energy exchange between the fuel cell, high-voltage battery, and powertrain system under Real Driving Conditions (RDC). Unlike manufacturer-derived data or laboratory simulations, the presented approach enables empirical validation of on-board energy management strategies in production FCEVs. The results reveal distinctive energy-flow patterns in ECO and Normal modes, offering reference data for the optimization of future hybrid control algorithms in hydrogen-powered vehicles. Full article

Passenger cars, sports utility vehicles (SUVs), and light trucks/vans, constituting the overwhelming majority of all road vehicles globally, burn about 25% of all fossil fuels, emit significant amounts of greenhouse gas emissions (CO₂), and deteriorate the environment. Nearly three-quarters of the engine power generated by burning fossil fuels is required to overcome aerodynamic resistance (drag) at highway driving speeds. Streamlining the body shape, especially the projected frontal area, can lead to a decrease in aerodynamic drag. Even though drag coefficients have plateaued since the late 1990s, further altering body shape might worsen vehicle cooling. Thus, the primary objective of this study is to explore the potential for aerodynamic drag reduction and improved cooling performance through careful component design unaffected by stylistic restraints. A variety of strategies for protecting the cooling intakes to reduce the drag coefficient are considered. The potential cooling drag reduction was found to be around 7% without compromising the cooling performance, which is in line with predictions for roughly 2.9% and 1.7% fuel consumption reductions for highway and city driving conditions, respectively. The study also reveals that passenger electric cars designed for city driving conditions possess a battery-to-kerb weight ratio of around one-quarter of the kerb weight, and vehicles designed for higher ranges have significantly higher ratios (nearly one-third), resulting in higher rolling resistance and energy consumption. The reduction of battery weight for EVs, streamlining vehicle shapes, and applying active and passive airflow management can help reduce aerodynamic drag and rolling resistance further, enhance driving range, and reduce energy consumption and greenhouse gas emissions. Full article

The controller of the energy management system must be capable of meeting the rapid and dynamic demands of fuel cell electric vehicles (FCEVs) without compromising its performance and durability. The performance of FCEVs can be enhanced through powertrain hybridization with battery and ultracapacitor systems. The overall dynamic optimization of the energy between the batteries/ultracapacitors and the Proton Exchange Membrane Fuel Cell (PEMFC) output can play an important role in hydrogen fuel economy and the durability of vehicle systems. The present study investigates the system’s efficiency and fuel consumption in European Drive Cycles when employing diverse energy management strategies. This investigation utilizes a novel switch real-time strategy (SWA_RTO), which is founded on an A-factor algorithm that alternates between the most effective Real Time Optimization (RTO) strategies. The objective of this paper is to underscore the significance of algorithmic optimization by presenting the optimal results obtained for the fuel economy of the SWA_RTO strategy. These results are compared with the basic RTO strategy and the static Feed-Forward (sFF) reference strategy. The load demand during driving cycles is primarily determined by the PEMFC system. Minor discrepancies in power balance are addressed by the hybrid battery and ultracapacitor system. Consequently, the lifespan of the subject will increase, and the state of charge (SOC) will no longer be a factor in monitoring. Full article

Soil compaction from repeated mechanized traffic in sugarcane cultivation reduces porosity, root growth, water infiltration and nutrient availability. Pre-consolidation stresses (σP) in sugarcane soils (70–210 kPa) are frequently exceeded by machine loads up to 595 kPa, producing bulk density (ρb) above 1.65 Mg m⁻³ and soil resistance to penetration (SR) beyond 2.0 MPa within the upper 0.40 m; approximately 80% of root biomass concentrates in this zone. Conventional whole-area subsoiling is energy-intensive, destabilizes soil structure and accelerates re-compaction, limiting long-term efficacy. This review proposes integrating strip soil tillage (SST) with controlled traffic farming (CTF) via a multifunctional implement that performs selective subsoiling, in-row chemical correction and targeted input application. The system is designed to mobilize 53% of the area, preserve inter-row structure, reduce fuel consumption by 43.5%, decrease CO₂ emissions by 163–315.4 kg ha⁻¹ and lower operational costs by 53.5% relative to conventional approaches. The implement features adjustable-depth subsoiler shanks with dedicated input dispensers, rotary hoes for organic amendment incorporation and GNSS-guided autopilot for precise in-row operations. Expected outcomes include improved soil physical quality, enhanced root development beyond 1.30 m, increased input-use efficiency and sustainable productivity gains under CTF–SST management. This review is innovative in explicitly proposing and detailing the integration of CTF with SST through a multifunctional implement. This approach advances current knowledge by overcoming the main limitations of conventional soil tillage systems, such as accelerated recompaction, high energy consumption, and inefficient input use, while promoting measurable improvements in soil physical quality, operational efficiency, and sustainable productivity. A literature review search up to 31 May 2025 supported the integration of SST and CTF as a viable strategy for sustainable soil management in sugarcane production. Full article

Rising energy demand, fossil fuel depletion, and global warming are accelerating research into sustainable energy solutions, with growing interest in hydrogen as a promising alternative. This research presents a detailed experimental investigation and novel digital twin (DT) models for an integrated hydrogen-based energy system consisting of an Anion Exchange Membrane Electrolyzer (AEMEL), Proton Exchange Membrane Fuel Cell (PEMFC), hydrogen storage, and Battery Energy Storage System (BESS). Conducted at a real-world facility in Risavika, Norway, the study employed commercial units: the Enapter EL 4.1 AEM electrolyzer and Intelligent Energy IE-Lift 1T/1U PEMFC. Experimental tests under dynamic load conditions demonstrated stable operation, achieving hydrogen production rates of up to 512 NL/h and a specific power consumption of 4.2 kWh/Nm³, surpassing the manufacturer’s specifications. The PEMFC exhibited a unique cyclic operational mechanism addressing cathode water flooding, a critical issue in fuel cell systems, achieving steady-state efficiencies around 43.6% under prolonged (190 min) rated-power operation. Subsequently, advanced DT models were developed for both devices: a physics-informed interpolation model for the AEMEL, selected due to its linear and steady operational behavior, and an ANN-based model for the PEMFC to capture its inherently nonlinear, dynamically fluctuating characteristics. Both models were validated, showing excellent predictive accuracy (<3.8% deviation). The DTs integrated manufacturer constraints, accurately modeling transient behaviors, safety logic, and operational efficiency. The round-trip efficiency of the integrated system was calculated (~27%), highlighting the inherent efficiency trade-offs for autonomous hydrogen-based energy storage. This research significantly advances our understanding of integrated H₂ systems, providing robust DT frameworks for predictive diagnostics, operational optimization, and performance analysis, supporting the broader deployment and management of hydrogen technologies. Full article