This paper introduces a method for modeling the entire aggregated electric vehicle(EV)charging process and analyzing its dispatchable capabilities.The methodology involves developing a model for aggregated EV charging...This paper introduces a method for modeling the entire aggregated electric vehicle(EV)charging process and analyzing its dispatchable capabilities.The methodology involves developing a model for aggregated EV charging at the charging station level,estimating its physical dispatchable capability,determining its economic dispatchable capability under economic incentives,modeling its participation in the grid,and investigating the effects of different scenarios and EV penetration on the aggregated load dispatch and dispatchable capability.The results indicate that using economic dispatchable capability reduces charging prices by 9.7%compared to physical dispatchable capability and 9.3%compared to disorderly charging.Additionally,the peak-to-valley difference is reduced by 64.6%when applying economic dispatchable capability with 20%EV penetration and residential base load,compared to disorderly charging.展开更多
The accelerated global adoption of electric vehicles(EVs)is driving significant expansion and increasing complexity within the EV charging infrastructure,consequently presenting novel and pressing cybersecurity challe...The accelerated global adoption of electric vehicles(EVs)is driving significant expansion and increasing complexity within the EV charging infrastructure,consequently presenting novel and pressing cybersecurity challenges.While considerable effort has focused on preventative cybersecurity measures,a critical deficiency persists in structured methodologies for digital forensic analysis following security incidents,a gap exacerbated by system heterogeneity,distributed digital evidence,and inconsistent logging practices which hinder effective incident reconstruction and attribution.This paper addresses this critical need by proposing a novel,data-driven forensic framework tailored to the EV charging infrastructure,focusing on the systematic identification,classification,and correlation of diverse digital evidence across its physical,network,and application layers.Our methodology integrates open-source intelligence(OSINT)with advanced system modeling based on a three-layer cyber-physical system architecture to comprehensively map potential evidentiary sources.Key contributions include a comprehensive taxonomy of cybersecurity threats pertinent to EV charging ecosystems,detailed mappings between these threats and the resultant digital evidence to guide targeted investigations,the formulation of adaptable forensic investigation workflows for various incident scenarios,and a critical analysis of significant gaps in digital evidence availability within current EV charging systems,highlighting limitations in forensic readiness.The practical application and utility of this method are demonstrated through illustrative case studies involving both empirically-derived and virtual incident scenarios.The proposed datadriven approach is designed to significantly enhance digital forensic capabilities,support more effective incident response,strengthen compliance with emerging cybersecurity regulations,and ultimately contribute to bolstering the overall security,resilience,and trustworthiness of this increasingly vital critical infrastructure.展开更多
In China,electric vehicle(EV)fast-charging power has quadrupled in the past five years,progressing toward 10-minute ultrafast charging.This rapid increase raises concerns about the impact on the power grid including i...In China,electric vehicle(EV)fast-charging power has quadrupled in the past five years,progressing toward 10-minute ultrafast charging.This rapid increase raises concerns about the impact on the power grid including increased peak power demand and the need for substantial upgrades to power infrastruc-ture.Here,we introduce an integrated model to assess fast and ultrafast charging impacts for represen-tative charging stations in China,combining real-world charging patterns and detailed station optimization models.We find that larger stations with 12 or more chargers experience modest peak power increases of less than 30%when fast-charging power is doubled,primarily because shorter charg-ing sessions are less likely to overlap.For more typical stations(e.g.,8-9 chargers and 120 kW·charger^(−1)),upgrading chargers to 350-550 kW while allowing managed dynamic waiting strategies(of∼1 minute)can reduce overall charging times to∼9 minutes.At stations,deploying battery storage and/or expanding transformers can help manage future increases in station loads,yet the primary device cost of the former is∼4 times higher than that of the latter.Our results offer insights for charging infrastructure planning,EV-grid interactions,and associated policymaking.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
High-power direct current fast charging(DC-HPC),particularly for megawatt-level charging currents(≥1000 A),is expected to significantly reduce charging time and improve electric vehicle durability,despite the risk of...High-power direct current fast charging(DC-HPC),particularly for megawatt-level charging currents(≥1000 A),is expected to significantly reduce charging time and improve electric vehicle durability,despite the risk of instantaneous thermal shocks.Conventional cooling methods,which separately transmit current and heat,struggle to achieve both flexible maneuverability and high-efficiency cooling.In this study,we present a synergetic cooling and transmission strategy using a gallium-based liquid metal flexible charging connector(LMFCC),which efficiently dissipates ultra-high heat flux while simultaneously carrying superhigh current.The LMFCC exhibits exceptional flexible operability(bending radius of 2 cm)and transmission stability even under significant deformation owing to the excellent liquidity and conductivity of liquid metal(LM).These properties are markedly better than those of solid metal connector.A compact induction electromagnet-driven method is optimized to significantly increase the LM flow rate and the active cooling capacity,resulting in sudden low temperature(<16℃at 1000 A).This synergetic cooling and charging strategy are expected to enable ultrahigh-heat-flux thermal management and accelerate development of the electric vehicle industry.展开更多
Potential high-temperature risks exist in heat-prone components of electric moped charging devices,such as sockets,interfaces,and controllers.Traditional detection methods have limitations in terms of real-time perfor...Potential high-temperature risks exist in heat-prone components of electric moped charging devices,such as sockets,interfaces,and controllers.Traditional detection methods have limitations in terms of real-time performance and monitoring scope.To address this,a temperature detection method based on infrared image processing has been proposed:utilizing the median filtering algorithm to denoise the original infrared image,then applying an image segmentation algorithm to divide the image.展开更多
Sodium-ion batteries have emerged as competitive substitutes for low-temperature applications due to severe capacity loss and safety concerns of lithium-ion batteries at−20°C or lower.However,the key capability o...Sodium-ion batteries have emerged as competitive substitutes for low-temperature applications due to severe capacity loss and safety concerns of lithium-ion batteries at−20°C or lower.However,the key capability of ultrafast charging at ultralow temperature for SIBs is rarely reported.Herein,a hybrid of Bi nanoparticles embedded in carbon nanorods is demonstrated as an ideal material to address this issue,which is synthesized via a high temperature shock method.Such a hybrid shows an unprecedented rate performance(237.9 mAh g^(−1) at 2 A g^(−1))at−60℃,outperforming all reported SIB anode materials.Coupled with a Na_(3)V_(2)(PO_(4))_(3)cathode,the energy density of the full cell can reach to 181.9 Wh kg^(−1) at−40°C.Based on this work,a novel strategy of high-rate activation is proposed to enhance performances of Bi-based materials in cryogenic conditions by creating new active sites for interfacial reaction under large current.展开更多
