Rapid evolutions of the Internet of Electric Vehicles(IoEVs)are reshaping and modernizing transport systems,yet challenges remain in energy efficiency,better battery aging,and grid stability.Typical charging methods a...Rapid evolutions of the Internet of Electric Vehicles(IoEVs)are reshaping and modernizing transport systems,yet challenges remain in energy efficiency,better battery aging,and grid stability.Typical charging methods allow for EVs to be charged without thought being given to the condition of the battery or the grid demand,thus increasing energy costs and battery aging.This study proposes a smart charging station with an AI-powered Battery Management System(BMS),developed and simulated in MATLAB/Simulink,to increase optimality in energy flow,battery health,and impractical scheduling within the IoEV environment.The system operates through real-time communication,load scheduling based on priorities,and adaptive charging based on batterymathematically computed State of Charge(SOC),State of Health(SOH),and thermal state,with bidirectional power flow(V2G),thus allowing EVs’participation towards grid stabilization.Simulation results revealed that the proposed model can reduce peak grid load by 37.8%;charging efficiency is enhanced by 92.6%;battery temperature lessened by 4.4℃;SOH extended over 100 cycles by 6.5%,if compared against the conventional technique.By this way,charging time was decreased by 12.4% and energy costs dropped by more than 20%.These results showed that smart charging with intelligent BMS can boost greatly the operational efficiency and sustainability of the IoEV ecosystem.展开更多
To achieve low-carbon regulation of electric vehicle(EV)charging loads under the“dual carbon”goals,this paper proposes a coordinated scheduling strategy that integrates dynamic carbon factor prediction and multiobje...To achieve low-carbon regulation of electric vehicle(EV)charging loads under the“dual carbon”goals,this paper proposes a coordinated scheduling strategy that integrates dynamic carbon factor prediction and multiobjective optimization.First,a dual-convolution enhanced improved Crossformer prediction model is constructed,which employs parallel 1×1 global and 3×3 local convolutionmodules(Integrated Convolution Block,ICB)formultiscale feature extraction,combinedwith anAdaptive Spectral Block(ASB)to enhance time-series fluctuationmodeling.Based on high-precision predictions,a carbon-electricity cost joint optimization model is further designed to balance economic,environmental,and grid-friendly objectives.The model’s superiority was validated through a case study using real-world data from a renewable-heavy grid.Simulation results show that the proposed multi-objective strategy demonstrated a superior balance compared to baseline and benchmark models,achieving a 15.8%reduction in carbon emissions and a 5.2%reduction in economic costs,while still providing a substantial 22.2%reduction in the peak-valley difference.Its balanced performance significantly outperformed both a single-objective strategy and a state-of-the-art Model Predictive Control(MPC)benchmark,highlighting the advantage of a global optimization approach.This study provides theoretical and technical pathways for dynamic carbon factor-driven EV charging optimization.展开更多
Due to the centralization of charging stations(CSs),CSs are organized as charging station alliances(CSAs)in the commercial competition.Under this situation,this paper studies the profit-oriented dynamic pricing strate...Due to the centralization of charging stations(CSs),CSs are organized as charging station alliances(CSAs)in the commercial competition.Under this situation,this paper studies the profit-oriented dynamic pricing strategy of CSAs.As the practicability basis,a privacy-protected bidirectional real-time information interaction framework is designed,under which the status of EVs is utilized as the reference for pricing,and the prices of CSs are the reference for charging decisions.Based on this framework,the decision-making models of EVs and CSs are established,in which the uncertainty caused by the information asymmetry between EVs and CSs and the bounded rationality of EV users are integrated.To solve the pricing decision model,the evolutionary game theory is adopted to describe the dynamic pricing game among CSAs,the equilibrium of which gives the optimal pricing strategy.Finally,the case study conducted in an urban area of Shanghai,China,validates the practicability of the framework and the effectiveness of the dynamic pricing strategy.展开更多
Accurate state of health(SOH)estimation is essential for the safe and reliable operation of lithium-ion batteries.However,existing methods face significant challenges,primarily because they rely on complete charge–di...Accurate state of health(SOH)estimation is essential for the safe and reliable operation of lithium-ion batteries.However,existing methods face significant challenges,primarily because they rely on complete charge–discharge cycles and fixed-form physical constraints,which limit adaptability to different chemistries and real-world conditions.To address these issues,this study proposes an approach that extracts features from segmented state of charge(SOC)intervals and integrates them into an enhanced physics-informed neural network(PINN).Specifically,voltage data within the 25%–75%SOC range during charging are used to derive statistical,time–frequency,and mechanism-based features that capture degradation trends.A hybrid PINN-Lasso-Transformer-BiLSTM architecture is developed,where Lasso regression enables sparse feature selection,and