Search Results (1,718)

The rapid development of renewable energy vehicles (REVs) has deepened the coupling between transportation and power systems, leading to the formation of the vehicle–station–traffic–grid (VSTG) coupled system. This paper provides a systematic review of multi-agent intelligent interaction technologies for REVs under the VSTG framework, covering the evolutionary process of VSTG systems, the composition and coupling mechanisms of vehicle–station–traffic–grid subsystems, the objectives and constraints of heterogeneous agents, representative V2X interaction modes, deployment-related standards, and collaborative optimization methods. First, the development trajectory of VSTG systems is traced, from independent planning and uncoordinated charging to V2G integration and V2X multi-network interaction. Second, a multi-agent interaction framework is established to characterize vehicle agents, charging station agents, grid agents, traffic management agents, user/operator agents, aggregator/platform agents, and roadside infrastructure agents. In addition, representative vehicle-to-everything (V2X) modes, including V2L, V2H, V2B, V2mG, and V2G, are compared in terms of their operating principles, application scenarios, and technical characteristics. Moreover, various optimization methods for the coupled system are reviewed. Finally, key challenges, including cross-domain coupling complexity, operational uncertainty, interoperability, battery degradation, and engineering deployment, are discussed, and future research directions are proposed. This review provides a structured reference for the modeling, optimization, and practical deployment of intelligent VSTG systems. Full article

Port operators must now reduce emissions without weakening the reliability of cargo-handling and logistics services. Two load groups are especially important in this setting: vessels connected to shore-side facilities during berthing and heavy-duty vehicles working inside the terminal area. Their energy-use patterns shape both dispatch stability and the carbon intensity of the port energy system. This paper therefore proposes an integrated port energy management model that jointly schedules wind power, photovoltaic generation, hydrogen production and storage, shore power, conventional purchases, berthed-vessel demand, and low-carbon heavy-duty transport demand. The model combines price-based demand response with a tiered carbon-trading penalty so that flexible electricity consumption and emission costs are reflected in the dispatch decision. Numerical simulations show that the joint use of demand response and the carbon-penalty mechanism lowers total economic dispatch cost by about $11.05\%$ and reduces carbon emissions by $24.52\%$. The results indicate that coordinated renewable-energy and logistics-aware scheduling can improve the economic and environmental performance of port operations. Full article

Intelligent Transportation Systems (ITS) play a central role in the development of more efficient, adaptive, and resilient road networks. Traffic control strategies have progressively evolved from traditional approaches toward more intelligent and adaptive frameworks. This paper presents a taxonomy-based perspective on intelligent traffic control strategies for road networks, organizing existing approaches according to three complementary dimensions: control scope, decision-making mechanism, and control architecture. Based on this framework, the paper discusses representative methodologies, including rule-based control, model-based methods, simulation-based optimization, data-driven and artificial intelligence-based methods, and emerging cooperative strategies enabled by connected and automated vehicles (CAVs). The analysis also examines key application domains, such as traffic signal control, ramp metering, CAV-based traffic management, and simulation platforms, highlighting their operational principles, advantages, limitations, and implementation challenges. Particular attention is given to the transition from local and reactive control toward coordinated, predictive, and learning-based traffic management systems. The paper identifies major challenges related to scalability, robustness, interpretability, safety, real-world deployment, and the gap between simulation performance and practical implementation. The proposed taxonomy also supports practical comparison and preliminary selection of context-specific strategies. Future directions point toward integrated and hybrid frameworks combining data-driven adaptability, vehicle–infrastructure cooperation, and digital twin technologies. Full article

The transport sector plays a significant role in air pollution, and real-world emissions measurements are becoming increasingly important. In this study, emissions from a turbocharged, direct-injection gasoline internal combustion engine (ICE) vehicle and a port fuel injection (PFI) hybrid electric vehicle (HEV) were compared using a portable emissions measurement system (PEMS) under real-world driving conditions. The CO₂, CO, NO_x, and PN emissions of the two vehicles were measured in urban, rural, and motorway sections. HEV CO₂ emissions were ~20% lower than ICE emissions in the entire Real Driving Emissions (RDE) cycle, while in urban operation, they were almost 50% lower. PN emissions were lower for HEV in rural and motorway sections than for ICE, but significant PN peaks occurred during the early urban phase, attributable to the slower engine warm-up of the HEV. Machine learning analysis (Random Forest and Extra Trees Regressor) indicated that coolant temperature was the dominant driver of HEV PN emissions. The results indicate that powertrain characteristics and thermal management strongly influence real-world driving emissions, highlighting their importance for the further development of hybrid vehicles. Full article