Lithium-ion batteries(LIBs)are an electrochemical energy storage technology that has been widely used for portable electrical devices,electric vehicles,and grid storage,etc.To satisfy the demand for user convenience e...Lithium-ion batteries(LIBs)are an electrochemical energy storage technology that has been widely used for portable electrical devices,electric vehicles,and grid storage,etc.To satisfy the demand for user convenience especially for electric vehicles,the development of a fast-charging technology for LIBs has become a critical focus.In commercial LIBs,the slow kinetics of Li+intercalation into the graphite anode from the electrolyte solution is known as the main restriction for fast-charging.We summarize the recent advances in obtaining fast-charging graphite-based anodes,mainly involving modifications of the electrolyte solution and graphite anode.Specifically,strategies for increasing the ionic conductivity and regulating the Li+solvation/desolvation state in the electrolyte solution,as well as optimizing the fabrication and the intrinsic activity of graphite-based anodes are discussed in detail.This review considers practical ways to obtain fast Li+intercalation kinetics into a graphite anode from the electrolyte as well as analysing progress in the commercialization of fast-charging LIBs.展开更多
Developing fast-charging lithium-ion batteries(LIBs)that feature high energy density is critical for the scalable application of electric vehicles.Iron vanadate(FVO)holds great potential as anode material in fast-char...Developing fast-charging lithium-ion batteries(LIBs)that feature high energy density is critical for the scalable application of electric vehicles.Iron vanadate(FVO)holds great potential as anode material in fast-charging LIBs because of its high theoretical specific capacity and the high natural abundance of its constituents.However,the capacity of FVO rapidly decays due to its low electrical conductivity.Herein,uniform FVO nanoparticles are grown in situ on ordered mesoporous carbon(CMK-3)support,forming a highly electrically conductive porous network,FVO/CMK-3.The structure of CMK-3 helps prevent agglomeration of FVO particles.The electrically conductive nature of CMK-3 can further enhance the electrical conductivity of FVO/CMK-3 and buffer the volume expansion of FVO particles during cycling processes.As a result,the FVO/CMK-3 displays excellent fast-charging performance of 364.6 mAh·g^(-1)capacity for 2500 cycles at 10 A·g^(-1)(with an ultralow average capacity loss per cycle of 0.003%)through a pseudocapacitive-dominant process.Moreover,the LiCoO_(2)//FVO/CMK-3 full cell achieves a high capacity of 100.2 mAh·g^(-1)and a high capacity retention(96.2%)after 200 cycles.The superior electrochemical performance demonstrates that FVO/CMK-3 is an ideal anode material candidate for fast-charging,stable LIBs with high energy density.展开更多
The global public HPC(high-power charging)network for EVs(electric vehicles)is rapidly expanding.This growth is crucial for supporting the increasing adoption of EVs but highlights the industry’s early stage.Regional...The global public HPC(high-power charging)network for EVs(electric vehicles)is rapidly expanding.This growth is crucial for supporting the increasing adoption of EVs but highlights the industry’s early stage.Regional maturity varies,with China leading due to strong government support,followed by Europe and the United States.A significant challenge is the lack of industry standards,causing inconsistencies in charger types and payment systems.Efforts are underway,to ensure interoperability and reliability.Interoperability is crucial for the success of EV HPC infrastructure,ensuring seamless integration among charge points,management systems,and service providers.Despite the use of protocols like the OCPP(Open Charge Point Protocol),variations in implementation create complexities.Ensuring uniform standards across the ecosystem is essential for reliability and efficiency.Vendor-specific error codes,which are more detailed than standardized codes,are vital for diagnosing issues but lack standardization,adding complexity.Addressing these challenges is key to supporting widespread EV adoption and enhancing user experience.To provide a compelling driver value proposition,EV charging services must be reliable and seamless.The operations and maintenance of the HPC network must be cost-effective and leverage the intelligence of the integrated ecosystem.The technical complexity of managing high-power DC charging,combined with diverse authentication and payment systems,results in numerous potential issues.Moving from reactive to predictive maintenance is essential for undisrupted operations and a smooth driver experience.Shell’s Intelligent Operations Technology Strategy incorporates GenAI elements in its advanced analytics and operational performance management tools.By ingesting big data from multiple sources across the EV ecosystem,Shell engineers can perform detailed pattern recognition and targeted troubleshooting.Monitoring,configurable alerting,and remote fixing based on auto-healing and targeted auto-allocation enhance charger availability and reduce downtime.This automation has evolved Shell’s maintenance and operations strategy from reactive to predictive,improving overall charger performance and user satisfaction.Key achievements include transitioning to prescriptive and preventive asset management approaches,significantly improving uptime and charging experience,and increasing commercial value through cost reduction and enhanced revenue.Future challenges include evolving OCPP,integrating data from non-OCPP systems,and ensuring interoperability across diverse systems.Standardization and cross-collaboration within the industry are essential for smooth interoperability,higher uptime,and increased CSR(charging success rate).Technological innovations will further shape the industry,promoting stabilization and efficiency as it matures.展开更多
Electric vehicles(EVs)are gradually being deployed in the transportation sector.Although they have a high impact on reducing greenhouse gas emissions,their penetration is challenged by their random energy demand and d...Electric vehicles(EVs)are gradually being deployed in the transportation sector.Although they have a high impact on reducing greenhouse gas emissions,their penetration is challenged by their random energy demand and difficult scheduling of their optimal charging.To cope with these problems,this paper presents a novel approach for photovoltaic grid-connected microgrid EV charging station energy demand forecasting.The present study is part of a comprehensive framework involving emerging technologies such as drones and artificial intelligence designed to support the EVs’charging scheduling task.By using predictive algorithms for solar generation and load demand estimation,this approach aimed at ensuring dynamic and efficient energy flow between the solar energy source,the grid and the electric vehicles.The main contribution of this paper lies in developing an intelligent approach based on deep recurrent neural networks to forecast the energy demand using only its previous records.Therefore,various forecasters based on Long Short-term Memory,Gated Recurrent Unit,and their bi-directional and stacked variants were investigated using a real dataset collected from an EV charging station located at Trieste University(Italy).The developed forecasters have been evaluated and compared according to different metrics,including R,RMSE,MAE,and MAPE.We found that the obtained R values for both PV power generation and energy demand ranged between 97%and 98%.These study findings can be used for reliable and efficient decision-making on the management side of the optimal scheduling of the charging operations.展开更多