a nonlinear empirical degradation model is embedded as a learnable physical term within a dynamically scaled composite loss.This design adaptively balances data-driven accuracy with physical consistency,thereby enhancing estimation precision,robustness,and generalization.The results show that the proposed method outperforms conventional neural networks across four battery chemistries,achieving root mean square error and mean absolute error below 1%.Notably,features from partial charging segments exhibit higher robustness than those from full cycles.Furthermore,the model maintains strong performance under high temperatures and demonstrates excellent generalization capacity in transfer learning across chemistries,temperatures,and C-rates.This work establishes a scalable and interpretable solution for accurate SOH estimation under diverse practical operating conditions.展开更多
As renewable energy penetration continues to rise,enhancing power system flexibility has become a critical requirement.Photovoltaic–storage–charging stations(PSCSs)are key components for enhancing local regulation c...As renewable energy penetration continues to rise,enhancing power system flexibility has become a critical requirement.Photovoltaic–storage–charging stations(PSCSs)are key components for enhancing local regulation capability and promoting renewable integration.However,evaluating the adjustable capability of such hybrid stations while considering security constraints remains a major challenge.This paper first analyzes the adjustable capabilities of all the resources within such a station based on the power-energy boundary(PEB)model.Then,an optimal formulation is proposed to obtain the adjusted parameters of the aggregate feasible region(AFR)model,which embeds low-dimensional linear models within high-dimensional linear models to improve the accuracy.To solve this formulation,it is transformed using duality theory and an alternating optimization algorithm is designed to obtain the solution.Finally,a multi-station adjustable capability aggregation method considering security constraints is introduced.Simulation results verify that the proposed method effectively reduces infeasible regions and improves smoothness of aggregated boundaries,providing an accurate and practical tool for flexibility evaluation in PSCSs and offering guidance for aggregators and system planners.展开更多
Under the background of achieving energy efficiency and carbon reduction,the use of Continuous Casting-Direct Hot Charging Rolling(CC-DHCR)technology was explored at the Hot Rolling Plant of Baosteel,China.As temperat...Under the background of achieving energy efficiency and carbon reduction,the use of Continuous Casting-Direct Hot Charging Rolling(CC-DHCR)technology was explored at the Hot Rolling Plant of Baosteel,China.As temperature variation and temperature uniformity directly affect the rolling quality of the billet,numerical simulations of the billet temperature profile changes in the CC-DHCR were conducted at the start of the industrial trial,and the billet temperature distribution and cross-section temperature difference during its transportation and throughout the heating process were analyzed.According to numerical simulation calculations,the average temperature of billet discharged from the heating furnace was 1150℃,which after subsequent controlled cooling met the final rolling temperature requirements of(880±30)℃for this kind of steel.The maximum temperature difference of the billet discharged from the furnace was within 35 K,which meets the billet heating uniformity requirements of the rolling process.The simulation results were compared with experimental results,and the rationality of the simulation was validated.In addition,the industrial trial billet was rolled,and the rolling quality was good during tracking.In this CC-DHCR industrial trial,the fuel consumption was 28.6 kgce/t(kilogram coal equivalent per ton),a reduction of 33.7%compared with the current traditional process,and CO 2 emissions were reduced by 38.09 kg/t.展开更多
With the large-scale integration of new energy sources,various resources such as energy storage,electric vehicles(EVs),and photovoltaics(PV) have participated in the scheduling of active distribution networks(ADNs),po...With the large-scale integration of new energy sources,various resources such as energy storage,electric vehicles(EVs),and photovoltaics(PV) have participated in the scheduling of active distribution networks(ADNs),posing new challenges to the operation and scheduling of distribution networks.Aiming at the uncertainty of PV and EV,an optimal scheduling model for ADNs based on multi-scenario fuzzy set based charging station resource forecasting is constructed.To address the scheduling uncertainties caused by PV and load forecasting errors,a day-ahead optimal scheduling model based on conditional value at risk(CVaR) for cost assessment is established,with the optimization objectives of minimizing the operation cost of distribution networks and the risk cost caused by forecasting errors.An improved subtractive optimizer algorithm is proposed to solve the model and formulate day-ahead optimization schemes.Secondly,a forecasting model for dispatchable resources in charging stations is constructed based on event-based fuzzy set theory.On this basis,an intraday scheduling model is built to comprehensively utilize the dispatchable resources of charging stations to coordinate with the output of distributed power sources,achieving optimal scheduling with the goal of minimizing operation costs.Finally,an experimental scenario based on the IEEE-33 node system is designed for simulation verification.The comparison of optimal scheduling results shows that the proposed method can fully exploit the potential scheduling resources of charging stations,improving the operation stability of ADNs and the accommodution capacity of new energy.展开更多
