Ammonia(NH3)has been widely recognized as a key precursor of atmospheric secondary aerosol formation.Vehicle emission is a major source of urban atmospheric NH3.With the tightening of emission standards and the growin...Ammonia(NH3)has been widely recognized as a key precursor of atmospheric secondary aerosol formation.Vehicle emission is a major source of urban atmospheric NH3.With the tightening of emission standards and the growing trend of vehicle fleet electrification,it is imperative to update the emission factors for NH3 from real-world on-road fleets.In this study,a tunnel measurement was conducted in the urban area of Tianjin,China.The fleet-average NH3 emission factor(EF)was 11.2 mg/(km·veh),significantly lower than those in previous studies,showing the benefit of emission standard updating.Through a multiple linear regression analysis,the EFs of light-duty gasoline vehicles,light-duty diesel vehicles,and heavy-duty diesel vehicles(HDDVs)were estimated to be 5.7±0.6 mg/(km·veh),40.8±5.1 mg/(km·veh),and 160.2±16.6 mg/(km·veh),respectively.Based on the results from this study,we found that HDDVs,which comprise<3%of the total vehicles may contribute approximately 22%of total NH3 emissions in Tianjin.Our results highlight NH3 emissions from HDDVs,a previously potentially overlooked source of NH3 emissions in urban areas.The actual on-road NH3 emissions from HDDVs may exceed current expectations,posing a growing concern for the future.展开更多
Rapid evolutions of the Internet of Electric Vehicles(IoEVs)are reshaping and modernizing transport systems,yet challenges remain in energy efficiency,better battery aging,and grid stability.Typical charging methods a...Rapid evolutions of the Internet of Electric Vehicles(IoEVs)are reshaping and modernizing transport systems,yet challenges remain in energy efficiency,better battery aging,and grid stability.Typical charging methods allow for EVs to be charged without thought being given to the condition of the battery or the grid demand,thus increasing energy costs and battery aging.This study proposes a smart charging station with an AI-powered Battery Management System(BMS),developed and simulated in MATLAB/Simulink,to increase optimality in energy flow,battery health,and impractical scheduling within the IoEV environment.The system operates through real-time communication,load scheduling based on priorities,and adaptive charging based on batterymathematically computed State of Charge(SOC),State of Health(SOH),and thermal state,with bidirectional power flow(V2G),thus allowing EVs’participation towards grid stabilization.Simulation results revealed that the proposed model can reduce peak grid load by 37.8%;charging efficiency is enhanced by 92.6%;battery temperature lessened by 4.4℃;SOH extended over 100 cycles by 6.5%,if compared against the conventional technique.By this way,charging time was decreased by 12.4% and energy costs dropped by more than 20%.These results showed that smart charging with intelligent BMS can boost greatly the operational efficiency and sustainability of the IoEV ecosystem.展开更多
Rapidly-exploring Random Tree(RRT)and its variants have become foundational in path-planning research,yet in complex three-dimensional off-road environments their uniform blind sampling and limited safety guarantees l...Rapidly-exploring Random Tree(RRT)and its variants have become foundational in path-planning research,yet in complex three-dimensional off-road environments their uniform blind sampling and limited safety guarantees lead to slow convergence and force an unfavorable trade-off between path quality and traversal safety.To address these challenges,we introduce HS-APF-RRT*,a novel algorithm that fuses layered sampling,an enhanced Artificial Potential Field(APF),and a dynamic neighborhood-expansion mechanism.First,the workspace is hierarchically partitioned into macro,meso,and micro sampling layers,progressively biasing random samples toward safer,lower-energy regions.Second,we augment the traditional APF by incorporating a slope-dependent repulsive term,enabling stronger avoidance of steep obstacles.Third,a dynamic expansion strategy adaptively switches between 8 and 16 connected neighborhoods based on local obstacle density,striking an effective balance between search efficiency and collision-avoidance precision.In simulated off-road scenarios,HS-APF-RRT*is benchmarked against RRT*,GoalBiased RRT*,and APF-RRT*,and demonstrates significantly faster convergence,lower path-energy consumption,and enhanced safety margins.展开更多
Amphibious vehicles are more prone to attitude instability compared to ships,making it crucial to develop effective methods for monitoring instability risks.However,large inclination events,which can lead to instabili...Amphibious vehicles are more prone to attitude instability compared to ships,making it crucial to develop effective methods for monitoring instability risks.However,large inclination events,which can lead to instability,occur frequently in both experimental and operational data.This infrequency causes events to be overlooked by existing prediction models,which lack the precision to accurately predict inclination attitudes in amphibious vehicles.To address this gap in predicting attitudes near extreme inclination points,this study introduces a novel loss function,termed generalized extreme value loss.Subsequently,a deep learning model for improved waterborne attitude prediction,termed iInformer,was developed using a Transformer-based approach.During the embedding phase,a text prototype is created based on the vehicle’s operation log data is constructed to help the model better understand the vehicle’s operating environment.Data segmentation techniques are used to highlight local data variation features.Furthermore,to mitigate issues related to poor convergence and slow training speeds caused by the extreme value loss function,a teacher forcing mechanism is integrated into the model,enhancing its convergence capabilities.Experimental results validate the effectiveness of the proposed method,demonstrating its ability to handle data imbalance challenges.Specifically,the model achieves over a 60%improvement in root mean square error under extreme value conditions,with significant improvements observed across additional metrics.展开更多
To achieve low-carbon regulation of electric vehicle(EV)charging loads under the“dual carbon”goals,this paper proposes a coordinated scheduling strategy that integrates dynamic carbon factor prediction and multiobje...To achieve low-carbon regulation of electric vehicle(EV)charging loads under the“dual carbon”goals,this paper proposes a coordinated scheduling strategy that integrates dynamic carbon factor prediction and multiobjective optimization.First,a dual-convolution enhanced improved Crossformer prediction model is constructed,which employs parallel 1×1 global and 3×3 local convolutionmodules(Integrated Convolution Block,ICB)formultiscale feature extraction,combinedwith anAdaptive Spectral Block(ASB)to enhance time-series fluctuationmodeling.Based on high-precision predictions,a carbon-electricity cost joint optimization model is further designed to balance economic,environmental,and grid-friendly objectives.The model’s superiority was validated through a case study using real-world data from a renewable-heavy grid.Simulation results show that the proposed multi-objective strategy demonstrated a superior balance compared to baseline and benchmark models,achieving a 15.8%reduction in carbon emissions and a 5.2%reduction in economic costs,while still providing a substantial 22.2%reduction in the peak-valley difference.Its balanced performance significantly outperformed both a single-objective strategy and a state-of-the-art Model Predictive Control(MPC)benchmark,highlighting the advantage of a global optimization approach.This study provides theoretical and technical pathways for dynamic carbon factor-driven EV charging optimization.展开更多
