Search Results (163)

This paper investigates the problem of computation offloading and resource allocation in an integrated space–air–sea network based on unmanned aerial vehicle (UAV) and low Earth orbit (LEO) satellites supporting Maritime Internet of Things (M-IoT) devices. Considering the complex, dynamic environment comprising M-IoT devices, UAVs and LEO satellites, traditional optimization methods encounter significant limitations due to non-convexity and the combinatorial explosion in possible solutions. A multi-agent deep deterministic policy gradient (MADDPG)-based optimization algorithm is proposed to address these challenges. This algorithm is designed to minimize the total system costs, balancing energy consumption and latency through partial task offloading within a cloud–edge-device collaborative mobile edge computing (MEC) system. A comprehensive system model is proposed, with the problem formulated as a partially observable Markov decision process (POMDP) that integrates association control, power control, computing resource allocation, and task distribution. Each M-IoT device and UAV acts as an intelligent agent, collaboratively learning the optimal offloading strategies through a centralized training and decentralized execution framework inherent in the MADDPG. The numerical simulations validate the effectiveness of the proposed MADDPG-based approach, which demonstrates rapid convergence and significantly outperforms baseline methods, and indicate that the proposed MADDPG-based algorithm reduces the total system cost by 15–60% specifically. Full article

In this paper, unmanned aerial vehicle (UAV)-assisted vehicular communications are investigated to minimize latency and maximize the utilization of available UAV battery power. As communication and cooperation among UAV and vehicles is frequently required, a viable approach is to reduce the transmission of redundant messages. However, when the sensor data captured by the varying number of vehicles is not independent and identically distributed (non-i.i.d.), this becomes challenging. Hence, in order to group the vehicles with similar data distributions in a cluster, we utilize federated learning (FL) based on an attention mechanism. We jointly maximize the UAV’s available battery power in each transmission window and minimize communication latency. The simulation experiments reveal that the proposed personalized FL approach achieves performance improvement compared with baseline FL approaches. Our model, trained on the V2X-Sim dataset, outperforms existing methods on key performance indicators. The proposed FL approach with an attention mechanism offers a reduction in communication latency by up to 35% and a significant reduction in computational complexity without degradation in performance. Specifically, we achieve an improvement of approximately 40% in UAV energy efficiency, 20% reduction in the communication overhead, and 15% minimization in sojourn time. Full article

Uncoordinated electric vehicle (EV) charging can significantly complicate power system operations. In this paper, we develop a hybrid EV charging method that seamlessly integrates centralized EV charging and distributed control schemes to address EV energy demand challenges. The proposed method includes (1) a centralized day-ahead optimal scheduling mechanism and EV shifting process based on mixed-integer linear programming (MILP) and (2) a distributed control strategy based on a genetic algorithm (GA) that dynamically adjusts the charging rate in real-time grid scenarios. The MILP minimizes energy imbalance at overloaded slots by reallocating EVs based on supply–demand mismatch. By combining full and minimum charging strategies with MILP-based shifting, the method significantly reduces network stress due to EV charging. The centralized model schedules time slots using valley-filling and EV-specific constraints, and the local GA-based distributed control adjusts charging currents based on minimum energy, system availability, waiting time, and a priority index (PI). This PI enables user prioritization in both the EV shifting process and power allocation decisions. The method is validated using demand data on a radial feeder with residential and commercial load profiles. Simulation results demonstrate that the proposed hybrid EV charging framework significantly improves grid-level efficiency and user satisfaction. Compared to the baseline without EV integration, the average-to-peak demand ratio is improved from 61% to 74% at Station-A, from 64% to 80% at Station-B, and from 51% to 63% at Station-C, highlighting enhanced load balancing. The framework also ensures that all EVs receive energy above their minimum needs, achieving user satisfaction scores of 88.0% at Stations A and B and 81.6% at Station C. This study underscores the potential of hybrid charging schemes in optimizing energy utilization while maintaining system reliability and user convenience. Full article

The rapid growth of electric vehicles (EVs) poses significant challenges to the safe operation of charging stations and distribution networks. Variations in charging power across different EV manufacturers lead to substantial load fluctuations at charging stations. In some tourist cities in China, charging loads can surge at specific times, yet existing research mainly focuses on optimizing station location and basic capacity configuration, neglecting sudden peak load management. To address this, we propose a method that enhances charging station carrying capacity (CSCC) by coordinating multi-flexibility resources. This method optimizes the configuration of soft open points (SOPs) to enable flexible interconnections between feeders and incorporates elastic load scheduling for data centers. An optimization model is developed to coordinate these flexible resources, thereby improving the CSCC. Case studies demonstrate that this approach effectively increases CSCC at lower costs, facilitates the utilization of renewable energy, and enhances the overall system economy. The results validate the feasibility and effectiveness of the proposed approach, offering new insights for urban grid planning and EV charging stations optimization. Full article