Canned motor pumps are widely utilized due to their distinct advantage of a completely leakage-free structure. Among them, an integrated impeller–rotor configuration is employed in the axial-flux canned motor pump, resulting in a shorter axial length and higher power density. This novel configuration allows for easy integration into space-constrained systems, such as electric vehicles, aerospace applications, and liquid-cooled servers. However, research on the internal flow characteristics of these pumps remains scarce. To address this gap, the present study investigates the internal flow across various flow rates. Numerical simulations are validated against experimental data. The average error remains below 2%. The pump achieves a peak efficiency of 68.6% at the design condition, but experiences efficiency drops of 15.0 and 25.2 percentage points under 0.5Q_d and 1.5Q_d, respectively. Results demonstrate that flow rates significantly govern internal characteristics. These include pressure, velocity, and entropy distributions, along with vortex structures and pressure fluctuations. Notably, operating at off-design conditions can intensify the internal pressure fluctuations by up to a factor of 29.4. Entropy analysis identifies major losses on blade suction sides and diffusers. These findings provide crucial hydrodynamic guidelines for low-noise thermal management systems in electric vehicles and ensuring high-reliability cooling loops in aerospace and liquid-cooled servers. Full article

The Internet of Vehicles (IoV) is reshaping intelligent transportation through pervasive connectivity, real-time data exchange, cooperative perception, and vehicle–edge–cloud services, while also expanding cybersecurity and privacy risks across heterogeneous cyber–physical environments. This paper presents a PRISMA 2020-informed systematic review of IoV security and privacy protection research. A cross-layer and lifecycle-oriented analytical framework is developed by integrating a four-layer IoV architecture—sensing layer, network access layer, coordinative computing layer, and application layer—with a five-stage data lifecycle covering data collection, transmission, storage, usage, and disposal. Based on this framework, the paper examines representative threat surfaces, vehicle-to-everything (V2X) communication security, public key infrastructure (PKI) based authentication, trust management, privacy-preserving data sharing, intrusion detection, active defense, and AI-assisted security analytics. Privacy-preserving mechanisms, including differential privacy, federated learning, blockchain, homomorphic encryption, and secure multi-party computation, are further compared in terms of deployment layer, lifecycle stage, real-time suitability, and representative performance evidence. In addition, the review discusses the engineering relevance of UNECE WP.29 R155/R156, ISO/SAE 21434, and related national standards, with emphasis on compliance evidence, over-the-air (OTA) governance, supply-chain coordination, and lifecycle cybersecurity management. The review shows that no single protection mechanism can simultaneously satisfy the requirements of real-time performance, scalability, privacy preservation, trustworthiness, and regulatory compliance in dynamic IoV environments. Future research should emphasize lightweight and adaptive protection, cross-layer trust coordination, privacy–utility co-optimization, trustworthy AI-assisted security operations, and evidence-based lifecycle governance. This review provides a structured reference for researchers and a practical basis for secure and privacy-aware IoV system design. Full article

