Developing sensorless techniques for estimating battery expansion is essential for effective mechanical state monitoring,improving the accuracy of digital twin simulation and abnormality detection.Therefore,this paper...Developing sensorless techniques for estimating battery expansion is essential for effective mechanical state monitoring,improving the accuracy of digital twin simulation and abnormality detection.Therefore,this paper presents a data-driven approach to expansion estimation using electromechanical coupled models with machine learning.The proposed method integrates reduced-order impedance models with data-driven mechanical models,coupling the electrochemical and mechanical states through the state of charge(SOC)and mechanical pressure within a state estimation framework.The coupling relationship was established through experimental insights into pressure-related impedance parameters and the nonlinear mechanical behavior with SOC and pressure.The data-driven model was interpreted by introducing a novel swelling coefficient defined by component stiffnesses to capture the nonlinear mechanical behavior across various mechanical constraints.Sensitivity analysis of the impedance model shows that updating model parameters with pressure can reduce the mean absolute error of simulated voltage by 20 mV and SOC estimation error by 2%.The results demonstrate the model's estimation capabilities,achieving a root mean square error of less than 1 kPa when the maximum expansion force is from 30 kPa to 120 kPa,outperforming calibrated stiffness models and other machine learning techniques.The model's robustness and generalizability are further supported by its effective handling of SOC estimation and pressure measurement errors.This work highlights the importance of the proposed framework in enhancing state estimation and fault diagnosis for lithium-ion batteries.展开更多
Addressing climate change and facilitating the large-scale integration of renewable energy sources(RESs)have driven the development of hydrogen-coupled integrated energy systems(HIES),which enhance energy sustainabili...Addressing climate change and facilitating the large-scale integration of renewable energy sources(RESs)have driven the development of hydrogen-coupled integrated energy systems(HIES),which enhance energy sustainability through coordinated electricity,thermal,natural gas,and hydrogen utilization.This study proposes a two-stage distributionally robust optimization(DRO)-based scheduling method to improve the economic efficiency and reduce carbon emissions of HIES.The framework incorporates a ladder-type carbon trading mechanism to regulate emissions and implements a demand response(DR)program to adjustflexible multi-energy loads,thereby prioritizing RES consumption.Uncertainties from RES generation and load demand are addressed through an ambiguity set,enabling robust decision-making.The column-and-constraint generation(C&CG)algorithm efficiently solves the two-stage DRO model.Case studies demonstrate that the proposed method reduces operational costs by 3.56%,increases photovoltaic consumption rates by 5.44%,and significantly lowers carbon emissions compared to conventional approaches.Furthermore,the DRO framework achieves a superior balance between conservativeness and robustness over conventional stochastic and robust optimization methods,highlighting its potential to advance cost-effective,low-carbon energy systems while ensuring grid stability under uncertainty.展开更多
Accurately evaluating the safety status of lithium-ion battery systems in electric vehicles is imperative due to the challenges in effectively predicting potential battery failure risks under stochastic profiles.Compl...Accurately evaluating the safety status of lithium-ion battery systems in electric vehicles is imperative due to the challenges in effectively predicting potential battery failure risks under stochastic profiles.Complex battery fault mechanisms and limited poor-quality data collection impede fault detection for battery systems under real-world conditions.This paper proposes a novel graph-guided fault detection method designed to recognize concealed anomalies in realistic data.Graphs guided by physical relationships are constructed for learning the dynamic evolution of physical quantities under normal conditions and their potential change characteristics in fault scenarios.An ensemble Graph Sample and Aggregate Network model are developed to tackle sample distribution imbalances and non-uniformity battery system specifications across vehicles.Failure risk probabilities for diverse battery charging and discharging segments are derived.An ablation study verifies the necessity of ensemble learning in addressing imbalanced datasets.Analysis of 102,095 segments across 86 vehicles with different battery material systems,battery capacities,and numbers of cells and temperature sensors confirms the robustness and generalization of the proposed method,yielding a recall of 98.37%.By introducing the graph,spatio-temporal global fault characteristics of battery systems are automatically extracted.The coupling relationship and evolution of physical quantities under both normal and faulty states are established,effectively uncovering fault information hidden in collected battery data without observable anomalies.The safety state of battery systems is reflected in terms of failure risk probability,providing reliable data support for battery system maintenance.展开更多
DQ impedance-based method has been widely used to study the stability of three-phase converter systems.As the dq impedance model of each converter depends on its local dq reference frame,the dq impedance modeling of c...DQ impedance-based method has been widely used to study the stability of three-phase converter systems.As the dq impedance model of each converter depends on its local dq reference frame,the dq impedance modeling of complex converter networks gets complicated.Because the reference frames of different converters might not fully align,depending on the structure.Thus,in order to find an accurate impedance model of a complex network for stability analysis,converting the impedances of different converters into a common reference frame is required.This paper presents a comprehensive investigation on the transformation of dq impedances to a common reference frame in complex converter networks.Four different methods are introduced and analyzed in a systematic way.Moreover,a rigorous comparison among these approaches is carried out,where the method with the simplest transformation procedure is finally suggested for the modeling of complex converter networks.The performed analysis is verified by injecting two independent small-signal perturbations into the d and the q axis,and doing a point-by-point impedance measurement.展开更多