As urban landscapes evolve and vehicular volumes soar,traditional traffic monitoring systems struggle to scale,often failing under the complexities of dense,dynamic,and occluded environments.This paper introduces a no...As urban landscapes evolve and vehicular volumes soar,traditional traffic monitoring systems struggle to scale,often failing under the complexities of dense,dynamic,and occluded environments.This paper introduces a novel,unified deep learning framework for vehicle detection,tracking,counting,and classification in aerial imagery designed explicitly for modern smart city infrastructure demands.Our approach begins with adaptive histogram equalization to optimize aerial image clarity,followed by a cutting-edge scene parsing technique using Mask2Former,enabling robust segmentation even in visually congested settings.Vehicle detection leverages the latest YOLOv11 architecture,delivering superior accuracy in aerial contexts by addressing occlusion,scale variance,and fine-grained object differentiation.We incorporate the highly efficient ByteTrack algorithm for tracking,enabling seamless identity preservation across frames.Vehicle counting is achieved through an unsupervised DBSCAN-based method,ensuring adaptability to varying traffic densities.We further introduce a hybrid feature extraction module combining Convolutional Neural Networks(CNNs)with Zernike Moments,capturing both deep semantic and geometric signatures of vehicles.The final classification is powered by NASNet,a neural architecture search-optimized model,ensuring high accuracy across diverse vehicle types and orientations.Extensive evaluations of the VAID benchmark dataset demonstrate the system’s outstanding performance,achieving 96%detection,94%tracking,and 96.4%classification accuracy.On the UAVDT dataset,the system attains 95%detection,93%tracking,and 95%classification accuracy,confirming its robustness across diverse aerial traffic scenarios.These results establish new benchmarks in aerial traffic analysis and validate the framework’s scalability,making it a powerful and adaptable solution for next-generation intelligent transportation systems and urban surveillance.展开更多
Traffic at urban intersections frequently encounters unexpected obstructions,resulting in congestion due to uncooperative and priority-based driving behavior.This paper presents an optimal right-turn coordination syst...Traffic at urban intersections frequently encounters unexpected obstructions,resulting in congestion due to uncooperative and priority-based driving behavior.This paper presents an optimal right-turn coordination system for Connected and Automated Vehicles(CAVs)at single-lane intersections,particularly in the context of left-hand side driving on roads.The goal is to facilitate smooth right turns for certain vehicles without creating bottlenecks.We consider that all approaching vehicles share relevant information through vehicular communications.The Intersection Coordination Unit(ICU)processes this information and communicates the optimal crossing or turning times to the vehicles.The primary objective of this coordination is to minimize overall traffic delays,which also helps improve the fuel consumption of vehicles.By considering information from upcoming vehicles at the intersection,the coordination system solves an optimization problem to determine the best timing for executing right turns,ultimately minimizing the total delay for all vehicles.The proposed coordination system is evaluated at a typical urban intersection,and its performance is compared to traditional traffic systems.Numerical simulation results indicate that the proposed coordination system significantly enhances the average traffic speed and fuel consumption compared to the traditional traffic system in various scenarios.展开更多
Vehicle Edge Computing(VEC)and Cloud Computing(CC)significantly enhance the processing efficiency of delay-sensitive and computation-intensive applications by offloading compute-intensive tasks from resource-constrain...Vehicle Edge Computing(VEC)and Cloud Computing(CC)significantly enhance the processing efficiency of delay-sensitive and computation-intensive applications by offloading compute-intensive tasks from resource-constrained onboard devices to nearby Roadside Unit(RSU),thereby achieving lower delay and energy consumption.However,due to the limited storage capacity and energy budget of RSUs,it is challenging to meet the demands of the highly dynamic Internet of Vehicles(IoV)environment.Therefore,determining reasonable service caching and computation offloading strategies is crucial.To address this,this paper proposes a joint service caching scheme for cloud-edge collaborative IoV computation offloading.By modeling the dynamic optimization problem using Markov Decision Processes(MDP),the scheme jointly optimizes task delay,energy consumption,load balancing,and privacy entropy to achieve better quality of service.Additionally,a dynamic adaptive multi-objective deep reinforcement learning algorithm is proposed.Each Double Deep Q-Network(DDQN)agent obtains rewards for different objectives based on distinct reward functions and dynamically updates the objective weights by learning the value changes between objectives using Radial Basis Function Networks(RBFN),thereby efficiently approximating the Pareto-optimal decisions for multiple objectives.Extensive experiments demonstrate that the proposed algorithm can better coordinate the three-tier computing resources of cloud,edge,and vehicles.Compared to existing algorithms,the proposed method reduces task delay and energy consumption by 10.64%and 5.1%,respectively.展开更多
Traditional automated guided vehicle(AGV)primarily relies on scheduling systems to manage warehouse locations and execute picking or placing tasks on fixedheight pallets.However,these conventional systems are illsuite...Traditional automated guided vehicle(AGV)primarily relies on scheduling systems to manage warehouse locations and execute picking or placing tasks on fixedheight pallets.However,these conventional systems are illsuited for scenarios involving variable heights,such as vehicle loading and unloading or the complex stacking of soft packages.To address the challenges of AGV endeffector operations in nonfixed height scenarios,this paper proposes an innovative solution leveraging lowcost depth camera sensors.By capturing image and depth data,and integrating deep learning,image processing,and spatial attitude calculation techniques,the method accurately determines the position of the endeffector center point relative to the upper plane of the fork.The approach effectively resolves a key issue in AGV operations within intelligent logistics scenarios that lack fixed heights.The proposed algorithm is deployed on a domestic embedded,lowcost ARM chip controller,and extensive experiments are conducted on a real AGV equipped with multiple stacked vehicles and nonstandard vehicles.The experimental results demonstrate that for diverse vehicles with different heights,the measurement error can be maintained within±10 mm,satisfying the requirements for highprecision measurement.The height measurement method developed in the paper not only enhances the AGV’s adaptability in nonfixed height scenarios but also significantly broadens its application potential across various industries.展开更多
The implementation of the standard is expected to help electric vehicle battery swap stations to adapt to diversified needs and vehicle models,promoting the industry’s orderly and healthy development.