The integration of renewable energy and electric vehicle (EV) charging stations into distribution systems presents critical challenges, including the inherent variability of renewable generation, the complex behavioral patterns of EV users, and the need for effective carbon emission mitigation. To address these challenges, this paper proposes a novel distribution system planning method based on the carbon pricing optimization mechanism. First, to address the strong randomness and volatility of renewable energy, a prediction model for renewable energy output considering climatic conditions is established to characterize the output features of wind and solar power. Subsequently, a charging station model is constructed based on the behavioral characteristics of electric vehicle users. Then, an optimized carbon trading price mechanism incorporating the carbon price growth rate is introduced into the carbon emission cost accounting. Based on this, a joint planning model for the power and transportation systems is developed, aiming to minimize the total economic cost while accounting for renewable energy integration and electric vehicle charging station deployment. In the case study, the proposed model is validated using the actual operational data of a specific region and a modified IEEE 33-node system, demonstrating the rationality and effectiveness of the model. Full article

This paper addresses the critical need for detailed electricity and peak power demand maps for urban public transportation vehicles. Current approaches often rely on overly general assumptions, leading to considerable errors in specific applications or, conversely, overly specific measurements that limit generalisability. We aim to present a comprehensive data-driven methodology for analysing energy consumption within a large urban agglomeration. The method leverages a unique and extensive set of real-world performance data, collected over two years from onboard recorders on all public bus lines in the Capital City of Warsaw. This large dataset enables a robust probabilistic analysis, ensuring high accuracy of the results. For this study, three representative bus lines were selected. The approach involves isolating inter-stop trips, for which instantaneous power waveforms and energy consumption are determined using classical mathematical models of vehicle drive systems. The extracted data for these sections is then characterised using probability distributions. This methodology provides accurate calculation results for specific operating conditions and allows for generalisation with additional factors like air conditioning or heating. The direct result of this paper is a detailed urban map of energy demand and peak power for public transport vehicles. Such a map is invaluable for planning new traffic routes, verifying existing ones regarding energy consumption, and providing a reliable input source for strategic charger deployment analysis along the route. Full article

In the future, higher penetrations of electrical loads in low-voltage distribution grids are to be expected. To prevent grid overload, a possible solution is coordination of controllable loads. Typical examples might be charging of electric vehicles or operation of electric heat pumps. Such loads are associated with specific requirements that should be fulfilled if possible. However, at the same time, a safe grid operation must be ensured. To this end, a corresponding optimal power flow optimization problem might be formulated and solved. This article gives a comprehensive review of the state of the art of optimal power flow formulations. It is investigated which constraint handling techniques are used and how hyper parameters are tuned when solving optimal power flow problems using metaheuristic solvers and how controllable loads and fluctuating renewable production are incorporated into optimal power flow formulations. Therefore, the literature is reviewed for pre-defined criteria. The results show possible gaps to be filled with future research: extended optimal power flow formulations to account for controllable loads, investigation of effects of choosing constraint handling techniques or hyper parameter tuning on the performance of the metaheuristic solver and automated methods for determining optimal values for hyper parameters. Full article

The integration of Distributed Energy Resources (DERs) such as photovoltaic (PV) systems, battery energy storage systems (BESSs), and electric vehicles (EVs) introduces new challenges to distribution networks despite offering opportunities for decarbonization and grid flexibility. This paper proposes a scalable simulation-based framework that combines deterministic nodal hosting capacity analysis with probabilistic Monte Carlo simulations to evaluate and optimize DER integration in diverse feeder types. The methodology is demonstrated using the IEEE 13-bus and 123-bus test systems under full-year time-series simulations. Deterministic hosting capacity analysis shows that individual nodes can accommodate up to 76% of base load from PV sources, while Monte Carlo analysis reveals a network-wide average hosting capacity of 62%. Uncoordinated DER deployment leads to increased system losses, overvoltages, and thermal overloads. In contrast, coordinated integration achieves up to 38.7% reduction in power losses, 25% peak demand shaving, and voltage improvements from 0.928 p.u. to 0.971 p.u. Additionally, congestion-centric optimization reduces thermal overload indices by up to 65%. This framework aids utilities and policymakers in making informed decisions on DER planning by capturing both spatial and stochastic constraints. Its modular design allows for flexible adaptation across feeder scales and configurations. The results highlight the need for node-specific deployment strategies and probabilistic validation to ensure reliable, efficient DER integration. Future work will incorporate optimization-driven control and hardware-in-the-loop testing to support real-time implementation. Full article