In traditional networks,enabling new network functions often needs to add new proprietary middleboxes.However,finding the space and power to accommodate these middleboxes is becoming increasingly difficult,along with ...In traditional networks,enabling new network functions often needs to add new proprietary middleboxes.However,finding the space and power to accommodate these middleboxes is becoming increasingly difficult,along with the increasing costs of energy and capital in-vestment.Due to the heterogeneous nature of hardware middleboxes,they suffer from long development and up-grading cycles and are hard to scale at peak load.展开更多
Battery pack capacity estimation under real-world operating conditions is important for battery performance optimization and health management,contributing to the reliability and longevity of batterypowered systems.Ho...Battery pack capacity estimation under real-world operating conditions is important for battery performance optimization and health management,contributing to the reliability and longevity of batterypowered systems.However,complex operating conditions,coupling cell-to-cell inconsistency,and limited labeled data pose great challenges to accurate and robust battery pack capacity estimation.To address these issues,this paper proposes a hierarchical data-driven framework aimed at enhancing the training of machine learning models with fewer labeled data.Unlike traditional data-driven methods that lack interpretability,the hierarchical data-driven framework unveils the“mechanism”of the black box inside the data-driven framework by splitting the final estimation target into cell-level and pack-level intermediate targets.A generalized feature matrix is devised without requiring all cell voltages,significantly reducing the computational cost and memory resources.The generated intermediate target labels and the corresponding features are hierarchically employed to enhance the training of two machine learning models,effectively alleviating the difficulty of learning the relationship from all features due to fewer labeled data and addressing the dilemma of requiring extensive labeled data for accurate estimation.Using only 10%of degradation data,the proposed framework outperforms the state-of-the-art battery pack capacity estimation methods,achieving mean absolute percentage errors of 0.608%,0.601%,and 1.128%for three battery packs whose degradation load profiles represent real-world operating conditions.Its high accuracy,adaptability,and robustness indicate the potential in different application scenarios,which is promising for reducing laborious and expensive aging experiments at the pack level and facilitating the development of battery technology.展开更多
The safety and durability of lithium-ion batteries under mechanical constraints depend significantly on electrochemical,thermal,and mechanical fields in applications.Characterizing and quantifying the multi-field coup...The safety and durability of lithium-ion batteries under mechanical constraints depend significantly on electrochemical,thermal,and mechanical fields in applications.Characterizing and quantifying the multi-field coupling behaviors requires interdisciplinary efforts.Here,we design experiments under mechanical constraints and introduce an in-situ analytical framework to clarify the complex interaction mechanisms and coupling degrees among multi-physics fields.The proposed analytical framework integrates the parameterization of equivalent models,in-situ mechanical analysis,and quantitative assessment of coupling behavior.The results indicate that the significant impact of pressure on impedance at low temperatures results from the diffusion-controlled step,enhancing kinetics when external pressure,like 180 to 240 k Pa at 10℃,is applied.The diversity in control steps for the electrochemical reaction accounts for the varying impact of pressure on battery performance across different temperatures.The thermal expansion rate suggests that the swelling force varies by less than 1.60%per unit of elevated temperature during the lithiation process.By introducing a composite metric,we quantify the coupling correlation and intensity between characteristic parameters and physical fields,uncovering the highest coupling degree in electrochemical-thermal fields.These results underscore the potential of analytical approaches in revealing the mechanisms of interaction among multi-fields,with the goal of enhancing battery performance and advancing battery management.展开更多
With the rapid advancement of the Internet,network attack methods are constantly evolving and adapting.To better identify the network attack behavior,a universal gravitation clustering algorithm was proposed by analyz...With the rapid advancement of the Internet,network attack methods are constantly evolving and adapting.To better identify the network attack behavior,a universal gravitation clustering algorithm was proposed by analyzing the dissimilarities and similarities of the clustering algorithms.First,the algorithm designated the cluster set as vacant,with the introduction of a new object.Subsequently,a new cluster based on the given object was constructed.The dissimilarities between it and each existing cluster were calculated using a defined difference measure.Theminimumdissimilaritywas selected.Through comparing the proposed algorithmwith the traditional Back Propagation(BP)neural network and nearest neighbor detection algorithm,the application of the Defense Advanced Research Projects Agency(DARPA)00 and Knowledge Discovery and Data Mining(KDD)Cup 99 datasets revealed that the performance of the proposed algorithmsurpassed that of both algorithms in terms of the detection rate,speed,false positive rate,and false negative rate.展开更多