Electric Vehicle Charging Stations (EVCSs) have become increasingly important due to the growing penetration of electric vehicles (EVs) and renewable-based power generation. However, challenges such as fluctuating renewable energy availability, increasing charging demand, power losses, operational cost, and charging delays continue to affect overall grid performance and stability. To address these issues, this study proposes a hybrid Goat Optimization Algorithm–Hierarchical Multi-Granularity Interaction Graph Convolutional Network (GOA–HMGIGCN) framework for intelligent smart grid energy management and EV charging coordination. The proposed framework combines the Goat Optimization Algorithm (GOA) for optimal EVCS placement and charging scheduling with the Hierarchical Multi-Granularity Interaction Graph Convolutional Network (HMGIGCN) for forecasting renewable generation, charging demand, and load variations. The framework was implemented and evaluated in MATLAB/Simulink R2024a using the IEEE 14-bus smart grid test system under varying operating conditions. Simulation results demonstrated that the proposed framework achieved superior performance compared with the Coot Optimization Algorithm–Fractional Backpropagation Physics-Informed Neural Network (COA-FBPINN), Dingo Optimization Algorithm–Convolutional Hypergraph Graph Neural Network (DOA-CHGNN), Self-Feedback Feedforward Artificial Neural Network (SFFANN), Deep Neural Network (DNN), and Golden Jackal Optimization–Attention-Based Probabilistic Convolutional Neural Network (GJO-APCNN) techniques by attaining the lowest operational cost of USD 1561, the highest efficiency of 99.2%, the minimum power loss of 10.6 kW, and the shortest charging time of 32 min. In addition, the proposed framework and overall grid reliability, confirming its effectiveness for intelligent renewable-integrated smart grid applications. Full article

Despite the large amount of successful existing methods and frameworks for planning sets of multiple unmanned aerial systems (UASs), there is still a lack of coordination frameworks that are capable of coping with real-world operational conditions. This paper presents U-Plan, an integrated management framework for the coordination of multi-UAS missions. U-Plan is designed to plan, schedule, monitor, and replan a heterogeneous set of UASs to complete point of interest (PoI) visiting missions while ensuring that all the generated trajectories are safe, feasible, and compliant with the required PoIs’ arrival times, UAS kinematics and energy constraints, and the existing 3D no-fly zones (NFZs). U-Plan is designed as a practical tool for strongly dynamic missions and is built upon three core components: (1) an NFZ-aware route computation method that explicitly accounts for NFZs prior to vehicle routing problem (VRP) optimization, resulting in shorter NFZ-safe routes; (2) a trajectory smoothing module that ensures the generation of kinematically feasible trajectories for fixed-wing UASs; and (3) a mission supervision module for real-time monitoring and replanning in case of changes in the UAS, mission, wind speed, or airspace restrictions. To validate the proposed architecture, we conducted rigorous experiments utilizing the VECTOR-SIL autopilot and Visionair Ground Control Station to realistically replicate the behavior of certified fixed-wing autopilots under various weather conditions using the exact same hardware and flight control software that runs onboard the physical drones. The validation shows U-Plan’s capacity to efficiently satisfy complex mission requirements with strong scalability. Due to its high computational efficiency, U-Plan enables online mission replanning, allowing UAS fleets to seamlessly adapt to changes that are typical of real-world operational scenarios. Full article

The increasing penetration of renewable energy sources and converter-interfaced loads has intensified the need for fast and reliable grid-support services. Although electric vehicle (EV) battery chargers have emerged as promising resources for Vehicle-to-Grid (V2G) applications, existing solutions typically focus on individual services such as virtual inertia or frequency regulation, while limited attention has been given to the coordinated provision of multiple ancillary services within a unified framework. Furthermore, the use of batteries alone for fast frequency support may accelerate battery degradation due to frequent high-power transients. To address these challenges, this paper proposes a hybrid energy storage-based EV battery charger architecture and a coordinated multi-timescale control strategy capable of simultaneously providing virtual inertia support, long-term frequency regulation, reactive power compensation, and harmonic mitigation. The proposed approach utilizes a DC-link capacitor to deliver fast inertial response while the battery supplies sustained frequency support, thereby reducing battery stress and improving energy management efficiency. An enhanced frequency estimation method based on a phase-locked loop combined with a low-pass filter is also introduced to improve dynamic performance. Simulation results demonstrate the effectiveness of the proposed strategy under various grid disturbances. The system achieves an equivalent virtual inertia constant of approximately 1.85 s and delivers up to 786 W of transient inertial support within 80 ms during frequency events. The enhanced frequency estimation method significantly reduces transient overshoot, while harmonic compensation limits the grid current and voltage total harmonic distortion to 1.50% and 3.23%, respectively. In addition, the controller provides up to 400 VAR of reactive power support during voltage disturbances while maintaining stable battery operation. These results demonstrate that the proposed EV battery charger can function as a multifunctional grid-support resource, enhancing frequency stability, voltage regulation, power quality, and overall V2G capability in future smart grids. Full article