This study addresses the critical challenges of conductor structure fusing, thermal management failure, and thermal runaway risks in lithium-ion batteries under extreme high-amperage discharge conditions. By integrating theoretical analysis, multiphysics coupling simulations, and experimental validation, the research systematically investigates the overcurrent capability of lithium battery conductor structures. A novel current–thermal structure coupled finite element model was developed to analyze the dynamic relationship between key parameters, specifically overcurrent cross-sectional area and contact area, and their influence on temperature gradient distribution. Experimental results confirm the model’s accuracy, revealing that under extreme high-amperage conditions, increasing the conductor cross-sectional area by 50% only marginally extends the battery’s current-carrying duration from 0.75 s to 0.8 s. This limited enhancement is attributed to rapid heat generation, which restricts the effectiveness of increasing the cross-sectional area alone. Instead, optimizing the conductor structure by modifying the heat conduction path, which involves a similar increase in the cross-sectional area and an additional 60% increase in contact area through the addition of a welding reinforcement structure, achieves thermal equilibrium. The optimized design achieves a current-carrying duration of 1.73 s, which is 230% of the duration of the traditional configuration. This work establishes a scalable framework for enhancing the thermal–electrical performance of lithium-ion batteries, providing a theoretical foundation for structural optimization and offering significant methodological support for advancing research in high-power battery design, with potential applications in electric vehicles, renewable energy systems, and industrial robotics. Full article

Understanding the sensitivity of vehicle energy consumption to average speed variations is critical for accurately assessing the environmental impacts of urban transportation systems. While the energy consumption patterns of conventional vehicles (CVs) have been extensively studied, the response characteristics of electric vehicles (EVs) and their fundamental differences from CVs remain insufficiently explored. This knowledge gap may lead to misguided policy interventions—for instance, implementing congestion mitigation strategies that may paradoxically increase EV energy demand. To address this research gap, we developed an energy consumption model based on vehicle-specific power (VSP) distribution analysis, calibrated with over 25 million second-by-second driving records from Beijing. The proposed comparative framework systematically evaluates the sensitivity of EV and CV energy consumption across different speed regimes. The results indicated that EV energy use exhibits a distinctive parabolic trend, with high energy use at both low and high speeds, and a notable increase beyond approximately 70 km/h. A case study indicates that, during the pandemic lockdown, which led to a significant increase in average speed, CV energy use generally decreased, whereas EV energy consumption increased. This discrepancy is primarily attributed to differences in energy consumption rates rather than variations in driving behavior, as reflected in VSP distributions. Full article

Real-time 3D object detection is a cornerstone for the safe operation of drones and autonomous vehicles (AVs)—drones must avoid millimeter-scale power lines in cluttered airspace, while AVs require instantaneous recognition of pedestrians and vehicles in dynamic urban environments. Although significant progress has been made in detection methods based on point clouds, cameras, and multimodal fusion, the computational complexity of existing high-precision models struggles to meet the real-time requirements of vehicular edge devices. Additionally, during the model lightweighting process, issues such as multimodal feature coupling failure and the imbalance between classification and localization performance often arise. To address these challenges, this paper proposes a knowledge distillation framework for multimodal 3D object detection, incorporating attention guidance, rank-aware learning, and interactive feature supervision to achieve efficient model compression and performance optimization. Specifically: To enhance the student model’s ability to focus on key channel and spatial features, we introduce attention-guided feature distillation, leveraging a bird’s-eye view foreground mask and a dual-attention mechanism. To mitigate the degradation of classification performance when transitioning from two-stage to single-stage detectors, we propose ranking-aware category distillation by modeling anchor-level distribution. To address the insufficient cross-modal feature extraction capability, we enhance the student network’s image features using the teacher network’s point cloud spatial priors, thereby constructing a LiDAR-image cross-modal feature alignment mechanism. Experimental results demonstrate the effectiveness of the proposed approach in multimodal 3D object detection. On the KITTI dataset, our method improves network performance by 4.89% even after reducing the number of channels by half. Full article

This study demonstrates that small-scale liquefied natural gas (SS LNG) is a viable and cost-effective alternative to High-Speed Diesel (HSD) for power generation in remote areas of Indonesia. An integrated supply chain model is developed to optimize total costs based on LNG inventory levels. The model minimizes transportation costs from supply depots to demand points and handling costs at receiving terminals, which utilize Floating Storage Regasification Units (FSRUs). LNG distribution is optimized using a Multi-Depot Capacitated Vehicle Routing Problem (MDCVRP), formulated as a Mixed Integer Linear Programming (MILP) problem to reduce fuel consumption, CO₂ emissions, and vessel rental expenses. The novelty of this research lies in its integrated cost optimization, combining transportation and handling within a model specifically adapted to Indonesia’s complex geography and infrastructure. The simulation involves four LNG plant supply nodes and 50 demand locations, serving a total demand of 15,528 m³/day across four clusters. The analysis estimates a total investment of USD 685.3 million, with a plant-gate LNG price of 10.35 to 11.28 USD/MMBTU at a 10 percent discount rate, representing a 55 to 60 percent cost reduction compared to HSD. These findings support the strategic deployment of SS LNG to expand affordable electricity access in remote and underserved regions. Full article