The 2025 Shanghai Auto Show reaffirmed its role as one of the world’s most influential automotive industry events,offering a panoramic view of the future shaped by intelligent and electrified vehicles.With over 200 n...The 2025 Shanghai Auto Show reaffirmed its role as one of the world’s most influential automotive industry events,offering a panoramic view of the future shaped by intelligent and electrified vehicles.With over 200 new models on display-85 percent of them new energy vehicles-this year’s show spotlighted how the global auto industry is pivoting rapidly towards an era of software-defined and AI-powered mobility.展开更多
The usage of electric vehicles holds a crucial role in lowering the diminishing of the ozone layer because electric vehicles are not dependent on fossil fuels.With more research,evaluation,and its characteristics on e...The usage of electric vehicles holds a crucial role in lowering the diminishing of the ozone layer because electric vehicles are not dependent on fossil fuels.With more research,evaluation,and its characteristics on electric vehicles,the infrastructure of charging points,production of electric vehicles,and network modelling,this paper provides a comprehensive overview of electric vehicles,and hybrid vehicles,including an analysis of their market growth,as well as different types of optimization used in the current scenario.In developing countries like India,the biggest barrier is their unfulfilled facility over the charging.Without renewable energy sources,vehicle-to-grid technology facilitates the enhancement of additional power requirements.The mobility factor has been considered an important and special characteristic of electric vehicles.展开更多
Aiming at the problem that the existing algorithms for vehicle detection in smart factories are difficult to detect partial occlusion of vehicles,vulnerable to background interference,lack of global vision,and excessi...Aiming at the problem that the existing algorithms for vehicle detection in smart factories are difficult to detect partial occlusion of vehicles,vulnerable to background interference,lack of global vision,and excessive suppression of real targets,which ultimately cause accuracy degradation.At the same time,to facilitate the subsequent positioning of vehicles in the factory,this paper proposes an improved YOLOv8 algorithm.Firstly,the RFCAConv module is combined to improve the original YOLOv8 backbone.Pay attention to the different features in the receptive field,and give priority to the spatial features of the receptive field to capture more vehicle feature information and solve the problem that the vehicle is partially occluded and difficult to detect.Secondly,the SFE module is added to the neck of v8,which improves the saliency of the target in the reasoning process and reduces the influence of background interference on vehicle detection.Finally,the head of the RT-DETR algorithm is used to replace the head in the original YOLOv8 algorithm,which avoids the excessive suppression of the real target while combining the context information.The experimental results show that compared with the original YOLOv8 algorithm,the detection accuracy of the improved YOLOv8 algorithm is improved by 4.6%on the self-made smart factory data set,and the detection speed also meets the real-time requirements of smart factory vehicle detection and subsequent vehicle positioning.展开更多
Dear Editor,This letter proposes a convex optimization-based model predictive control(MPC)autonomous guidance method for the Mars ascent vehicle(MAV).We use the modified chebyshev-picard iteration(MCPI)to solve optimi...Dear Editor,This letter proposes a convex optimization-based model predictive control(MPC)autonomous guidance method for the Mars ascent vehicle(MAV).We use the modified chebyshev-picard iteration(MCPI)to solve optimization sub-problems within the MPC framework,eliminating the dynamic constraints in solving the optimal control problem and enhancing the convergence performance of the algorithm.Moreover,this method can repeatedly perform trajectory optimization calculations at a high frequency,achieving timely correction of the optimal control command.Numerical simulations demonstrate that the method can satisfy the requirements of rapid computation and reliability for the MAV system when considering uncertainties and perturbations.展开更多
This study observes the process of strategy building and capability accumulation of companies in the currently booming Chinese electric vehicles(EV)1 market from the perspective of business ecosystems.While examining ...This study observes the process of strategy building and capability accumulation of companies in the currently booming Chinese electric vehicles(EV)1 market from the perspective of business ecosystems.While examining the internal and external factors of the formation about the Chinese EV industry business ecosystem,such as industrial structure transformation,technology transfer,government policies,and corporate competition,with the platform theory,I analyze the growth strategies and competitiveness of Chinese companies,particularly BYD Co.,Ltd.(BYD),which has risen to the top of the world in EV completed vehicles,and Contemporary Amperex Technology Co.,Ltd.(CATL),which has risen to the top of the world in electric vehicle batteries(EVB)2.BYD and CATL have gained competitive advantages by utilizing the distinctive management resources,which have accumulated over the years to build platforms for EVBs and EVs in response to changes in the external environment,and have actively developed their platform strategies.展开更多
Flapping-Wing Air Vehicles(FWAVs)have been developed to pursue the efficient,agile,and quiet flight of flying animals.However,unlike lightweight FWAVs capable of vertical takeoff,relatively heavy FWAVs face challenges...Flapping-Wing Air Vehicles(FWAVs)have been developed to pursue the efficient,agile,and quiet flight of flying animals.However,unlike lightweight FWAVs capable of vertical takeoff,relatively heavy FWAVs face challenges in self-takeoff,which refers to taking off without both external device and energy input.In this study,a cliff-drop method is implemented for an independent takeoff of a heavy FWAV,relying solely on gravity.In the takeoff process using the cliff-drop method,the FWAV moves on the ground to a cliff edge using a wheel-driving motor and then descends from the cliff to achieve the necessary speed for flight.To demonstrate the cliff-drop method,the KAIST Robotic Hawk(KRoHawk)with a mass of 740 g and a wingspan of 120 cm is developed.The takeoff tests demonstrate that the KRoHawk,significantly heavier than the vertical-takeoff capable FWAVs,can successfully take off using the gravity-assisted takeoff method.The scalability of cliff-drop method is analyzed through simulations.When drop constraints are absent,the wheel-driving motor mass fraction for cliff-drop method remains negligible even as the vehicle's weight increases.When drop constraints are set to 4 m,FWAVs heavier than KRoHawk,weighing up to 4.4 kg,can perform the cliff-drop takeoffs with a wheel-driving motor mass fraction of less than 8%.展开更多