Ni-rich cathode materials are essential for enhancing the performance of lithium-ion batteries(LIBs)in electric vehicles(EVs),particularly concerning extreme fast charging(XFC)and durability.While much of studies shin...Ni-rich cathode materials are essential for enhancing the performance of lithium-ion batteries(LIBs)in electric vehicles(EVs),particularly concerning extreme fast charging(XFC)and durability.While much of studies shine a spotlight on Li plating on the anode to improve rate capability,there is a critical lack of studies addressing the combination of kinetic improvements and mechanical strength of cathode materials under XFC conditions.In this work,Mg/Ti co-doped Ni-rich LiNi_(0.88)Co_(0.09)Mn_(0.03)O_(2)(MT-NCM)was successfully synthesized to address structural challenges associated with high-rate cycling.The results demonstrate that the stronger Ti–O bond contributes to the enhanced mechanical strength of secondary grains,which effectively alleviates microcrack formation during fast charging.Additionally,the detrimental phase transitions and internal strain as well as parasitic reactions of MT-NCM are significantly suppressed due to the synergistic effect of the dual dopants,ensuring excellent Li-ion transport kinetics compared to pristine NCM(P-NCM).Consequently,MT-NCM achieves remarkable high-rate cycling performance,retaining 88.04%of its initial capacity at 5 C and superior discharge capacity over 175 mA h g^(−1)even at 10 C.This work highlights the potential of optimizing the kinetic-mechanical properties of Ni-rich cathodes,providing a viable approach for the development of XFC LIBs with improved durability for EV applications.展开更多
Poor Li plating reversibility and high thermal runaway risks are key challenges for fast charging lithiumion batteries with graphite anodes.Herein,a dielectric and fire-resistant separator based on hybrid nanofibers o...Poor Li plating reversibility and high thermal runaway risks are key challenges for fast charging lithiumion batteries with graphite anodes.Herein,a dielectric and fire-resistant separator based on hybrid nanofibers of barium sulfate(BS)and bacterial cellulose(BC)is developed to synchronously enhance the battery's fast charging and thermal-safety performances.The regulation mechanism of the dielectric BS/BC separator in enhancing the Li^(+)ion transport and Li plating reversibility is revealed.(1)The Max-Wagner polarization electric field of the dielectric BS/BC separator can accelerate the desolvation of solvated Li^(+)ions,enhancing their transport kinetics.(2)Moreover,due to the charge balancing effect,the dielectric BS/BC separator homogenizes the electric field/Li^(+)ion flux at the graphite anode-separator interface,facilitating uniform Li plating and suppressing Li dendrite growth.Consequently,the fast-charge graphite anode with the BS/BC separator shows higher Coulombic efficiency(99.0%vs.96.9%)and longer cycling lifespan(100 cycles vs.59 cycles)than that with the polypropylene(PP)separator in the constantlithiation cycling test at 2 mA cm^(-2).The high-loading LiFePO4(15.5 mg cm^(-2))//graphite(7.5 mg cm^(-2))full cell with the BS/BC separator exhibits excellent fast charging performance,retaining 70%of its capacity after 500 cycles at a high rate of 2C,which is significantly better than that of the cell with the PP separator(retaining only 27%of its capacity after 500 cycles).More importantly,the thermally stable BS/BC separator effectively elevates the critical temperature and reduces the heat release rate during thermal runaway,thereby significantly enhancing the battery's safety.展开更多
Electrification of roadways using dynamic wireless charging(DWC)technology can provide an effective solution to range anxiety,high battery costs and long charging times of electric vehicles(EVs).With DWC systems insta...Electrification of roadways using dynamic wireless charging(DWC)technology can provide an effective solution to range anxiety,high battery costs and long charging times of electric vehicles(EVs).With DWC systems installed on roadways,they constitute a charging infrastructure or electrified roads(eRoads)that have many advantages.For instance,the large battery size of heavy-duty EVs can significantly be downsized due to charging-whiledriving.However,a high power demand of the DWC system,especially during traffic rush periods,could lead to voltage instability in the grid and undesirable power demand curves.In this paper,a model for the power demand is developed to predict the DWC system's power demand at various levels of EV penetration rate.The DWC power demand profile in the chosen 550 km section of a major highway in Canada is simulated.Solar photovoltaic(PV)panels are integrated with the DWC,and the integrated system is optimized to mitigate the peak power demand on the electrical grid.With solar panels of 55,000 kW rated capacity installed along roadsides in the study region,the peak power demand on the electrical grid is reduced from 167.5 to 136.1 MW or by 18.7%at an EV penetration rate of 30%under monthly average daily solar radiation in July.It is evidenced that solar PV power has effectively smoothed the peak power demand on the grid.Moreover,the locally generated renewable power could help ease off expensive grid upgrades and expansions for the eRoad.Also,the economic feasibility of the solar PV integrated DWC system is assessed using cost analysis metrics.展开更多
Electric vehicles are pivotal in the global shift toward decarbonizing road transport,with lithium-ion batteries at the heart of this technological evolution.However,the pursuit of batteries capable of extremely fast ...Electric vehicles are pivotal in the global shift toward decarbonizing road transport,with lithium-ion batteries at the heart of this technological evolution.However,the pursuit of batteries capable of extremely fast charging that also satisfy high energy and safety criteria,poses a significant challenge to current lithium-ion batteries technologies.Additionally,the increasing demand for aluminum(Al)and copper(Cu)in electrification,solar energy technologies,and vehicle light-eighting is driving these metals toward near-critical status in the medium term.This study introduces metalized polythylene terephthalate(mPET)polymer films by depositing an Al or Cu thin layer onto two sides of a polyethylene terephthalate film—named mPET/Al and mPET/Cu,as lightweight,cost-effective alternatives to traditional metal current collectors in lithium-ion batteries.We have fabricated current collectors that significantly reduce weight(by 73%),thickness(by 33%),and cost(by 85%)compared with traditional metal foil counterparts.These advancements have the potential to enhance energy density to 280 Wh kg^(-1) at the electrode level under 10-min charging at 6 C.Through testing,including a novel extremely fast charging protocol across various C-rates and long-term cycling(up to 1000 cycles)in different cell configurations,the superior performance of these metalized polymer films has been demonstrated.Notably,mPET/Cu and mPET/Al films exhibited comparable capacities to conventional cells under extremely fast charging,with the mPET cells showing a 27%improvement in energy density at 6 C and maintaining significant energy density after 1000 cycles.This study underscores the potential of mPET films to revolutionize the roll-to-roll battery manufacturing process and significantly advance the performance metrics of lithium-ion batteries in electric vehicles applications.展开更多
Electric Vehicle Charging Systems(EVCS)are increasingly vulnerable to cybersecurity threats as they integrate deeply into smart grids and Internet ofThings(IoT)environments,raising significant security challenges.Most...Electric Vehicle Charging Systems(EVCS)are increasingly vulnerable to cybersecurity threats as they integrate deeply into smart grids and Internet ofThings(IoT)environments,raising significant security challenges.Most existing research primarily emphasizes network-level anomaly detection,leaving critical vulnerabilities at the host level underexplored.This study introduces a novel forensic analysis framework leveraging host-level data,including system logs,kernel events,and Hardware Performance Counters(HPC),to detect and analyze sophisticated cyberattacks such as cryptojacking,Denial-of-Service(DoS),and reconnaissance activities targeting EVCS.Using comprehensive forensic analysis and machine learning models,the proposed framework significantly outperforms existing methods,achieving an accuracy of 98.81%.The findings offer insights into distinct behavioral signatures associated with specific cyber threats,enabling improved cybersecurity strategies and actionable recommendations for robust EVCS infrastructure protection.展开更多