Rising demand for high-performance battery thermal management systems (BTMSs) has rendered single-mode cooling insufficient for advanced lithium-ion batteries (LIBs) in new energy vehicles (NEVs), particularly under high discharge rates. This study proposes a synergistic hybrid BTMS integrating composite phase-change material (CPCM)–aluminum foam with liquid cooling to enhance thermal regulation of cylindrical battery modules under 5 C discharge conditions. Multiple liquid-cooled plate (LCP) configurations, including serpentine, straight, and leaf-shaped designs, together with different flow channel topologies (FCTs), were systematically investigated and optimized. The effects of coolant flow speed (CFS) and ambient temperature were also analyzed. Results indicate that the optimized leaf-shaped LCP with FCT #2 delivers superior performance, limiting the maximum temperature to 309.98 K, reducing temperature difference by 7.6%, and decreasing pressure drop by 88.79% compared to the serpentine configuration. Increasing CFS improves heat dissipation and delays PCM melting, although it raises pressure losses. Furthermore, the proposed system maintains a cell-to-cell temperature difference below 0.51 K, indicating excellent thermal uniformity. Compared to a CPCM-only system, the hybrid BTMS reduces peak temperature by 8.81 K under elevated ambient conditions (309.15 K), demonstrating strong potential for reliable and efficient thermal management in demanding operating environments. Full article

Thermal regulation of lithium-ion (Li-ion) battery modules is a critical constraint for electric vehicle (EV) safety and durability, particularly during high-C-rate operation. Phase change materials (PCMs) have emerged as promising passive solutions due to their latent heat storage capability; however, current literature is heavily biased toward organic paraffin-based systems and lacks structured benchmarking across PCM categories and integration architectures. This review provides a systematic comparative assessment of PCM-based battery thermal management systems (BTMSs) comprising organic, inorganic, and eutectic materials under EV-relevant discharge conditions. The review is structured according to the conventional classification of PCMs; however, the available literature is predominantly focused on organic materials, particularly paraffin-based PCMs, leading to greater depth of analysis for this category. Thermophysical properties are analyzed in conjunction with discharge rate, module configuration, and hybrid cooling strategies. The results indicate that peak temperature mitigation is weakly correlated with latent heat magnitude when thermal conductivity remains below critical values. Conductivity-enhanced composites incorporating expanded graphite or metal foams significantly improve heat diffusion, reducing hotspot intensity and inter-cell temperature gradients under medium-to-high C-rates. Pure passive PCM systems exhibit thermodynamic limitations during sustained high-power operation due to saturation effects, underscoring the need for hybrid architectures for continuous heat rejection. This work establishes a structured benchmarking framework and demonstrates that effective thermal conductivity, integration strategy, and discharge-dependent design dominate BTMS performance over latent heat alone. The findings also reveal that inorganic and eutectic PCM-based BTMSs remain comparatively less explored in the literature, particularly at the battery module level and under realistic electric vehicle operating conditions, highlighting opportunities for future research. Full article

Designing low-voltage (LV) power distribution systems for mass-sensitive electric vehicles involves several unresolved technical challenges, including parasitic $I^{2}R$ losses, excessive mass of commercial off-the-shelf distribution units, and difficulties in isolating thermal phenomena during vehicle operation. In dynamic racing conditions, temperature measurements of LV components are strongly influenced by external heat sources such as traction batteries, motors, and inverters, complicating accurate assessment of conductor self-heating and distribution losses. This work presents a load-driven methodology for the specification, implementation, and validation of LV architectures, demonstrated using a Formula Student electric race car. The proposed approach combines harness current mapping, resistive loss modeling, and component-level topology optimization to support the development of lightweight and electrically robust systems. Within this framework, a mass-optimized programmable solid-state power distribution unit (PDU), an auxiliary battery system with a battery management system (BMS), and an optimized LV wiring harness were developed and experimentally validated through controlled subsystem tests and in-vehicle operation. The proposed methodology enabled reduction in PDU mass by 40–80% relative to commercially available solutions while maintaining programmable protection, integrated current sensing, and stable thermal operation under representative racing loads. This reduction was achieved through load-driven conductor sizing, application-specific protection threshold optimization, and elimination of redundant protection and interconnection hardware. The developed PDU achieved a mass of 155 g with measured channel resistances of 40–70 m$\mathsf{\Omega }$. The auxiliary battery pack exhibited an average internal resistance of 64.2 m$\mathsf{\Omega }$ at a total mass of 2190 g, while the optimized harness demonstrated resistivity in the range of 14.72–33.98 m$\mathsf{\Omega }$/m. Experimental validation confirmed stable operation below critical thermal limits under both nominal and off-nominal load conditions. The obtained results demonstrate that the proposed methodology enables measurable reductions in both system mass and resistive power losses through application-specific optimization of the LV architecture. However, the presented approach is primarily suited for motorsport and other highly mass-constrained applications, where reduced packaging volume, efficiency, and weight justify the increased design complexity and lower universality compared to commercial off-the-shelf solutions. Full article