The pressure wave generated by an urban-rail vehicle when passing through a tunnel affects the comfort of passengersand may even cause damage to the train and related tunnel structures.Therefore,controlling the trains...The pressure wave generated by an urban-rail vehicle when passing through a tunnel affects the comfort of passengersand may even cause damage to the train and related tunnel structures.Therefore,controlling the trainspeed in the tunnel is extremely important.In this study,this problem is investigated numerically in the frameworkof the standard k-εtwo-equation turbulence model.In particular,an eight-car urban rail train passingthrough a tunnel at different speeds(140,160,180 and 200 km/h)is considered.The results show that the maximumaerodynamic drag of the head and tail cars is most affected by the running speed.The pressure at selectedmeasuring points on the windward side of the head car is very high,and the negative pressure at the side windowof the driver’s cab of the tail car is also very large.From the head car to the tail car,the pressure at the same heightgradually decreases.The positive pressure peak at the head car and the negative pressure peak at the tail car aregreatly affected by the speed.展开更多
Aiming at the problem of increasing the peak-to-valley difference of grid load and the rising cost of user charging caused by the disorderly charging of large-scale electric vehicles,this paper proposes a coordinated ...Aiming at the problem of increasing the peak-to-valley difference of grid load and the rising cost of user charging caused by the disorderly charging of large-scale electric vehicles,this paper proposes a coordinated charging scheduling strategy for multiple types of electric vehicles based on the degree of urgency of vehicle use.First,considering the range loss characteristics,dynamic time-sharing tariff mechanism,and user incentive policy in the lowtemperature environment of northern winter,a differentiated charging model is constructed for four types of vehicles:family cars,official cars,buses,and cabs.Then,we innovatively introduce the urgency parameter of charging demand for multiple types of vehicles and dynamically divide the emergency and non-emergency charging modes according to the difference between the regular charging capacity and the user’s minimum power demand.When the conventional charging capacity is less than the minimum power demand of the vehicle within the specified time,it is the emergency vehicle demand,and this type of vehicle is immediately charged in fast charging mode after connecting to the grid.On the contrary,it is a non-emergency demand,and the vehicle is connected to the grid to choose the appropriate time to charge in conventional charging mode.Finally,by optimizing the objective function to minimize the peakto-valley difference between the grid and the vehicle owner’s charging cost,and designing the charging continuity constraints to avoid battery damage,it ensures that the vehicle is efficiently dispatched under the premise of meeting the minimum power demand.Simulation results show that the proposed charging strategy can reduce the charging cost of vehicle owners by 26.33%,reduce the peak-to-valley difference rate of the grid by 29.8%,and significantly alleviate the congestion problem during peak load hours,compared with the disordered charging mode,while ensuring that the electric vehicles are not overcharged and meet the electricity demand of vehicle owners.This paper solves the problems of the existing research on the singularity of vehicle models and the lack of environmental adaptability and provides both economic and practical solutions for the cooperative optimization of electric vehicles and power grids in multiple scenarios.展开更多
The design of wide-range high-efficiency aerodynamic configurations is one of the most important key technologies in the research of near-space hypersonic vehicles.A double-sided intake configuration with different in...The design of wide-range high-efficiency aerodynamic configurations is one of the most important key technologies in the research of near-space hypersonic vehicles.A double-sided intake configuration with different inlets on the upper and lower surfaces is proposed to adapt to widerange flight.Firstly,the double-sided intake configuration’s design method and flight profile are delineated.Secondly,Computational Fluid Dynamics(CFD)numerical simulation based on multi-Graphics Processing Unit(GPU)parallel computing is adopted to evaluate the vehicle’s performance comprehensively,aiming to verify the feasibility of the proposed scheme.This evaluation encompasses a wide-range basic aerodynamic characteristics,inlet performance,and heat flux at critical locations.The results show that the inlets of the designed integration configuration can start up across Mach number 3.5 to 8.The vehicle possesses multi-point cruising capability by flipping the fuselage.Simultaneously,a 180°rotation of the fuselage can significantly decrease the heat accumulation on the lower surface of the vehicle,particularly at the inlet lip,further decreasing the temperature gradient across the vehicle structure.This study has some engineering value for the aerodynamic configuration design of wide-range vehicles.However,further study reveals that the flow phenomena at the intersection of two inlets are complex,posing potential adverse impacts on propulsion efficiency.Therefore,it is imperative to conduct additional research to delve into this matter comprehensively.展开更多
In Internet of Vehicles,VehicleInfrastructure-Cloud cooperation supports diverse intelligent driving and intelligent transportation applications.Federated Learning(FL)is the emerging computation paradigm to provide ef...In Internet of Vehicles,VehicleInfrastructure-Cloud cooperation supports diverse intelligent driving and intelligent transportation applications.Federated Learning(FL)is the emerging computation paradigm to provide efficient and privacypreserving collaborative learning.However,in Io V environment,federated learning faces the challenges introduced by high mobility of vehicles and nonIndependently Identically Distribution(non-IID)of data.High mobility causes FL clients quit and the communication offline.The non-IID data leads to slow and unstable convergence of global model and single global model's weak adaptability to clients with different localization characteristics.Accordingly,this paper proposes a personalized aggregation strategy for hierarchical Federated Learning in Io V environment,including Fed SA(Special Asynchronous Federated Learning with Self-adaptive Aggregation)for low-level FL between a Road Side Unit(RSU)and the vehicles within its coverage,and Fed Att(Federated Learning with Attention Mechanism)for high-level FL between a cloud server and multiple RSUs.Agents self-adaptively obtain model aggregation weight based on Advantage Actor-Critic(A2C)algorithm.Experiments show the proposed strategy encourages vehicles to participate in global aggregation,and outperforms existing methods in training performance.展开更多
基金supported by the National key research and development program of China(No.2022YFE0135000)the National Natural Science Foundation of China(No.42175123)the Natural Science Foundation of Tianjin(No.23JCJQJC00170).