The origin of tight reservoirs in the Yanchang Formation of the Ordos Basin and their relationship with hydrocarbon charging remain unclear.Based on petrological observations,physical property analysis,fluid inclusion...The origin of tight reservoirs in the Yanchang Formation of the Ordos Basin and their relationship with hydrocarbon charging remain unclear.Based on petrological observations,physical property analysis,fluid inclusion system analysis and in situ U-Pb dating,the sequence of tight sandstone reservoir densification and oil charging was determined.Through petrological observations,fluid inclusion analysis and physical property analysis,it is concluded that compaction and cementation are the primary causes of reservoir densification.When the content of calcite cement is less than or equal to 7%,compaction dominates densification;otherwise,cementation becomes more significant.However,determining the exact timing of compaction densification proved challenging.Microscopic observations revealed that oil charging likely occurred either before or during the densification of the reservoir.According to in situ U-Pb dating and the porosity evolution curve,cementation densification occurred between 167.0±20.0 Ma and 151.8 Ma.Temperature measurements of the aqueous inclusions indicate that oil charging occurred between 125.0 and 96.0 Ma,suggesting that densification preceded oil charging.This study provides valuable insights for the future exploration of tight oil reservoirs in the Ordos Basin.展开更多
Electric Vehicles(EVs)have emerged as a cleaner,low-carbon,and environmentally friendly alternative to traditional internal combustion engine(ICE)vehicles.With the increasing adoption of EVs,they are expected to event...Electric Vehicles(EVs)have emerged as a cleaner,low-carbon,and environmentally friendly alternative to traditional internal combustion engine(ICE)vehicles.With the increasing adoption of EVs,they are expected to eventually replace ICE vehicles entirely.However,the rapid growth of EVs has significantly increased energy demand,posing challenges for power grids and infrastructure.This surge in energy demand has driven advancements in developing efficient charging infrastructure and energy management solutions to mitigate the risks of power outages and disruptions caused by the rising number of EVs on the road.To address these challenges,various deep learning(DL)models,such as Recurrent Neural Networks(RNNs)and Long Short-Term Memory(LSTM)networks,have been employed for predicting energy demand at EV charging stations(EVCS).However,these models face certain limitations.They often lack interpretability,treating all input steps equally without assigning greater importance to critical patterns that are more relevant for prediction.Additionally,these models process data sequentially,which makes them computationally slower and less efficient when dealing with large datasets.In the context of these limitations,this paper introduces a novel Attention-Augmented Long Short-Term Memory(AA-LSTM)model.The proposed model integrates an attention mechanism to focus on the most relevant time steps,thereby enhancing its ability to capture long-term dependencies and improve prediction accuracy.By combining the strengths of LSTM networks in handling sequential data with the interpretability and efficiency of the attention mechanism,the AA-LSTM model delivers superior performance.The attention mechanism selectively prioritizes critical parts of the input sequence,reducing the computational burden and making the model faster and more effective.The AA-LSTM model achieves impressive results,demonstrating a Mean Absolute Percentage Error(MAPE)of 3.90%and a Mean Squared Error(MSE)of 0.40,highlighting its accuracy and reliability.These results suggest that the AA-LSTM model is a highly promising solution for predicting energy demand at EVCS,offering improved performance and efficiency compared to contemporary approaches.展开更多
The incremental capacity analysis(ICA)technique is notably limited by its sensitivity to variations in charging conditions,which constrains its practical applicability in real-world scenarios.This paper introduces an ...The incremental capacity analysis(ICA)technique is notably limited by its sensitivity to variations in charging conditions,which constrains its practical applicability in real-world scenarios.This paper introduces an ICA-compensation technique to address this limitation and propose a generalized framework for assessing the state of health(SOH)of batteries based on ICA that is applicable under differing charging conditions.This novel approach calculates the voltage profile under quasi-static conditions by subtracting the voltage increase attributable to the additional polarization effects at high currents from the measured voltage profile.This approach's efficacy is contingent upon precisely acquiring the equivalent impedance.To obtain the equivalent impedance throughout the batteries'lifespan while minimizing testing costs,this study employs a current interrupt technique in conjunction with a long short-term memory(LSTM)network to develop a predictive model for equivalent impedance.Following the derivation of ICA curves using voltage profiles under quasi-static conditions,the research explores two scenarios for SOH estimation:one utilizing only incremental capacity(IC)features and the other incorporating both IC features and IC sampling.A genetic algorithm-optimized backpropagation neural network(GABPNN)is employed for the SOH estimation.The proposed generalized framework is validated using independent training and test datasets.Variable test conditions are applied for the test set to rigorously evaluate the methodology under challenging conditions.These evaluation results demonstrate that the proposed framework achieves an estimation accuracy of 1.04%for RMSE and 0.90%for MAPE across a spectrum of charging rates ranging from 0.1 C to 1 C and starting SOCs between 0%and 70%,which constitutes a major advancement compared to established ICA methods.It also significantly enhances the applicability of conventional ICA techniques in varying charging conditions and negates the necessity for separate testing protocols for each charging scenario.展开更多
The properties of electrolytes are critical for fast-charging and stable-cycling applications in lithium metal batteries(LMBs).However,the slow kinetics of Li^(+)transport and desolvation in commercial carbonate elect...The properties of electrolytes are critical for fast-charging and stable-cycling applications in lithium metal batteries(LMBs).However,the slow kinetics of Li^(+)transport and desolvation in commercial carbonate electrolytes,cou pled with the formation of unstable solid electrolyte interphases(SEI),exacerbate the degradation of LMB performance at high current densities.Herein,we propose a versatile electrolyte design strategy that incorporates cyclohexyl methyl ether(CME)as a co-solvent to reshape the Li^(+)solvation environment by the steric-hindrance effect of bulky molecules and their competitive coordination with other solvent molecules.Simulation calculations and spectral analysis demonstrate that the addition of CME molecules reduces the involvement of other solvent molecules in the Li solvation sheath and promotes the formation of Li^(+)-PF_(6)^(-)coordination,thereby accelerating Li^(+)transport kinetics.Additionally,this electrolyte composition improves Li^(+)desolvation kinetics and fosters the formation of inorganic-rich SEI,ensuring cycle stability under fast charging.Consequently,the Li‖LiNi_(0.8)Co_(0.1)Mn_(0.1)O_(2)battery with the modified electrolyte retains 82% of its initial capacity after 463 cycles at 1 C.Even under the extreme fast-charging condition of 5 C,the battery can maintain 80% capacity retention after 173 cycles.This work provides a promising approach for the development of highperformance LMBs by modulating solvation environment of electrolytes.展开更多
基金State Grid Henan Power Company Science and Technology Project‘Key Technology and Demonstration Application of Multi-Domain Electric Vehicle Aggregated Charging Load Dispatch’(5217L0240003).