Hybrid tractors are a promising solution for reducing fuel consumption and emissions in agricultural machinery. However, their low-speed, high-torque operation with frequent load fluctuations demands an energy management strategy (EMS) that is both real-time capable and highly adaptive. This study focuses on a coaxial parallel hybrid electric tractor, developing a forward simulation model that integrates longitudinal vehicle dynamics, engine, motor, battery, and transmission systems. An improved equivalent fuel consumption minimization strategy (ECMS) with state-of-charge feedback correction, termed F-ECMS, is proposed. It dynamically adjusts the equivalence factor based on real-time battery SOC to approach optimal fuel economy while sustaining charge. Dynamic programming (DP) is used to establish a global benchmark. Simulations under a typical plowing cycle show that over 14,400 s, the F-ECMS maintains SOC (0.5964) close to the DP reference (0.6000), while achieving a 1.51% reduction in equivalent fuel consumption compared to a rule-based strategy. The results demonstrate that the proposed F-ECMS offers an effective balance between real-time performance and fuel economy, showing strong potential for practical implementation in hybrid agricultural vehicles. Full article

The rising global population has heightened food demand, placing pressure on agricultural systems, particularly in water-scarce regions such as South Africa. Smallholder farmers, essential to the sector, face climatic variability and resource constraints, necessitating innovative solutions to enhance sustainability and productivity. This study evaluates unmanned aerial vehicles (UAVs) for generating spatially explicit evapotranspiration (ET) estimates in a small-scale sugarcane field, supporting precision water management. Vegetation indices (VIs) derived from UAV-based multispectral imagery were used to predict actual ET (ET_a) and validated against eddy covariance measurements. Five models were assessed, including Normalised Difference Vegetation Index (NDVI)-based and Enhanced Vegetation Index (EVI)-based approaches. Machine learning was used to relate crop coefficients (K_c) to NDVI, enabling improved estimation. The two-band EVI (EVI2) model achieved the highest accuracy, with an R² of 0.63, an RMSE of 0.67, and an MAE of 0.52. ET-VI approaches, particularly EVI2, require lower data and technical complexity, making them suitable for smallholder systems. However, reducing dependence on in situ data remains essential to improve accessibility of remote sensing approaches for agricultural water management in resource-limited environments. These findings demonstrate the potential of UAV-based ET_a modelling to support field-scale irrigation decision-making while highlighting the need for further refinement to improve operational applicability across diverse smallholder farming contexts and beyond. Full article

To make electric mobility possible and acceptable on a large scale, it is necessary to integrate electric vehicle (EV) charging infrastructure in residential energy systems. Solar surplus charging, a special case of controlled charging, is a popular and promising operating mode of installed systems. Comparison of different home energy management systems (HEMSs) in combination with a dedicated EV charging station reveals differences in control quality. Within the research project Wallbox-Inspektion, a test setup has been developed. The derived procedures determine the main criteria for evaluating the quality of solar surplus charging. The core question is: “How well does the EV charging power follow the reference?”. This contribution explains the tests for standby consumption and control quality of control steps and presents an approach to determine the impact on use case scenarios. Further, different solar charging systems (i.e., charging station, HEMS, energy meter) available on the market are compared and discussed regarding the quality of implemented solar charging strategies. Full article