This paper introduces an adaptive traffic allocation scheme with cooperation of multiple Radio Access Networks (RANs) in universal wireless environments.The different cooperation scenarios are studied,and based on the...This paper introduces an adaptive traffic allocation scheme with cooperation of multiple Radio Access Networks (RANs) in universal wireless environments.The different cooperation scenarios are studied,and based on the scenario of cooperation in both network layer and terminal layer,an open queuing system model,which is aiming to depict the characteristics of packet loss rate of wireless communication networks,is proposed to optimize the traffic allocation results.The analysis and numerical simulations indicate that the proposed scheme achieves inter-networking load balance tominimize the whole transmission delay and expands the communication ability of single-mode terminals to support high data rate traffics.展开更多
With the tremendous advances in the field of information and communications technology(ICT),and the emergence of new applications,such as 4K/8K,AR/VR,Internet of Things,and Industrial Internet and so on,the traditiona...With the tremendous advances in the field of information and communications technology(ICT),and the emergence of new applications,such as 4K/8K,AR/VR,Internet of Things,and Industrial Internet and so on,the traditional Internet has faced severe challenges on scalability,flexibility,controllability,mobility and security and so on.In order to cope with these challeng-展开更多
This paper proposes a stochastic and distributed optimal energy management approach for active distribution networks(ADNs)within office buildings.The proposed approach aims at scheduling office buildings fitted with h...This paper proposes a stochastic and distributed optimal energy management approach for active distribution networks(ADNs)within office buildings.The proposed approach aims at scheduling office buildings fitted with heating ventilation and air conditioning(HVAC)systems,and electric vehicle(EV)charging piles,to participate in the ADN optimization.First,an energy management approach for the ADN with aggregated office buildings is proposed.And the ADN optimization model is formulated considering the detailed building thermal dynamics and the mobile behaviors of workers.Then,to consider un-certainties of photovoltaic(PV)power,scenario-based stochastic programming is integrated into the ADN optimization model.To further realize the stochastic energy management of the ADN within office buildings in a distributed manner,the alternating direction method of multipliers(ADMM)is used to solve the ADN optimization model.The original ADN optimization problem is divided into the network-side and the building-side sub-problems to effectively protect the privacy of the ADN and the office buildings.Finally,the ADN optimization model incorporating office buildings is validated in the winter by numerical studies.In addition,the impacts of comfort temperature range and expected state of charge(SOC)at departure are analyzed.Index Terms—ADN,EV,HVAC system,Office building,Stochastic and distributed energy management.展开更多
Through the correct teaching and game design,not only can innovation be carried out,but the highly effective network security teaching and the training solution countermeasure can also develop the student’s correct s...Through the correct teaching and game design,not only can innovation be carried out,but the highly effective network security teaching and the training solution countermeasure can also develop the student’s correct social behavior and value idea.Furthermore,through rule design and role play,students can improve the overall level of network security awareness and enhance their sense of cooperation during their studies.This paper defines the concept of a serious game and its significance to the network security curriculum of schools and designs the corresponding card game according to the feature of network security.The purpose of this paper is to strengthen overall network security awareness,cultivate professional skills,and enhance network security.展开更多
Microgrids are networked control systems with multiple distributed generators(DGs).Microgrids are associated with many problems,such as communication delays,high sampling rates,and frequent controller updates,which ma...Microgrids are networked control systems with multiple distributed generators(DGs).Microgrids are associated with many problems,such as communication delays,high sampling rates,and frequent controller updates,which make it challenging to realize coordination control among the DGs.Therefore,finite-time consensus algorithms and event-triggered control methods are combined to propose a distributed coordination control method for microgrid systems.The DG in the microgrid system serves as an agent node in the control network,and a distributed secondary controller is designed using finite-time consensus algorithm,such that the frequency and voltage restoration control has a faster convergence time and better anti-interference performance.The event-triggered function was designed based on the state information of the agents.The controller exchanges the state information at the trigger instants.System stability is analyzed using the Lyapunov stability theory,and it is verified that the controller cannot exhibit the Zeno phenomenon in the event-triggered process.A simulation platform was developed in Matlab/Simulink to verify that the proposed control method can effectively reduce the frequency of controller updates during communication delays and the burden on the communication network.展开更多
This study establishes for the first time a P2D-coupled non-ideal double-layer capacitor model(P2D-CNIC),which can be used for mechanism analysis under high-frequency periodic signal excitation.The novelty of this wor...This study establishes for the first time a P2D-coupled non-ideal double-layer capacitor model(P2D-CNIC),which can be used for mechanism analysis under high-frequency periodic signal excitation.The novelty of this work is the consideration of the generally neglected electric double-layer capacitance and its dispersion effects,especially the capacitance of the solid electrolyte interface(SEI)film.The dispersion effect of the model is verified by a periodic current excitation signal and the corresponding phase change in the voltage response.Under sinusoidal alternating current(AC)excitation,a comparative analysis was conducted between the traditional P2D model,the traditional P2D model coupled with the ideal double-layer capacitor(P2D-CIC),and the proposed P2D-CNIC mechanism model.Furthermore,three models were evaluated under periodic short-circuit pulse discharge conditions to verify the accuracy and reliability of P2D-CNIC.The simulation results are used to analyze the dominant order of faradaic and non-Faraday processes under sinusoidal AC excitation,thereby providing insights into the internal mechanism analysis of lithium batteries under high-frequency cycling conditions.展开更多