文摘This paper introduces a method for modeling the entire aggregated electric vehicle(EV)charging process and analyzing its dispatchable capabilities.The methodology involves developing a model for aggregated EV charging at the charging station level,estimating its physical dispatchable capability,determining its economic dispatchable capability under economic incentives,modeling its participation in the grid,and investigating the effects of different scenarios and EV penetration on the aggregated load dispatch and dispatchable capability.The results indicate that using economic dispatchable capability reduces charging prices by 9.7%compared to physical dispatchable capability and 9.3%compared to disorderly charging.Additionally,the peak-to-valley difference is reduced by 64.6%when applying economic dispatchable capability with 20%EV penetration and residential base load,compared to disorderly charging.
基金supported by the National Research Foundation of Korea(NRF)grant funded by the Korea government(MSIT)(RS-2023-00242528,50%)supported by a grant from the Korea Electric Power Corporation(R24XO01-4,50%)for basic research and development projects starting in 2024.
文摘The accelerated global adoption of electric vehicles(EVs)is driving significant expansion and increasing complexity within the EV charging infrastructure,consequently presenting novel and pressing cybersecurity challenges.While considerable effort has focused on preventative cybersecurity measures,a critical deficiency persists in structured methodologies for digital forensic analysis following security incidents,a gap exacerbated by system heterogeneity,distributed digital evidence,and inconsistent logging practices which hinder effective incident reconstruction and attribution.This paper addresses this critical need by proposing a novel,data-driven forensic framework tailored to the EV charging infrastructure,focusing on the systematic identification,classification,and correlation of diverse digital evidence across its physical,network,and application layers.Our methodology integrates open-source intelligence(OSINT)with advanced system modeling based on a three-layer cyber-physical system architecture to comprehensively map potential evidentiary sources.Key contributions include a comprehensive taxonomy of cybersecurity threats pertinent to EV charging ecosystems,detailed mappings between these threats and the resultant digital evidence to guide targeted investigations,the formulation of adaptable forensic investigation workflows for various incident scenarios,and a critical analysis of significant gaps in digital evidence availability within current EV charging systems,highlighting limitations in forensic readiness.The practical application and utility of this method are demonstrated through illustrative case studies involving both empirically-derived and virtual incident scenarios.The proposed datadriven approach is designed to significantly enhance digital forensic capabilities,support more effective incident response,strengthen compliance with emerging cybersecurity regulations,and ultimately contribute to bolstering the overall security,resilience,and trustworthiness of this increasingly vital critical infrastructure.
基金the support of the National Natural Science Foundation of China(72325006,72488101,and 72293601)the Sze Family Foundationthe Climate Imperative Foundation(#2024-001465)
文摘In China,electric vehicle(EV)fast-charging power has quadrupled in the past five years,progressing toward 10-minute ultrafast charging.This rapid increase raises concerns about the impact on the power grid including increased peak power demand and the need for substantial upgrades to power infrastruc-ture.Here,we introduce an integrated model to assess fast and ultrafast charging impacts for represen-tative charging stations in China,combining real-world charging patterns and detailed station optimization models.We find that larger stations with 12 or more chargers experience modest peak power increases of less than 30%when fast-charging power is doubled,primarily because shorter charg-ing sessions are less likely to overlap.For more typical stations(e.g.,8-9 chargers and 120 kW·charger^(−1)),upgrading chargers to 350-550 kW while allowing managed dynamic waiting strategies(of∼1 minute)can reduce overall charging times to∼9 minutes.At stations,deploying battery storage and/or expanding transformers can help manage future increases in station loads,yet the primary device cost of the former is∼4 times higher than that of the latter.Our results offer insights for charging infrastructure planning,EV-grid interactions,and associated policymaking.
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the National Natural Science Foundation of China(NSFC)(52076213)the 2115 Talent Development Program of China Agricultural University for the financial coverage of this work。
文摘High-power direct current fast charging(DC-HPC),particularly for megawatt-level charging currents(≥1000 A),is expected to significantly reduce charging time and improve electric vehicle durability,despite the risk of instantaneous thermal shocks.Conventional cooling methods,which separately transmit current and heat,struggle to achieve both flexible maneuverability and high-efficiency cooling.In this study,we present a synergetic cooling and transmission strategy using a gallium-based liquid metal flexible charging connector(LMFCC),which efficiently dissipates ultra-high heat flux while simultaneously carrying superhigh current.The LMFCC exhibits exceptional flexible operability(bending radius of 2 cm)and transmission stability even under significant deformation owing to the excellent liquidity and conductivity of liquid metal(LM).These properties are markedly better than those of solid metal connector.A compact induction electromagnet-driven method is optimized to significantly increase the LM flow rate and the active cooling capacity,resulting in sudden low temperature(<16℃at 1000 A).This synergetic cooling and charging strategy are expected to enable ultrahigh-heat-flux thermal management and accelerate development of the electric vehicle industry.
基金supported by the National Key Research and Development Project of China(No.2023YFB3709605)the National Natural Science Foundation of China(No.62073193)the National College Student Innovation Training Program(No.202310422122)。
文摘Potential high-temperature risks exist in heat-prone components of electric moped charging devices,such as sockets,interfaces,and controllers.Traditional detection methods have limitations in terms of real-time performance and monitoring scope.To address this,a temperature detection method based on infrared image processing has been proposed:utilizing the median filtering algorithm to denoise the original infrared image,then applying an image segmentation algorithm to divide the image.
基金supported from Science and Technology Development Program of Jilin Province(Nos.20240101128JC,20230402058GH)National Natural Science Foundation of China(No.52130101).
文摘Sodium-ion batteries have emerged as competitive substitutes for low-temperature applications due to severe capacity loss and safety concerns of lithium-ion batteries at−20°C or lower.However,the key capability of ultrafast charging at ultralow temperature for SIBs is rarely reported.Herein,a hybrid of Bi nanoparticles embedded in carbon nanorods is demonstrated as an ideal material to address this issue,which is synthesized via a high temperature shock method.Such a hybrid shows an unprecedented rate performance(237.9 mAh g^(−1) at 2 A g^(−1))at−60℃,outperforming all reported SIB anode materials.Coupled with a Na_(3)V_(2)(PO_(4))_(3)cathode,the energy density of the full cell can reach to 181.9 Wh kg^(−1) at−40°C.Based on this work,a novel strategy of high-rate activation is proposed to enhance performances of Bi-based materials in cryogenic conditions by creating new active sites for interfacial reaction under large current.