文摘Ammonia(NH3)has been widely recognized as a key precursor of atmospheric secondary aerosol formation.Vehicle emission is a major source of urban atmospheric NH3.With the tightening of emission standards and the growing trend of vehicle fleet electrification,it is imperative to update the emission factors for NH3 from real-world on-road fleets.In this study,a tunnel measurement was conducted in the urban area of Tianjin,China.The fleet-average NH3 emission factor(EF)was 11.2 mg/(km·veh),significantly lower than those in previous studies,showing the benefit of emission standard updating.Through a multiple linear regression analysis,the EFs of light-duty gasoline vehicles,light-duty diesel vehicles,and heavy-duty diesel vehicles(HDDVs)were estimated to be 5.7±0.6 mg/(km·veh),40.8±5.1 mg/(km·veh),and 160.2±16.6 mg/(km·veh),respectively.Based on the results from this study,we found that HDDVs,which comprise<3%of the total vehicles may contribute approximately 22%of total NH3 emissions in Tianjin.Our results highlight NH3 emissions from HDDVs,a previously potentially overlooked source of NH3 emissions in urban areas.The actual on-road NH3 emissions from HDDVs may exceed current expectations,posing a growing concern for the future.
文摘Rapid evolutions of the Internet of Electric Vehicles(IoEVs)are reshaping and modernizing transport systems,yet challenges remain in energy efficiency,better battery aging,and grid stability.Typical charging methods allow for EVs to be charged without thought being given to the condition of the battery or the grid demand,thus increasing energy costs and battery aging.This study proposes a smart charging station with an AI-powered Battery Management System(BMS),developed and simulated in MATLAB/Simulink,to increase optimality in energy flow,battery health,and impractical scheduling within the IoEV environment.The system operates through real-time communication,load scheduling based on priorities,and adaptive charging based on batterymathematically computed State of Charge(SOC),State of Health(SOH),and thermal state,with bidirectional power flow(V2G),thus allowing EVs’participation towards grid stabilization.Simulation results revealed that the proposed model can reduce peak grid load by 37.8%;charging efficiency is enhanced by 92.6%;battery temperature lessened by 4.4℃;SOH extended over 100 cycles by 6.5%,if compared against the conventional technique.By this way,charging time was decreased by 12.4% and energy costs dropped by more than 20%.These results showed that smart charging with intelligent BMS can boost greatly the operational efficiency and sustainability of the IoEV ecosystem.
基金supported in part by 14th Five Year National Key R&D Program Project(Project Number:2023YFB3211001)the National Natural Science Foundation of China(62273339,U24A201397).
文摘Rapidly-exploring Random Tree(RRT)and its variants have become foundational in path-planning research,yet in complex three-dimensional off-road environments their uniform blind sampling and limited safety guarantees lead to slow convergence and force an unfavorable trade-off between path quality and traversal safety.To address these challenges,we introduce HS-APF-RRT*,a novel algorithm that fuses layered sampling,an enhanced Artificial Potential Field(APF),and a dynamic neighborhood-expansion mechanism.First,the workspace is hierarchically partitioned into macro,meso,and micro sampling layers,progressively biasing random samples toward safer,lower-energy regions.Second,we augment the traditional APF by incorporating a slope-dependent repulsive term,enabling stronger avoidance of steep obstacles.Third,a dynamic expansion strategy adaptively switches between 8 and 16 connected neighborhoods based on local obstacle density,striking an effective balance between search efficiency and collision-avoidance precision.In simulated off-road scenarios,HS-APF-RRT*is benchmarked against RRT*,GoalBiased RRT*,and APF-RRT*,and demonstrates significantly faster convergence,lower path-energy consumption,and enhanced safety margins.
基金Supported by the National Defense Basic Scientific Research Program of China.
文摘Amphibious vehicles are more prone to attitude instability compared to ships,making it crucial to develop effective methods for monitoring instability risks.However,large inclination events,which can lead to instability,occur frequently in both experimental and operational data.This infrequency causes events to be overlooked by existing prediction models,which lack the precision to accurately predict inclination attitudes in amphibious vehicles.To address this gap in predicting attitudes near extreme inclination points,this study introduces a novel loss function,termed generalized extreme value loss.Subsequently,a deep learning model for improved waterborne attitude prediction,termed iInformer,was developed using a Transformer-based approach.During the embedding phase,a text prototype is created based on the vehicle’s operation log data is constructed to help the model better understand the vehicle’s operating environment.Data segmentation techniques are used to highlight local data variation features.Furthermore,to mitigate issues related to poor convergence and slow training speeds caused by the extreme value loss function,a teacher forcing mechanism is integrated into the model,enhancing its convergence capabilities.Experimental results validate the effectiveness of the proposed method,demonstrating its ability to handle data imbalance challenges.Specifically,the model achieves over a 60%improvement in root mean square error under extreme value conditions,with significant improvements observed across additional metrics.
基金Supported by State Grid Corporation of China Science and Technology Project:Research on Key Technologies for Intelligent Carbon Metrology in Vehicle-to-Grid Interaction(Project Number:B3018524000Q).
文摘To achieve low-carbon regulation of electric vehicle(EV)charging loads under the“dual carbon”goals,this paper proposes a coordinated scheduling strategy that integrates dynamic carbon factor prediction and multiobjective optimization.First,a dual-convolution enhanced improved Crossformer prediction model is constructed,which employs parallel 1×1 global and 3×3 local convolutionmodules(Integrated Convolution Block,ICB)formultiscale feature extraction,combinedwith anAdaptive Spectral Block(ASB)to enhance time-series fluctuationmodeling.Based on high-precision predictions,a carbon-electricity cost joint optimization model is further designed to balance economic,environmental,and grid-friendly objectives.The model’s superiority was validated through a case study using real-world data from a renewable-heavy grid.Simulation results show that the proposed multi-objective strategy demonstrated a superior balance compared to baseline and benchmark models,achieving a 15.8%reduction in carbon emissions and a 5.2%reduction in economic costs,while still providing a substantial 22.2%reduction in the peak-valley difference.Its balanced performance significantly outperformed both a single-objective strategy and a state-of-the-art Model Predictive Control(MPC)benchmark,highlighting the advantage of a global optimization approach.This study provides theoretical and technical pathways for dynamic carbon factor-driven EV charging optimization.