A fuzzy multi-objective bi-level optimization problem is proposed to model the planning of energy storage system(ESS) in active distribution systems(ADS). The proposed model enables us to take into account how optimal...A fuzzy multi-objective bi-level optimization problem is proposed to model the planning of energy storage system(ESS) in active distribution systems(ADS). The proposed model enables us to take into account how optimal operation strategy of ESS in the lower level can affect and be affected by the optimal allocation of ESS in the upper level. The power characteristic model of micro-grid(MG)and typical daily scenarios are established to take full consideration of time-variable nature of renewable energy generations(REGs) and load demand while easing the burden of computation. To solve the bi-level mixed integer problem, a multi-subgroup hierarchical chaos hybrid algorithm is introduced based on differential evolution(DE) and particle swarm optimization(PSO). The modified IEEE-33 bus benchmark distribution system is utilized to investigate the availability and effectiveness of the proposed model and the hybrid algorithm. Results indicate that the planningmodel gives an adequate consideration to the optimal operation and different roles of ESS, and has the advantages of objectiveness and reasonableness.展开更多
1 IntroductionSince the coupled-mode theory in cylindrical optical-fiber systems was proposed in 1972, the optical coupling between parallel optical waveguides has been a matter of scientific concern. Two-core fiber c...1 IntroductionSince the coupled-mode theory in cylindrical optical-fiber systems was proposed in 1972, the optical coupling between parallel optical waveguides has been a matter of scientific concern. Two-core fiber couplers, especially, have been studied extensively since the success of producing a two-core fiber functioning as a directional coupler in 1980. The wavelength and polarization selectivity of two-core fibers can find many applications. The nonlinear properties of the two-core fiber coupler ...展开更多
Using the spontaneously synchronized clapping in a concert hall as a special case, we build a nonlinear emergent model to characterize the collective behaviors of the complex multi-agent systems. Based on this model, ...Using the spontaneously synchronized clapping in a concert hall as a special case, we build a nonlinear emergent model to characterize the collective behaviors of the complex multi-agent systems. Based on this model, we develop an experimental platform for emergent computation, which only depends on the local interaction and reveals the uncertainty and diversity of emergent behaviors. By analyzing the data in the procedure of many hands clapping, we find that there exists an obvious critical region generated in the procedure from disorder to synchronization. More- over, we propose a fundamental synchronous criterion as follows： If the coupling coefficients c1 and c2 satisfy the condition 0.02≤c2=≤0.965c1＋0.018, then the clapping can realize synchronization.展开更多
Battery fault diagnosis is essential for ensuring the reliability and safety of electric vehicles(EVs).The existing battery fault diagnosis methods are difficult to detect faults at an early stage based on the real-wo...Battery fault diagnosis is essential for ensuring the reliability and safety of electric vehicles(EVs).The existing battery fault diagnosis methods are difficult to detect faults at an early stage based on the real-world vehicle data since lithium-ion battery systems are usually accompanied by inconsistencies,which are difficult to distinguish from faults.A fault diagnosis method based on signal decomposition and two-dimensional feature clustering is introduced in this paper.Symplectic geometry mode decomposition(SGMD)is introduced to obtain the components characterizing battery states,and distance-based similarity measures with the normalized extended average voltage and dynamic time warping distances are established to evaluate the state of batteries.The 2-dimensional feature clustering based on DBSCAN is developed to reduce the number of feature thresholds and differentiate flaw cells from the battery pack with only one parameter under a wide range of values.The proposed method can achieve fault diagnosis and voltage anomaly identification as early as 43 days ahead of the thermal runaway.And the results of four electric vehicles and the comparison with other traditional methods validated the proposed method with strong robustness,high reliability,and long time scale warning,and the method is easy to implement online.展开更多
Development of power electronics transformers(PETs)has made dc distribution and hybrid ac/dc systems a competitive solution for future network expansion.This study focuses on the functions of a multiport PET interfaci...Development of power electronics transformers(PETs)has made dc distribution and hybrid ac/dc systems a competitive solution for future network expansion.This study focuses on the functions of a multiport PET interfacing medium-voltage and low-voltage networks.A comprehensive power flow control scheme based on a globalised multiport transmission model of the PET and droop control is derived.This allows for a coordinated energy exchange between the ports of the PET,which enables autonomous operation.Simulations of the proposed approach in a hybrid ac/dc distribution network with different levels of distributed generation and loads verify the effectiveness of the method.展开更多