文摘Lithium-ion batteries(LIBs)are an electrochemical energy storage technology that has been widely used for portable electrical devices,electric vehicles,and grid storage,etc.To satisfy the demand for user convenience especially for electric vehicles,the development of a fast-charging technology for LIBs has become a critical focus.In commercial LIBs,the slow kinetics of Li+intercalation into the graphite anode from the electrolyte solution is known as the main restriction for fast-charging.We summarize the recent advances in obtaining fast-charging graphite-based anodes,mainly involving modifications of the electrolyte solution and graphite anode.Specifically,strategies for increasing the ionic conductivity and regulating the Li+solvation/desolvation state in the electrolyte solution,as well as optimizing the fabrication and the intrinsic activity of graphite-based anodes are discussed in detail.This review considers practical ways to obtain fast Li+intercalation kinetics into a graphite anode from the electrolyte as well as analysing progress in the commercialization of fast-charging LIBs.
基金supported by the National Natural Science Foundation of China(No.52002170)the Central Guidance Fund Project for Local Scientific and Technological Development in Qinghai Province(No.2024ZY013)+1 种基金the Foundation of Key Laboratory of Flexible Electronics of Zhejiang Province(No.2023FE011)the Postgraduate Research&Practice Innovation Program of Jiangsu Province(No.KYCX24_1635).
文摘Developing fast-charging lithium-ion batteries(LIBs)that feature high energy density is critical for the scalable application of electric vehicles.Iron vanadate(FVO)holds great potential as anode material in fast-charging LIBs because of its high theoretical specific capacity and the high natural abundance of its constituents.However,the capacity of FVO rapidly decays due to its low electrical conductivity.Herein,uniform FVO nanoparticles are grown in situ on ordered mesoporous carbon(CMK-3)support,forming a highly electrically conductive porous network,FVO/CMK-3.The structure of CMK-3 helps prevent agglomeration of FVO particles.The electrically conductive nature of CMK-3 can further enhance the electrical conductivity of FVO/CMK-3 and buffer the volume expansion of FVO particles during cycling processes.As a result,the FVO/CMK-3 displays excellent fast-charging performance of 364.6 mAh·g^(-1)capacity for 2500 cycles at 10 A·g^(-1)(with an ultralow average capacity loss per cycle of 0.003%)through a pseudocapacitive-dominant process.Moreover,the LiCoO_(2)//FVO/CMK-3 full cell achieves a high capacity of 100.2 mAh·g^(-1)and a high capacity retention(96.2%)after 200 cycles.The superior electrochemical performance demonstrates that FVO/CMK-3 is an ideal anode material candidate for fast-charging,stable LIBs with high energy density.
文摘The global public HPC(high-power charging)network for EVs(electric vehicles)is rapidly expanding.This growth is crucial for supporting the increasing adoption of EVs but highlights the industry’s early stage.Regional maturity varies,with China leading due to strong government support,followed by Europe and the United States.A significant challenge is the lack of industry standards,causing inconsistencies in charger types and payment systems.Efforts are underway,to ensure interoperability and reliability.Interoperability is crucial for the success of EV HPC infrastructure,ensuring seamless integration among charge points,management systems,and service providers.Despite the use of protocols like the OCPP(Open Charge Point Protocol),variations in implementation create complexities.Ensuring uniform standards across the ecosystem is essential for reliability and efficiency.Vendor-specific error codes,which are more detailed than standardized codes,are vital for diagnosing issues but lack standardization,adding complexity.Addressing these challenges is key to supporting widespread EV adoption and enhancing user experience.To provide a compelling driver value proposition,EV charging services must be reliable and seamless.The operations and maintenance of the HPC network must be cost-effective and leverage the intelligence of the integrated ecosystem.The technical complexity of managing high-power DC charging,combined with diverse authentication and payment systems,results in numerous potential issues.Moving from reactive to predictive maintenance is essential for undisrupted operations and a smooth driver experience.Shell’s Intelligent Operations Technology Strategy incorporates GenAI elements in its advanced analytics and operational performance management tools.By ingesting big data from multiple sources across the EV ecosystem,Shell engineers can perform detailed pattern recognition and targeted troubleshooting.Monitoring,configurable alerting,and remote fixing based on auto-healing and targeted auto-allocation enhance charger availability and reduce downtime.This automation has evolved Shell’s maintenance and operations strategy from reactive to predictive,improving overall charger performance and user satisfaction.Key achievements include transitioning to prescriptive and preventive asset management approaches,significantly improving uptime and charging experience,and increasing commercial value through cost reduction and enhanced revenue.Future challenges include evolving OCPP,integrating data from non-OCPP systems,and ensuring interoperability across diverse systems.Standardization and cross-collaboration within the industry are essential for smooth interoperability,higher uptime,and increased CSR(charging success rate).Technological innovations will further shape the industry,promoting stabilization and efficiency as it matures.
基金University of Jeddah,Jeddah,Saudi Arabia,grant No.(UJ-23-SRP-10).
文摘Electric vehicles(EVs)are gradually being deployed in the transportation sector.Although they have a high impact on reducing greenhouse gas emissions,their penetration is challenged by their random energy demand and difficult scheduling of their optimal charging.To cope with these problems,this paper presents a novel approach for photovoltaic grid-connected microgrid EV charging station energy demand forecasting.The present study is part of a comprehensive framework involving emerging technologies such as drones and artificial intelligence designed to support the EVs’charging scheduling task.By using predictive algorithms for solar generation and load demand estimation,this approach aimed at ensuring dynamic and efficient energy flow between the solar energy source,the grid and the electric vehicles.The main contribution of this paper lies in developing an intelligent approach based on deep recurrent neural networks to forecast the energy demand using only its previous records.Therefore,various forecasters based on Long Short-term Memory,Gated Recurrent Unit,and their bi-directional and stacked variants were investigated using a real dataset collected from an EV charging station located at Trieste University(Italy).The developed forecasters have been evaluated and compared according to different metrics,including R,RMSE,MAE,and MAPE.We found that the obtained R values for both PV power generation and energy demand ranged between 97%and 98%.These study findings can be used for reliable and efficient decision-making on the management side of the optimal scheduling of the charging operations.