基金funded by the Open Access Initiative of the University of Bremen and the DFG via SuUB BremenThe authors extend their appreciation to the Deanship of Research and Graduate Studies at King Khalid University for funding this work through Large Group Project under grant number(RGP2/367/46)+1 种基金This research is supported and funded by Princess Nourah bint Abdulrahman University Researchers Supporting Project number(PNURSP2025R410)Princess Nourah bint Abdulrahman University,Riyadh,Saudi Arabia.
文摘As urban landscapes evolve and vehicular volumes soar,traditional traffic monitoring systems struggle to scale,often failing under the complexities of dense,dynamic,and occluded environments.This paper introduces a novel,unified deep learning framework for vehicle detection,tracking,counting,and classification in aerial imagery designed explicitly for modern smart city infrastructure demands.Our approach begins with adaptive histogram equalization to optimize aerial image clarity,followed by a cutting-edge scene parsing technique using Mask2Former,enabling robust segmentation even in visually congested settings.Vehicle detection leverages the latest YOLOv11 architecture,delivering superior accuracy in aerial contexts by addressing occlusion,scale variance,and fine-grained object differentiation.We incorporate the highly efficient ByteTrack algorithm for tracking,enabling seamless identity preservation across frames.Vehicle counting is achieved through an unsupervised DBSCAN-based method,ensuring adaptability to varying traffic densities.We further introduce a hybrid feature extraction module combining Convolutional Neural Networks(CNNs)with Zernike Moments,capturing both deep semantic and geometric signatures of vehicles.The final classification is powered by NASNet,a neural architecture search-optimized model,ensuring high accuracy across diverse vehicle types and orientations.Extensive evaluations of the VAID benchmark dataset demonstrate the system’s outstanding performance,achieving 96%detection,94%tracking,and 96.4%classification accuracy.On the UAVDT dataset,the system attains 95%detection,93%tracking,and 95%classification accuracy,confirming its robustness across diverse aerial traffic scenarios.These results establish new benchmarks in aerial traffic analysis and validate the framework’s scalability,making it a powerful and adaptable solution for next-generation intelligent transportation systems and urban surveillance.
基金supported by the Japan Society for the Promotion of Science(JSPS)Grants-in-Aid for Scientific Research(C)23K03898.
文摘Traffic at urban intersections frequently encounters unexpected obstructions,resulting in congestion due to uncooperative and priority-based driving behavior.This paper presents an optimal right-turn coordination system for Connected and Automated Vehicles(CAVs)at single-lane intersections,particularly in the context of left-hand side driving on roads.The goal is to facilitate smooth right turns for certain vehicles without creating bottlenecks.We consider that all approaching vehicles share relevant information through vehicular communications.The Intersection Coordination Unit(ICU)processes this information and communicates the optimal crossing or turning times to the vehicles.The primary objective of this coordination is to minimize overall traffic delays,which also helps improve the fuel consumption of vehicles.By considering information from upcoming vehicles at the intersection,the coordination system solves an optimization problem to determine the best timing for executing right turns,ultimately minimizing the total delay for all vehicles.The proposed coordination system is evaluated at a typical urban intersection,and its performance is compared to traditional traffic systems.Numerical simulation results indicate that the proposed coordination system significantly enhances the average traffic speed and fuel consumption compared to the traditional traffic system in various scenarios.
基金supported by Key Science and Technology Program of Henan Province,China(Grant Nos.242102210147,242102210027)Fujian Province Young and Middle aged Teacher Education Research Project(Science and Technology Category)(No.JZ240101)(Corresponding author:Dong Yuan).
文摘Vehicle Edge Computing(VEC)and Cloud Computing(CC)significantly enhance the processing efficiency of delay-sensitive and computation-intensive applications by offloading compute-intensive tasks from resource-constrained onboard devices to nearby Roadside Unit(RSU),thereby achieving lower delay and energy consumption.However,due to the limited storage capacity and energy budget of RSUs,it is challenging to meet the demands of the highly dynamic Internet of Vehicles(IoV)environment.Therefore,determining reasonable service caching and computation offloading strategies is crucial.To address this,this paper proposes a joint service caching scheme for cloud-edge collaborative IoV computation offloading.By modeling the dynamic optimization problem using Markov Decision Processes(MDP),the scheme jointly optimizes task delay,energy consumption,load balancing,and privacy entropy to achieve better quality of service.Additionally,a dynamic adaptive multi-objective deep reinforcement learning algorithm is proposed.Each Double Deep Q-Network(DDQN)agent obtains rewards for different objectives based on distinct reward functions and dynamically updates the objective weights by learning the value changes between objectives using Radial Basis Function Networks(RBFN),thereby efficiently approximating the Pareto-optimal decisions for multiple objectives.Extensive experiments demonstrate that the proposed algorithm can better coordinate the three-tier computing resources of cloud,edge,and vehicles.Compared to existing algorithms,the proposed method reduces task delay and energy consumption by 10.64%and 5.1%,respectively.
基金Supported by the Key Research and Development Program of Anhui Province(No.201904a05020035)the Postdoctoral Research Initiative of Anhui Province(No.2024B804)the Hefei City Key Technology Research and Development‘Ranking’(No.2023SGJ017).