Packet classification has been studied for decades; it classifies packets into specific flows based on a given rule set. As software-defined network was proposed, a recent trend of packet classification is to scale th...Packet classification has been studied for decades; it classifies packets into specific flows based on a given rule set. As software-defined network was proposed, a recent trend of packet classification is to scale the five-tuple model to multi-tuple. In general, packet classification on multiple fields is a complex problem. Although most existing software-based algorithms have been proved extraordinary in practice, they are only suitable for the classic five-tuple model and difficult to be scaled up. Meanwhile, hardware-specific solutions are inflexible and expensive, and some of them are power consuming. In this paper, we propose a universal multi-dimensional packet classification approach for multi-core systems. In our approach, novel data structures and four decomposition-based algorithms are designed to optimize the classification and updating of rules. For multi-field rules, a rule set is cut into several parts according to the number of fields. Each part works independently. In this way, the fields are searched in parallel and all the partial results are merged together at last. To demonstrate the feasibility of our approach, we implement a prototype and evaluate its throughput and latency. Experimental results show that our approach achieves a 40% higher throughput than that of other decomposed-based algorithms and a 43% lower latency of rule incremental update than that of the other algorithms on average. Furthermore, our approach saves 39% memory consumption on average and has a good scalability.展开更多
基金Fund supported this work for Excellent Youth Scholars of China(Grant No.52222708)the National Natural Science Foundation of China(Grant No.51977007)+1 种基金Part of this work is supported by the research project“SPEED”(03XP0585)at RWTH Aachen Universityfunded by the German Federal Ministry of Education and Research(BMBF)。
文摘Developing sensorless techniques for estimating battery expansion is essential for effective mechanical state monitoring,improving the accuracy of digital twin simulation and abnormality detection.Therefore,this paper presents a data-driven approach to expansion estimation using electromechanical coupled models with machine learning.The proposed method integrates reduced-order impedance models with data-driven mechanical models,coupling the electrochemical and mechanical states through the state of charge(SOC)and mechanical pressure within a state estimation framework.The coupling relationship was established through experimental insights into pressure-related impedance parameters and the nonlinear mechanical behavior with SOC and pressure.The data-driven model was interpreted by introducing a novel swelling coefficient defined by component stiffnesses to capture the nonlinear mechanical behavior across various mechanical constraints.Sensitivity analysis of the impedance model shows that updating model parameters with pressure can reduce the mean absolute error of simulated voltage by 20 mV and SOC estimation error by 2%.The results demonstrate the model's estimation capabilities,achieving a root mean square error of less than 1 kPa when the maximum expansion force is from 30 kPa to 120 kPa,outperforming calibrated stiffness models and other machine learning techniques.The model's robustness and generalizability are further supported by its effective handling of SOC estimation and pressure measurement errors.This work highlights the importance of the proposed framework in enhancing state estimation and fault diagnosis for lithium-ion batteries.
基金supported by National Key Research and Development Program(2024YFE0115600).
文摘Addressing climate change and facilitating the large-scale integration of renewable energy sources(RESs)have driven the development of hydrogen-coupled integrated energy systems(HIES),which enhance energy sustainability through coordinated electricity,thermal,natural gas,and hydrogen utilization.This study proposes a two-stage distributionally robust optimization(DRO)-based scheduling method to improve the economic efficiency and reduce carbon emissions of HIES.The framework incorporates a ladder-type carbon trading mechanism to regulate emissions and implements a demand response(DR)program to adjustflexible multi-energy loads,thereby prioritizing RES consumption.Uncertainties from RES generation and load demand are addressed through an ambiguity set,enabling robust decision-making.The column-and-constraint generation(C&CG)algorithm efficiently solves the two-stage DRO model.Case studies demonstrate that the proposed method reduces operational costs by 3.56%,increases photovoltaic consumption rates by 5.44%,and significantly lowers carbon emissions compared to conventional approaches.Furthermore,the DRO framework achieves a superior balance between conservativeness and robustness over conventional stochastic and robust optimization methods,highlighting its potential to advance cost-effective,low-carbon energy systems while ensuring grid stability under uncertainty.
基金funded by the National Natural Science Foundation of China(Grant No.52222708)。
文摘Accurately evaluating the safety status of lithium-ion battery systems in electric vehicles is imperative due to the challenges in effectively predicting potential battery failure risks under stochastic profiles.Complex battery fault mechanisms and limited poor-quality data collection impede fault detection for battery systems under real-world conditions.This paper proposes a novel graph-guided fault detection method designed to recognize concealed anomalies in realistic data.Graphs guided by physical relationships are constructed for learning the dynamic evolution of physical quantities under normal conditions and their potential change characteristics in fault scenarios.An ensemble Graph Sample and Aggregate Network model are developed to tackle sample distribution imbalances and non-uniformity battery system specifications across vehicles.Failure risk probabilities for diverse battery charging and discharging segments are derived.An ablation study verifies the necessity of ensemble learning in addressing imbalanced datasets.Analysis of 102,095 segments across 86 vehicles with different battery material systems,battery capacities,and numbers of cells and temperature sensors confirms the robustness and generalization of the proposed method,yielding a recall of 98.37%.By introducing the graph,spatio-temporal global fault characteristics of battery systems are automatically extracted.The coupling relationship and evolution of physical quantities under both normal and faulty states are established,effectively uncovering fault information hidden in collected battery data without observable anomalies.The safety state of battery systems is reflected in terms of failure risk probability,providing reliable data support for battery system maintenance.
基金The support of the first and fourth authors is given by National Key R&D Program of China,2018YFB0905200.The support for the second and third authors is coming from BIRD171227/17 project of the University of Padova.