基金supported by the Shenzhen Science and Technology Program(SGDX20230821100459001)the YCRG-CRF(C1002-24Y)the GRF Project(CityU 11220322,CityU 7006015),the City University of Hong Kong,Shenzhen Research Institute。
文摘Ni-rich cathode materials are essential for enhancing the performance of lithium-ion batteries(LIBs)in electric vehicles(EVs),particularly concerning extreme fast charging(XFC)and durability.While much of studies shine a spotlight on Li plating on the anode to improve rate capability,there is a critical lack of studies addressing the combination of kinetic improvements and mechanical strength of cathode materials under XFC conditions.In this work,Mg/Ti co-doped Ni-rich LiNi_(0.88)Co_(0.09)Mn_(0.03)O_(2)(MT-NCM)was successfully synthesized to address structural challenges associated with high-rate cycling.The results demonstrate that the stronger Ti–O bond contributes to the enhanced mechanical strength of secondary grains,which effectively alleviates microcrack formation during fast charging.Additionally,the detrimental phase transitions and internal strain as well as parasitic reactions of MT-NCM are significantly suppressed due to the synergistic effect of the dual dopants,ensuring excellent Li-ion transport kinetics compared to pristine NCM(P-NCM).Consequently,MT-NCM achieves remarkable high-rate cycling performance,retaining 88.04%of its initial capacity at 5 C and superior discharge capacity over 175 mA h g^(−1)even at 10 C.This work highlights the potential of optimizing the kinetic-mechanical properties of Ni-rich cathodes,providing a viable approach for the development of XFC LIBs with improved durability for EV applications.
基金financially supported by the National Natural Science Foundation of China(Grant No.52202328,52372099)the Shanghai Sailing Program(22YF1455500).
文摘Poor Li plating reversibility and high thermal runaway risks are key challenges for fast charging lithiumion batteries with graphite anodes.Herein,a dielectric and fire-resistant separator based on hybrid nanofibers of barium sulfate(BS)and bacterial cellulose(BC)is developed to synchronously enhance the battery's fast charging and thermal-safety performances.The regulation mechanism of the dielectric BS/BC separator in enhancing the Li^(+)ion transport and Li plating reversibility is revealed.(1)The Max-Wagner polarization electric field of the dielectric BS/BC separator can accelerate the desolvation of solvated Li^(+)ions,enhancing their transport kinetics.(2)Moreover,due to the charge balancing effect,the dielectric BS/BC separator homogenizes the electric field/Li^(+)ion flux at the graphite anode-separator interface,facilitating uniform Li plating and suppressing Li dendrite growth.Consequently,the fast-charge graphite anode with the BS/BC separator shows higher Coulombic efficiency(99.0%vs.96.9%)and longer cycling lifespan(100 cycles vs.59 cycles)than that with the polypropylene(PP)separator in the constantlithiation cycling test at 2 mA cm^(-2).The high-loading LiFePO4(15.5 mg cm^(-2))//graphite(7.5 mg cm^(-2))full cell with the BS/BC separator exhibits excellent fast charging performance,retaining 70%of its capacity after 500 cycles at a high rate of 2C,which is significantly better than that of the cell with the PP separator(retaining only 27%of its capacity after 500 cycles).More importantly,the thermally stable BS/BC separator effectively elevates the critical temperature and reduces the heat release rate during thermal runaway,thereby significantly enhancing the battery's safety.
基金Funding for this work was provided by Natural Resources Canada through the Program of Energy Research and Development.
文摘Electrification of roadways using dynamic wireless charging(DWC)technology can provide an effective solution to range anxiety,high battery costs and long charging times of electric vehicles(EVs).With DWC systems installed on roadways,they constitute a charging infrastructure or electrified roads(eRoads)that have many advantages.For instance,the large battery size of heavy-duty EVs can significantly be downsized due to charging-whiledriving.However,a high power demand of the DWC system,especially during traffic rush periods,could lead to voltage instability in the grid and undesirable power demand curves.In this paper,a model for the power demand is developed to predict the DWC system's power demand at various levels of EV penetration rate.The DWC power demand profile in the chosen 550 km section of a major highway in Canada is simulated.Solar photovoltaic(PV)panels are integrated with the DWC,and the integrated system is optimized to mitigate the peak power demand on the electrical grid.With solar panels of 55,000 kW rated capacity installed along roadsides in the study region,the peak power demand on the electrical grid is reduced from 167.5 to 136.1 MW or by 18.7%at an EV penetration rate of 30%under monthly average daily solar radiation in July.It is evidenced that solar PV power has effectively smoothed the peak power demand on the grid.Moreover,the locally generated renewable power could help ease off expensive grid upgrades and expansions for the eRoad.Also,the economic feasibility of the solar PV integrated DWC system is assessed using cost analysis metrics.
文摘Electric vehicles are pivotal in the global shift toward decarbonizing road transport,with lithium-ion batteries at the heart of this technological evolution.However,the pursuit of batteries capable of extremely fast charging that also satisfy high energy and safety criteria,poses a significant challenge to current lithium-ion batteries technologies.Additionally,the increasing demand for aluminum(Al)and copper(Cu)in electrification,solar energy technologies,and vehicle light-eighting is driving these metals toward near-critical status in the medium term.This study introduces metalized polythylene terephthalate(mPET)polymer films by depositing an Al or Cu thin layer onto two sides of a polyethylene terephthalate film—named mPET/Al and mPET/Cu,as lightweight,cost-effective alternatives to traditional metal current collectors in lithium-ion batteries.We have fabricated current collectors that significantly reduce weight(by 73%),thickness(by 33%),and cost(by 85%)compared with traditional metal foil counterparts.These advancements have the potential to enhance energy density to 280 Wh kg^(-1) at the electrode level under 10-min charging at 6 C.Through testing,including a novel extremely fast charging protocol across various C-rates and long-term cycling(up to 1000 cycles)in different cell configurations,the superior performance of these metalized polymer films has been demonstrated.Notably,mPET/Cu and mPET/Al films exhibited comparable capacities to conventional cells under extremely fast charging,with the mPET cells showing a 27%improvement in energy density at 6 C and maintaining significant energy density after 1000 cycles.This study underscores the potential of mPET films to revolutionize the roll-to-roll battery manufacturing process and significantly advance the performance metrics of lithium-ion batteries in electric vehicles applications.
文摘Electric Vehicle Charging Systems(EVCS)are increasingly vulnerable to cybersecurity threats as they integrate deeply into smart grids and Internet ofThings(IoT)environments,raising significant security challenges.Most existing research primarily emphasizes network-level anomaly detection,leaving critical vulnerabilities at the host level underexplored.This study introduces a novel forensic analysis framework leveraging host-level data,including system logs,kernel events,and Hardware Performance Counters(HPC),to detect and analyze sophisticated cyberattacks such as cryptojacking,Denial-of-Service(DoS),and reconnaissance activities targeting EVCS.Using comprehensive forensic analysis and machine learning models,the proposed framework significantly outperforms existing methods,achieving an accuracy of 98.81%.The findings offer insights into distinct behavioral signatures associated with specific cyber threats,enabling improved cybersecurity strategies and actionable recommendations for robust EVCS infrastructure protection.