文摘Traditional automated guided vehicle(AGV)primarily relies on scheduling systems to manage warehouse locations and execute picking or placing tasks on fixedheight pallets.However,these conventional systems are illsuited for scenarios involving variable heights,such as vehicle loading and unloading or the complex stacking of soft packages.To address the challenges of AGV endeffector operations in nonfixed height scenarios,this paper proposes an innovative solution leveraging lowcost depth camera sensors.By capturing image and depth data,and integrating deep learning,image processing,and spatial attitude calculation techniques,the method accurately determines the position of the endeffector center point relative to the upper plane of the fork.The approach effectively resolves a key issue in AGV operations within intelligent logistics scenarios that lack fixed heights.The proposed algorithm is deployed on a domestic embedded,lowcost ARM chip controller,and extensive experiments are conducted on a real AGV equipped with multiple stacked vehicles and nonstandard vehicles.The experimental results demonstrate that for diverse vehicles with different heights,the measurement error can be maintained within±10 mm,satisfying the requirements for highprecision measurement.The height measurement method developed in the paper not only enhances the AGV’s adaptability in nonfixed height scenarios but also significantly broadens its application potential across various industries.
文摘The implementation of the standard is expected to help electric vehicle battery swap stations to adapt to diversified needs and vehicle models,promoting the industry’s orderly and healthy development.
文摘The 2025 Shanghai Auto Show reaffirmed its role as one of the world’s most influential automotive industry events,offering a panoramic view of the future shaped by intelligent and electrified vehicles.With over 200 new models on display-85 percent of them new energy vehicles-this year’s show spotlighted how the global auto industry is pivoting rapidly towards an era of software-defined and AI-powered mobility.
文摘The usage of electric vehicles holds a crucial role in lowering the diminishing of the ozone layer because electric vehicles are not dependent on fossil fuels.With more research,evaluation,and its characteristics on electric vehicles,the infrastructure of charging points,production of electric vehicles,and network modelling,this paper provides a comprehensive overview of electric vehicles,and hybrid vehicles,including an analysis of their market growth,as well as different types of optimization used in the current scenario.In developing countries like India,the biggest barrier is their unfulfilled facility over the charging.Without renewable energy sources,vehicle-to-grid technology facilitates the enhancement of additional power requirements.The mobility factor has been considered an important and special characteristic of electric vehicles.
基金funded by Changzhou Science and Technology Project(No.CZ20230025)Postgraduate Research&Practice Innovation Program of Jiangsu Province(No.XSJCX23_36).
文摘Aiming at the problem that the existing algorithms for vehicle detection in smart factories are difficult to detect partial occlusion of vehicles,vulnerable to background interference,lack of global vision,and excessive suppression of real targets,which ultimately cause accuracy degradation.At the same time,to facilitate the subsequent positioning of vehicles in the factory,this paper proposes an improved YOLOv8 algorithm.Firstly,the RFCAConv module is combined to improve the original YOLOv8 backbone.Pay attention to the different features in the receptive field,and give priority to the spatial features of the receptive field to capture more vehicle feature information and solve the problem that the vehicle is partially occluded and difficult to detect.Secondly,the SFE module is added to the neck of v8,which improves the saliency of the target in the reasoning process and reduces the influence of background interference on vehicle detection.Finally,the head of the RT-DETR algorithm is used to replace the head in the original YOLOv8 algorithm,which avoids the excessive suppression of the real target while combining the context information.The experimental results show that compared with the original YOLOv8 algorithm,the detection accuracy of the improved YOLOv8 algorithm is improved by 4.6%on the self-made smart factory data set,and the detection speed also meets the real-time requirements of smart factory vehicle detection and subsequent vehicle positioning.
基金supported by the National Defense Basic Scientific Research Program(JCKY2021603B030)the National Natural Science Foundation of China(62273118,12150008)the Natural Science Foundation of Heilongjiang Province(LH2022F023).
文摘Dear Editor,This letter proposes a convex optimization-based model predictive control(MPC)autonomous guidance method for the Mars ascent vehicle(MAV).We use the modified chebyshev-picard iteration(MCPI)to solve optimization sub-problems within the MPC framework,eliminating the dynamic constraints in solving the optimal control problem and enhancing the convergence performance of the algorithm.Moreover,this method can repeatedly perform trajectory optimization calculations at a high frequency,achieving timely correction of the optimal control command.Numerical simulations demonstrate that the method can satisfy the requirements of rapid computation and reliability for the MAV system when considering uncertainties and perturbations.
文摘This study observes the process of strategy building and capability accumulation of companies in the currently booming Chinese electric vehicles(EV)1 market from the perspective of business ecosystems.While examining the internal and external factors of the formation about the Chinese EV industry business ecosystem,such as industrial structure transformation,technology transfer,government policies,and corporate competition,with the platform theory,I analyze the growth strategies and competitiveness of Chinese companies,particularly BYD Co.,Ltd.(BYD),which has risen to the top of the world in EV completed vehicles,and Contemporary Amperex Technology Co.,Ltd.(CATL),which has risen to the top of the world in electric vehicle batteries(EVB)2.BYD and CATL have gained competitive advantages by utilizing the distinctive management resources,which have accumulated over the years to build platforms for EVBs and EVs in response to changes in the external environment,and have actively developed their platform strategies.
基金supported by Unmanned Vehicles Core Technology Research and Development Program through the National Research Foundation of Korea(NRF)Unmanned Vehicle Advanced Research Center(UVARC)funded by the Ministry of Science and ICT,the Republic of Korea(2020M3C1C1A01083415).
文摘Flapping-Wing Air Vehicles(FWAVs)have been developed to pursue the efficient,agile,and quiet flight of flying animals.However,unlike lightweight FWAVs capable of vertical takeoff,relatively heavy FWAVs face challenges in self-takeoff,which refers to taking off without both external device and energy input.In this study,a cliff-drop method is implemented for an independent takeoff of a heavy FWAV,relying solely on gravity.In the takeoff process using the cliff-drop method,the FWAV moves on the ground to a cliff edge using a wheel-driving motor and then descends from the cliff to achieve the necessary speed for flight.To demonstrate the cliff-drop method,the KAIST Robotic Hawk(KRoHawk)with a mass of 740 g and a wingspan of 120 cm is developed.The takeoff tests demonstrate that the KRoHawk,significantly heavier than the vertical-takeoff capable FWAVs,can successfully take off using the gravity-assisted takeoff method.The scalability of cliff-drop method is analyzed through simulations.When drop constraints are absent,the wheel-driving motor mass fraction for cliff-drop method remains negligible even as the vehicle's weight increases.When drop constraints are set to 4 m,FWAVs heavier than KRoHawk,weighing up to 4.4 kg,can perform the cliff-drop takeoffs with a wheel-driving motor mass fraction of less than 8%.