Plug-in hybrid electric vehicles (PHEVs) typically employ batteries with relatively small capacities due to constraints on chassis space and vehicle cost. Consequently, under conditions such as acceleration and hill climbing, these vehicles often experience high-current battery discharges, which can significantly compromise the battery’s lifespan. To address this issue, this paper focuses on a plug-in hybrid passenger vehicle, introducing supercapacitors to form a hybrid energy storage system (HESS) in conjunction with the PHEV battery, and it proposes a dual-layer energy management strategy for PHEVs. First, a PHEV model is developed, and a rule-based energy management strategy is designed. By conducting simulation comparisons of the CLTC under three control rules with different thresholds, the strategy yielding the lowest fuel consumption is selected, and its battery discharge characteristics are analyzed. Subsequently, the required power parameters of the supercapacitor are calculated, and, taking chassis space constraints into account, the number and specifications of the supercapacitors are determined. Subsequently, a dual-layer energy distribution strategy for PHEVs equipped with supercapacitors is proposed. In the upper layer, an equivalent fuel consumption minimization method is applied to optimize the torque distribution between the engine and the motor, while the lower layer employs a rule-based strategy for power allocation between the battery and the supercapacitor. A proportional feedback factor is introduced for the real-time adjustment of the engine and motor torque distribution, and simulations under the CLTC are conducted to evaluate the PHEV’s torque distribution and fuel consumption. The results indicate that the dual-layer energy management strategy reduces the duration of high-current battery discharge in the supercapacitor-equipped PHEV by 73.61%, decreases the peak current by 30.76%, and lowers the overall vehicle fuel consumption by 5%. Unlike other studies, this paper analyzes and calculates the specifications, dimensions, and quantity of supercapacitors based on the available chassis space of the PHEV passenger car. The results demonstrate that the designed supercapacitor array effectively mitigates the high-current discharge of the PHEV battery, and the proposed dual-layer energy management strategy is capable of reducing the overall fuel consumption of the vehicle. Full article

In the context of global energy conservation and emission reduction, electric vehicles (EVs) are essential for low-carbon transport. However, their rapid growth challenges power grids with load imbalances across networks and increases user charging costs. To address the issues of load balancing across large-scale distribution networks and the charging costs for users, this paper proposes an optimization strategy for EV charging behavior based on deep reinforcement learning (DRL). The strategy aims to minimize user charging costs while achieving load balancing across distribution networks. Specifically, the strategy divides the charging process into two stages: charging station selection and in-station charging scheduling. In the first stage, a Load Balancing Matching Strategy (LBMS) is employed to assist users in selecting a charging station. In the second stage, we use the DRL algorithm. In the DRL algorithm, we design a novel reward function that enables charging stations to meet user charging demands while minimizing user charging costs and reducing the load gap among distribution networks. Case study results demonstrate the effectiveness of the proposed strategy in a multi-distribution network environment. Moreover, even when faced with varying levels of EV user participation, the strategy continues to demonstrate strong performance. Full article

In cold environments, traditional independent thermal management systems heavily rely on inefficient Positive Temperature Coefficient (PTC) heaters, which exacerbate range anxiety in vehicles. In this study, an energy management-based control strategy for an integrated thermal management system (ITMS) designed for hybrid electric vehicles (HEVs) is proposed. By coupling the four thermal flow circuits of the entire vehicle and integrating driving modes with heating demands, this strategy achieves full vehicle-level integrated control. Through optimizing the distribution and utilization of heat within the vehicle, this enhances the heating performance of the air source heat pump. The simulation results demonstrate that the proposed strategy significantly reduces the power consumption of the heat pump and improves heating efficiency for both the battery and the cabin. By utilizing waste heat from the motor and the engine, the ITMS increases the heating capacity of the heat pump, particularly in low-temperature environments. Compared to traditional thermal management systems, the ITMS control strategy achieves substantial improvements in both heating time and energy efficiency. Specifically, the system reduces battery heating time by 55.94% and enhances the overall heating performance of the vehicle. Furthermore, the strategy reduces fuel consumption by 5.18%, demonstrating its potential to improve the energy efficiency of HEVs in cold climates. Full article