文摘DQ impedance-based method has been widely used to study the stability of three-phase converter systems.As the dq impedance model of each converter depends on its local dq reference frame,the dq impedance modeling of complex converter networks gets complicated.Because the reference frames of different converters might not fully align,depending on the structure.Thus,in order to find an accurate impedance model of a complex network for stability analysis,converting the impedances of different converters into a common reference frame is required.This paper presents a comprehensive investigation on the transformation of dq impedances to a common reference frame in complex converter networks.Four different methods are introduced and analyzed in a systematic way.Moreover,a rigorous comparison among these approaches is carried out,where the method with the simplest transformation procedure is finally suggested for the modeling of complex converter networks.The performed analysis is verified by injecting two independent small-signal perturbations into the d and the q axis,and doing a point-by-point impedance measurement.
文摘In traditional networks,enabling new network functions often needs to add new proprietary middleboxes.However,finding the space and power to accommodate these middleboxes is becoming increasingly difficult,along with the increasing costs of energy and capital in-vestment.Due to the heterogeneous nature of hardware middleboxes,they suffer from long development and up-grading cycles and are hard to scale at peak load.
基金supported by the National Outstanding Youth Science Fund Project of National Natural Science Foundation of China[Grant No.52222708]the Natural Science Foundation of Beijing Municipality[Grant No.3212033]。
文摘Battery pack capacity estimation under real-world operating conditions is important for battery performance optimization and health management,contributing to the reliability and longevity of batterypowered systems.However,complex operating conditions,coupling cell-to-cell inconsistency,and limited labeled data pose great challenges to accurate and robust battery pack capacity estimation.To address these issues,this paper proposes a hierarchical data-driven framework aimed at enhancing the training of machine learning models with fewer labeled data.Unlike traditional data-driven methods that lack interpretability,the hierarchical data-driven framework unveils the“mechanism”of the black box inside the data-driven framework by splitting the final estimation target into cell-level and pack-level intermediate targets.A generalized feature matrix is devised without requiring all cell voltages,significantly reducing the computational cost and memory resources.The generated intermediate target labels and the corresponding features are hierarchically employed to enhance the training of two machine learning models,effectively alleviating the difficulty of learning the relationship from all features due to fewer labeled data and addressing the dilemma of requiring extensive labeled data for accurate estimation.Using only 10%of degradation data,the proposed framework outperforms the state-of-the-art battery pack capacity estimation methods,achieving mean absolute percentage errors of 0.608%,0.601%,and 1.128%for three battery packs whose degradation load profiles represent real-world operating conditions.Its high accuracy,adaptability,and robustness indicate the potential in different application scenarios,which is promising for reducing laborious and expensive aging experiments at the pack level and facilitating the development of battery technology.
基金supported by the National Science Fund for Excellent Youth Scholars of China(52222708)the National Natural Science Foundation of China(51977007)。
文摘The safety and durability of lithium-ion batteries under mechanical constraints depend significantly on electrochemical,thermal,and mechanical fields in applications.Characterizing and quantifying the multi-field coupling behaviors requires interdisciplinary efforts.Here,we design experiments under mechanical constraints and introduce an in-situ analytical framework to clarify the complex interaction mechanisms and coupling degrees among multi-physics fields.The proposed analytical framework integrates the parameterization of equivalent models,in-situ mechanical analysis,and quantitative assessment of coupling behavior.The results indicate that the significant impact of pressure on impedance at low temperatures results from the diffusion-controlled step,enhancing kinetics when external pressure,like 180 to 240 k Pa at 10℃,is applied.The diversity in control steps for the electrochemical reaction accounts for the varying impact of pressure on battery performance across different temperatures.The thermal expansion rate suggests that the swelling force varies by less than 1.60%per unit of elevated temperature during the lithiation process.By introducing a composite metric,we quantify the coupling correlation and intensity between characteristic parameters and physical fields,uncovering the highest coupling degree in electrochemical-thermal fields.These results underscore the potential of analytical approaches in revealing the mechanisms of interaction among multi-fields,with the goal of enhancing battery performance and advancing battery management.
基金supported by the Fujian China University Education Informatization Project(FJGX2023013)National Natural Science Foundation of China Youth Program(72001126)+1 种基金Sanming University’s Research and Optimization of the Function of Safety Test Management and Control Platform Project(KH22097)Young and Middle-Aged Teacher Education Research Project of Fujian Provincial Department of Education(JAT200642,B202033).
文摘With the rapid advancement of the Internet,network attack methods are constantly evolving and adapting.To better identify the network attack behavior,a universal gravitation clustering algorithm was proposed by analyzing the dissimilarities and similarities of the clustering algorithms.First,the algorithm designated the cluster set as vacant,with the introduction of a new object.Subsequently,a new cluster based on the given object was constructed.The dissimilarities between it and each existing cluster were calculated using a defined difference measure.Theminimumdissimilaritywas selected.Through comparing the proposed algorithmwith the traditional Back Propagation(BP)neural network and nearest neighbor detection algorithm,the application of the Defense Advanced Research Projects Agency(DARPA)00 and Knowledge Discovery and Data Mining(KDD)Cup 99 datasets revealed that the performance of the proposed algorithmsurpassed that of both algorithms in terms of the detection rate,speed,false positive rate,and false negative rate.