基金supported by the project of the Exploration Department of the Huabei Oilfield Company of Sinopec(No.34550008-20-ZC0609-0031).
文摘The origin of tight reservoirs in the Yanchang Formation of the Ordos Basin and their relationship with hydrocarbon charging remain unclear.Based on petrological observations,physical property analysis,fluid inclusion system analysis and in situ U-Pb dating,the sequence of tight sandstone reservoir densification and oil charging was determined.Through petrological observations,fluid inclusion analysis and physical property analysis,it is concluded that compaction and cementation are the primary causes of reservoir densification.When the content of calcite cement is less than or equal to 7%,compaction dominates densification;otherwise,cementation becomes more significant.However,determining the exact timing of compaction densification proved challenging.Microscopic observations revealed that oil charging likely occurred either before or during the densification of the reservoir.According to in situ U-Pb dating and the porosity evolution curve,cementation densification occurred between 167.0±20.0 Ma and 151.8 Ma.Temperature measurements of the aqueous inclusions indicate that oil charging occurred between 125.0 and 96.0 Ma,suggesting that densification preceded oil charging.This study provides valuable insights for the future exploration of tight oil reservoirs in the Ordos Basin.
基金supported by the SC&SS,Jawaharlal Nehru University,New Delhi,India.
文摘Electric Vehicles(EVs)have emerged as a cleaner,low-carbon,and environmentally friendly alternative to traditional internal combustion engine(ICE)vehicles.With the increasing adoption of EVs,they are expected to eventually replace ICE vehicles entirely.However,the rapid growth of EVs has significantly increased energy demand,posing challenges for power grids and infrastructure.This surge in energy demand has driven advancements in developing efficient charging infrastructure and energy management solutions to mitigate the risks of power outages and disruptions caused by the rising number of EVs on the road.To address these challenges,various deep learning(DL)models,such as Recurrent Neural Networks(RNNs)and Long Short-Term Memory(LSTM)networks,have been employed for predicting energy demand at EV charging stations(EVCS).However,these models face certain limitations.They often lack interpretability,treating all input steps equally without assigning greater importance to critical patterns that are more relevant for prediction.Additionally,these models process data sequentially,which makes them computationally slower and less efficient when dealing with large datasets.In the context of these limitations,this paper introduces a novel Attention-Augmented Long Short-Term Memory(AA-LSTM)model.The proposed model integrates an attention mechanism to focus on the most relevant time steps,thereby enhancing its ability to capture long-term dependencies and improve prediction accuracy.By combining the strengths of LSTM networks in handling sequential data with the interpretability and efficiency of the attention mechanism,the AA-LSTM model delivers superior performance.The attention mechanism selectively prioritizes critical parts of the input sequence,reducing the computational burden and making the model faster and more effective.The AA-LSTM model achieves impressive results,demonstrating a Mean Absolute Percentage Error(MAPE)of 3.90%and a Mean Squared Error(MSE)of 0.40,highlighting its accuracy and reliability.These results suggest that the AA-LSTM model is a highly promising solution for predicting energy demand at EVCS,offering improved performance and efficiency compared to contemporary approaches.
基金funded by the Bavarian State Ministry of ScienceResearch and Art(Grant number:H.2-F1116.WE/52/2)。
文摘The incremental capacity analysis(ICA)technique is notably limited by its sensitivity to variations in charging conditions,which constrains its practical applicability in real-world scenarios.This paper introduces an ICA-compensation technique to address this limitation and propose a generalized framework for assessing the state of health(SOH)of batteries based on ICA that is applicable under differing charging conditions.This novel approach calculates the voltage profile under quasi-static conditions by subtracting the voltage increase attributable to the additional polarization effects at high currents from the measured voltage profile.This approach's efficacy is contingent upon precisely acquiring the equivalent impedance.To obtain the equivalent impedance throughout the batteries'lifespan while minimizing testing costs,this study employs a current interrupt technique in conjunction with a long short-term memory(LSTM)network to develop a predictive model for equivalent impedance.Following the derivation of ICA curves using voltage profiles under quasi-static conditions,the research explores two scenarios for SOH estimation:one utilizing only incremental capacity(IC)features and the other incorporating both IC features and IC sampling.A genetic algorithm-optimized backpropagation neural network(GABPNN)is employed for the SOH estimation.The proposed generalized framework is validated using independent training and test datasets.Variable test conditions are applied for the test set to rigorously evaluate the methodology under challenging conditions.These evaluation results demonstrate that the proposed framework achieves an estimation accuracy of 1.04%for RMSE and 0.90%for MAPE across a spectrum of charging rates ranging from 0.1 C to 1 C and starting SOCs between 0%and 70%,which constitutes a major advancement compared to established ICA methods.It also significantly enhances the applicability of conventional ICA techniques in varying charging conditions and negates the necessity for separate testing protocols for each charging scenario.
基金supported by the Lithium Resources and Lithium Materials Key Laboratory of Sichuan Province(LRMKF202405)the National Natural Science Foundation of China(52402226)+3 种基金the Natural Science Foundation of Sichuan Province(2024NSFSC1016)the Scientific Research Startup Foundation of Chengdu University of Technology(10912-KYQD2023-10240)the opening funding from Key Laboratory of Engineering Dielectrics and Its Application(Harbin University of Science and Technology)(KFM202507,Ministry of Education)the funding provided by the Alexander von Humboldt Foundation。
文摘The properties of electrolytes are critical for fast-charging and stable-cycling applications in lithium metal batteries(LMBs).However,the slow kinetics of Li^(+)transport and desolvation in commercial carbonate electrolytes,cou pled with the formation of unstable solid electrolyte interphases(SEI),exacerbate the degradation of LMB performance at high current densities.Herein,we propose a versatile electrolyte design strategy that incorporates cyclohexyl methyl ether(CME)as a co-solvent to reshape the Li^(+)solvation environment by the steric-hindrance effect of bulky molecules and their competitive coordination with other solvent molecules.Simulation calculations and spectral analysis demonstrate that the addition of CME molecules reduces the involvement of other solvent molecules in the Li solvation sheath and promotes the formation of Li^(+)-PF_(6)^(-)coordination,thereby accelerating Li^(+)transport kinetics.Additionally,this electrolyte composition improves Li^(+)desolvation kinetics and fosters the formation of inorganic-rich SEI,ensuring cycle stability under fast charging.Consequently,the Li‖LiNi_(0.8)Co_(0.1)Mn_(0.1)O_(2)battery with the modified electrolyte retains 82% of its initial capacity after 463 cycles at 1 C.Even under the extreme fast-charging condition of 5 C,the battery can maintain 80% capacity retention after 173 cycles.This work provides a promising approach for the development of highperformance LMBs by modulating solvation environment of electrolytes.