基金supported by the Beijing Postdoctoral Research Foundation(No.2023-ZZ-133)Scientific Research Foundation of Beijing Infrastructure Investment Co.,Ltd.(No.2023-ZB-03)Fundamental Research Funds for the Central Universities(No.2682023ZTPY036).
文摘The pressure wave generated by an urban-rail vehicle when passing through a tunnel affects the comfort of passengersand may even cause damage to the train and related tunnel structures.Therefore,controlling the trainspeed in the tunnel is extremely important.In this study,this problem is investigated numerically in the frameworkof the standard k-εtwo-equation turbulence model.In particular,an eight-car urban rail train passingthrough a tunnel at different speeds(140,160,180 and 200 km/h)is considered.The results show that the maximumaerodynamic drag of the head and tail cars is most affected by the running speed.The pressure at selectedmeasuring points on the windward side of the head car is very high,and the negative pressure at the side windowof the driver’s cab of the tail car is also very large.From the head car to the tail car,the pressure at the same heightgradually decreases.The positive pressure peak at the head car and the negative pressure peak at the tail car aregreatly affected by the speed.
基金funded by Science and Technology Project of SGCC(SGJLCC00KJJS2203595).
文摘Aiming at the problem of increasing the peak-to-valley difference of grid load and the rising cost of user charging caused by the disorderly charging of large-scale electric vehicles,this paper proposes a coordinated charging scheduling strategy for multiple types of electric vehicles based on the degree of urgency of vehicle use.First,considering the range loss characteristics,dynamic time-sharing tariff mechanism,and user incentive policy in the lowtemperature environment of northern winter,a differentiated charging model is constructed for four types of vehicles:family cars,official cars,buses,and cabs.Then,we innovatively introduce the urgency parameter of charging demand for multiple types of vehicles and dynamically divide the emergency and non-emergency charging modes according to the difference between the regular charging capacity and the user’s minimum power demand.When the conventional charging capacity is less than the minimum power demand of the vehicle within the specified time,it is the emergency vehicle demand,and this type of vehicle is immediately charged in fast charging mode after connecting to the grid.On the contrary,it is a non-emergency demand,and the vehicle is connected to the grid to choose the appropriate time to charge in conventional charging mode.Finally,by optimizing the objective function to minimize the peakto-valley difference between the grid and the vehicle owner’s charging cost,and designing the charging continuity constraints to avoid battery damage,it ensures that the vehicle is efficiently dispatched under the premise of meeting the minimum power demand.Simulation results show that the proposed charging strategy can reduce the charging cost of vehicle owners by 26.33%,reduce the peak-to-valley difference rate of the grid by 29.8%,and significantly alleviate the congestion problem during peak load hours,compared with the disordered charging mode,while ensuring that the electric vehicles are not overcharged and meet the electricity demand of vehicle owners.This paper solves the problems of the existing research on the singularity of vehicle models and the lack of environmental adaptability and provides both economic and practical solutions for the cooperative optimization of electric vehicles and power grids in multiple scenarios.
基金co-supported by the Foundation of National Key Laboratory of Science and Technology on Aerodynamic Design and Research,China(No.614220121020114)the Key R&D Projects of Hunan Province,China(No.2023GK2022)。
文摘The design of wide-range high-efficiency aerodynamic configurations is one of the most important key technologies in the research of near-space hypersonic vehicles.A double-sided intake configuration with different inlets on the upper and lower surfaces is proposed to adapt to widerange flight.Firstly,the double-sided intake configuration’s design method and flight profile are delineated.Secondly,Computational Fluid Dynamics(CFD)numerical simulation based on multi-Graphics Processing Unit(GPU)parallel computing is adopted to evaluate the vehicle’s performance comprehensively,aiming to verify the feasibility of the proposed scheme.This evaluation encompasses a wide-range basic aerodynamic characteristics,inlet performance,and heat flux at critical locations.The results show that the inlets of the designed integration configuration can start up across Mach number 3.5 to 8.The vehicle possesses multi-point cruising capability by flipping the fuselage.Simultaneously,a 180°rotation of the fuselage can significantly decrease the heat accumulation on the lower surface of the vehicle,particularly at the inlet lip,further decreasing the temperature gradient across the vehicle structure.This study has some engineering value for the aerodynamic configuration design of wide-range vehicles.However,further study reveals that the flow phenomena at the intersection of two inlets are complex,posing potential adverse impacts on propulsion efficiency.Therefore,it is imperative to conduct additional research to delve into this matter comprehensively.
基金supported by the National Natural Science Foundation of China under Grant 61931005Beijing Natural Science Foundation under Grant L202018the Key Laboratory of Internet of Vehicle Technical Innovation and Testing(CAICT),Ministry of Industry and Information Technology under Grant No.KL-2023-001。
文摘In Internet of Vehicles,VehicleInfrastructure-Cloud cooperation supports diverse intelligent driving and intelligent transportation applications.Federated Learning(FL)is the emerging computation paradigm to provide efficient and privacypreserving collaborative learning.However,in Io V environment,federated learning faces the challenges introduced by high mobility of vehicles and nonIndependently Identically Distribution(non-IID)of data.High mobility causes FL clients quit and the communication offline.The non-IID data leads to slow and unstable convergence of global model and single global model's weak adaptability to clients with different localization characteristics.Accordingly,this paper proposes a personalized aggregation strategy for hierarchical Federated Learning in Io V environment,including Fed SA(Special Asynchronous Federated Learning with Self-adaptive Aggregation)for low-level FL between a Road Side Unit(RSU)and the vehicles within its coverage,and Fed Att(Federated Learning with Attention Mechanism)for high-level FL between a cloud server and multiple RSUs.Agents self-adaptively obtain model aggregation weight based on Advantage Actor-Critic(A2C)algorithm.Experiments show the proposed strategy encourages vehicles to participate in global aggregation,and outperforms existing methods in training performance.