基金supported by the National Natural Science Foundation of China under Grant No.60971125National Major Project under Grant No.2011ZX03003-003-01
文摘This paper introduces an adaptive traffic allocation scheme with cooperation of multiple Radio Access Networks (RANs) in universal wireless environments.The different cooperation scenarios are studied,and based on the scenario of cooperation in both network layer and terminal layer,an open queuing system model,which is aiming to depict the characteristics of packet loss rate of wireless communication networks,is proposed to optimize the traffic allocation results.The analysis and numerical simulations indicate that the proposed scheme achieves inter-networking load balance tominimize the whole transmission delay and expands the communication ability of single-mode terminals to support high data rate traffics.
文摘With the tremendous advances in the field of information and communications technology(ICT),and the emergence of new applications,such as 4K/8K,AR/VR,Internet of Things,and Industrial Internet and so on,the traditional Internet has faced severe challenges on scalability,flexibility,controllability,mobility and security and so on.In order to cope with these challeng-
基金supported in part by the Fundamental Research Funds for the Central Universities(2021YJS148)the National Natural Science Foundation of China(Grant No.51677004).
文摘This paper proposes a stochastic and distributed optimal energy management approach for active distribution networks(ADNs)within office buildings.The proposed approach aims at scheduling office buildings fitted with heating ventilation and air conditioning(HVAC)systems,and electric vehicle(EV)charging piles,to participate in the ADN optimization.First,an energy management approach for the ADN with aggregated office buildings is proposed.And the ADN optimization model is formulated considering the detailed building thermal dynamics and the mobile behaviors of workers.Then,to consider un-certainties of photovoltaic(PV)power,scenario-based stochastic programming is integrated into the ADN optimization model.To further realize the stochastic energy management of the ADN within office buildings in a distributed manner,the alternating direction method of multipliers(ADMM)is used to solve the ADN optimization model.The original ADN optimization problem is divided into the network-side and the building-side sub-problems to effectively protect the privacy of the ADN and the office buildings.Finally,the ADN optimization model incorporating office buildings is validated in the winter by numerical studies.In addition,the impacts of comfort temperature range and expected state of charge(SOC)at departure are analyzed.Index Terms—ADN,EV,HVAC system,Office building,Stochastic and distributed energy management.
基金Supported by Hainan Provincial National Science Foundation of China,621MS0789.
文摘Through the correct teaching and game design,not only can innovation be carried out,but the highly effective network security teaching and the training solution countermeasure can also develop the student’s correct social behavior and value idea.Furthermore,through rule design and role play,students can improve the overall level of network security awareness and enhance their sense of cooperation during their studies.This paper defines the concept of a serious game and its significance to the network security curriculum of schools and designs the corresponding card game according to the feature of network security.The purpose of this paper is to strengthen overall network security awareness,cultivate professional skills,and enhance network security.
基金National Natural Science Foundation of China(62063016).
文摘Microgrids are networked control systems with multiple distributed generators(DGs).Microgrids are associated with many problems,such as communication delays,high sampling rates,and frequent controller updates,which make it challenging to realize coordination control among the DGs.Therefore,finite-time consensus algorithms and event-triggered control methods are combined to propose a distributed coordination control method for microgrid systems.The DG in the microgrid system serves as an agent node in the control network,and a distributed secondary controller is designed using finite-time consensus algorithm,such that the frequency and voltage restoration control has a faster convergence time and better anti-interference performance.The event-triggered function was designed based on the state information of the agents.The controller exchanges the state information at the trigger instants.System stability is analyzed using the Lyapunov stability theory,and it is verified that the controller cannot exhibit the Zeno phenomenon in the event-triggered process.A simulation platform was developed in Matlab/Simulink to verify that the proposed control method can effectively reduce the frequency of controller updates during communication delays and the burden on the communication network.
基金supported by the National Natural Science Foundation of China(grant no.52177206)the Joint Funds of Equipment Pre-Research and Ministry of Education of China(grant no.8091B022130).
文摘This study establishes for the first time a P2D-coupled non-ideal double-layer capacitor model(P2D-CNIC),which can be used for mechanism analysis under high-frequency periodic signal excitation.The novelty of this work is the consideration of the generally neglected electric double-layer capacitance and its dispersion effects,especially the capacitance of the solid electrolyte interface(SEI)film.The dispersion effect of the model is verified by a periodic current excitation signal and the corresponding phase change in the voltage response.Under sinusoidal alternating current(AC)excitation,a comparative analysis was conducted between the traditional P2D model,the traditional P2D model coupled with the ideal double-layer capacitor(P2D-CIC),and the proposed P2D-CNIC mechanism model.Furthermore,three models were evaluated under periodic short-circuit pulse discharge conditions to verify the accuracy and reliability of P2D-CNIC.The simulation results are used to analyze the dominant order of faradaic and non-Faraday processes under sinusoidal AC excitation,thereby providing insights into the internal mechanism analysis of lithium batteries under high-frequency cycling conditions.
基金supported by Application Technology Research and Engineering Demonstration Program of National Energy Administration in China (No. NY20150301)
文摘A fuzzy multi-objective bi-level optimization problem is proposed to model the planning of energy storage system(ESS) in active distribution systems(ADS). The proposed model enables us to take into account how optimal operation strategy of ESS in the lower level can affect and be affected by the optimal allocation of ESS in the upper level. The power characteristic model of micro-grid(MG)and typical daily scenarios are established to take full consideration of time-variable nature of renewable energy generations(REGs) and load demand while easing the burden of computation. To solve the bi-level mixed integer problem, a multi-subgroup hierarchical chaos hybrid algorithm is introduced based on differential evolution(DE) and particle swarm optimization(PSO). The modified IEEE-33 bus benchmark distribution system is utilized to investigate the availability and effectiveness of the proposed model and the hybrid algorithm. Results indicate that the planningmodel gives an adequate consideration to the optimal operation and different roles of ESS, and has the advantages of objectiveness and reasonableness.
文摘1 IntroductionSince the coupled-mode theory in cylindrical optical-fiber systems was proposed in 1972, the optical coupling between parallel optical waveguides has been a matter of scientific concern. Two-core fiber couplers, especially, have been studied extensively since the success of producing a two-core fiber functioning as a directional coupler in 1980. The wavelength and polarization selectivity of two-core fibers can find many applications. The nonlinear properties of the two-core fiber coupler ...
基金the National Basic Research Program of China (Grant No. 2007CB310800)the National Natural Science Foundation of China (Grant Nos. 60375016, 60496323 and 60675032)
文摘Using the spontaneously synchronized clapping in a concert hall as a special case, we build a nonlinear emergent model to characterize the collective behaviors of the complex multi-agent systems. Based on this model, we develop an experimental platform for emergent computation, which only depends on the local interaction and reveals the uncertainty and diversity of emergent behaviors. By analyzing the data in the procedure of many hands clapping, we find that there exists an obvious critical region generated in the procedure from disorder to synchronization. More- over, we propose a fundamental synchronous criterion as follows： If the coupling coefficients c1 and c2 satisfy the condition 0.02≤c2=≤0.965c1＋0.018, then the clapping can realize synchronization.
基金the National Natural Science Foundation of China[No.51977007,No.52007006]the Natural Science Foundation of Beijing under grant 3212033.
文摘Battery fault diagnosis is essential for ensuring the reliability and safety of electric vehicles(EVs).The existing battery fault diagnosis methods are difficult to detect faults at an early stage based on the real-world vehicle data since lithium-ion battery systems are usually accompanied by inconsistencies,which are difficult to distinguish from faults.A fault diagnosis method based on signal decomposition and two-dimensional feature clustering is introduced in this paper.Symplectic geometry mode decomposition(SGMD)is introduced to obtain the components characterizing battery states,and distance-based similarity measures with the normalized extended average voltage and dynamic time warping distances are established to evaluate the state of batteries.The 2-dimensional feature clustering based on DBSCAN is developed to reduce the number of feature thresholds and differentiate flaw cells from the battery pack with only one parameter under a wide range of values.The proposed method can achieve fault diagnosis and voltage anomaly identification as early as 43 days ahead of the thermal runaway.And the results of four electric vehicles and the comparison with other traditional methods validated the proposed method with strong robustness,high reliability,and long time scale warning,and the method is easy to implement online.
基金National key R&D plan:Research and Demonstration of Key Technology of Charging Facilities Network Combined with Renewable Energy Power Generation(Project No.2016YFB0900505).
文摘Development of power electronics transformers(PETs)has made dc distribution and hybrid ac/dc systems a competitive solution for future network expansion.This study focuses on the functions of a multiport PET interfacing medium-voltage and low-voltage networks.A comprehensive power flow control scheme based on a globalised multiport transmission model of the PET and droop control is derived.This allows for a coordinated energy exchange between the ports of the PET,which enables autonomous operation.Simulations of the proposed approach in a hybrid ac/dc distribution network with different levels of distributed generation and loads verify the effectiveness of the method.
基金This work was supported by the National Basic Research 973 Program of China under Grant No. 2012CB315805 and the National Natural Science Foundation of China under Grant Nos. 61472130 and 61702174.
文摘Packet classification has been studied for decades; it classifies packets into specific flows based on a given rule set. As software-defined network was proposed, a recent trend of packet classification is to scale the five-tuple model to multi-tuple. In general, packet classification on multiple fields is a complex problem. Although most existing software-based algorithms have been proved extraordinary in practice, they are only suitable for the classic five-tuple model and difficult to be scaled up. Meanwhile, hardware-specific solutions are inflexible and expensive, and some of them are power consuming. In this paper, we propose a universal multi-dimensional packet classification approach for multi-core systems. In our approach, novel data structures and four decomposition-based algorithms are designed to optimize the classification and updating of rules. For multi-field rules, a rule set is cut into several parts according to the number of fields. Each part works independently. In this way, the fields are searched in parallel and all the partial results are merged together at last. To demonstrate the feasibility of our approach, we implement a prototype and evaluate its throughput and latency. Experimental results show that our approach achieves a 40% higher throughput than that of other decomposed-based algorithms and a 43% lower latency of rule incremental update than that of the other algorithms on average. Furthermore, our approach saves 39% memory consumption on average and has a good scalability.