To address the performance limitations of conventional LiPF6-carbonate electrolytes under extreme temperatures and high-rate charging,lithium difluoro(oxalato)borate(LiDFOB)is introduced into the LiPF6-carbonate elect...To address the performance limitations of conventional LiPF6-carbonate electrolytes under extreme temperatures and high-rate charging,lithium difluoro(oxalato)borate(LiDFOB)is introduced into the LiPF6-carbonate electrolyte to form a dual-salt system.The optimization mechanism enhancing the fast-charging capability of LiNi_(0.52)Co_(0.2)Mn_(0.28)O_(2)(NCM523)cathode is systematically explored.Molecular dynamics simulations and electrochemical characterization demonstrate the reconstruction of Li+solvation structures,expanding the voltage window and reducting Li^(+)desolvation barriers.In addition,the incorporation of LiDFOB induces the generation of a LiF/Li_(x)BO_(y)F_(z)-enriched cathode-electrolyte interphase,which effectively suppresses the dissolution of transition metals.In situ impedance measurements reveal the accelerated interfacial charge transfer kinetics.As expected,the NCM523 cathode achieves an 82%state-of-charge(SOC)in 12 min at 5 C(25°C)with 87%capacity retention after 100 cycles,and exhibits a 65%higher discharge capacity at 1 C than the baseline at−20°C.The 1 Ah pouch cells based on LiNi_(0.52)Co_(0.2)Mn_(0.28)O_(2)cathodes,graphite anodes,and 0.5 wt%LiDFOB-modified electrolyte demonstrate fast-charging capabilities:charging 97%of the pouch cell capacity within 30 min(2 C)and 80%within 15 min(4 C)at 25°C.This study offers a practical electrolyte design strategy that enhances the fast-charging performance of lithium-ion batteries(LIBs)over a wide temperature range(from−20 to 25°C).展开更多
Zn-based thermal charging devices,utilizing the synergistic effect of ion thermoextraction and thermodiffusion,are able to efficiently convert thermal energy into electrical energy and storage in the devices,making th...Zn-based thermal charging devices,utilizing the synergistic effect of ion thermoextraction and thermodiffusion,are able to efficiently convert thermal energy into electrical energy and storage in the devices,making them a highly promising technology for low-grade heat recovery and utilization.However,the low output power density and energy conversion efficiency resulted by the slow diffusion kinetics of Zn^(2+)hinder their development.Herein,we present a highperformance thermal charging cell design using Zn^(2+)/NH_(4)^(+)hybrid ion electrolyte,which not only maintains the high output voltage of the Zn-based thermoelectric system,but also significantly enhances the output power density due to the fast diffusion kinetics of NH_(4)^(+).Based on this strategy,the thermal charging cell displays a high thermopower of 12.5 mV K^(-1)and an excellent normalized power density of 19.6 mW m^(-2)K^(-2)at a temperature difference of 35 K.The Carnot-relative efficiency is as high as 12.74%.Moreover,it can operate continuously for over 72 h when the temperature difference persists,achieving a balance between thermoelectric conversion and output.This work provides a simple and effective strategy for the design of high-performance thermal charging cells for low-grade heat conversion and utilization.展开更多
We proposed a strategy to address the issue by synthesizing MnO with half-filled 3 d electron orbitals.That is,MnO nanocubes with an edge length of 61.82 nm were successfully prepared through electros-pinning and one-...We proposed a strategy to address the issue by synthesizing MnO with half-filled 3 d electron orbitals.That is,MnO nanocubes with an edge length of 61.82 nm were successfully prepared through electros-pinning and one-step pyrolysis as the cathode electrode for Li-O_(2)batteries.It is observed that the intermediate LiMnO_(4)rather than Li_(2)O_(2)is formed when LiO_(2)interactes with MnO(111)during the discharge process.It is precisely because of LiMnO_(4)that reduces its charge overpotential to 0.29 V.The novel reaction mechanism dominated by LiMnO_(4)further facilitates the lower charge overpotential,thereby enhancing the energy efficiency of the batteries.展开更多
In the Jimusaer Sag of the Junggar Basin,crude oils from the upper and lower sweet-spot intervals of the Permian Lucaogou Formation display a pronounced“light-heavy reversal”in oil properties that indicates a fundam...In the Jimusaer Sag of the Junggar Basin,crude oils from the upper and lower sweet-spot intervals of the Permian Lucaogou Formation display a pronounced“light-heavy reversal”in oil properties that indicates a fundamental mismatch between oil composition and host rock maturity.To resolve this anomaly,this study integrates geological,geochemical,and petrophysical datasets and systematically evaluates the combined roles of thermal evolution,organofacies,wettability,abnormal overpressure,and migration-related fractionation on shale oil composition.On this basis,a“staged charging-cumulative charging”model is proposed to explain compositional heterogeneity in lacustrine shale oils.The results demonstrate that crude-oil compositions are jointly controlled by the extent of biomarker depletion,the temporal evolution of hydrocarbon charging,and the openness of the source-reservoir system,rather than by thermal maturity or organofacies alone.The upper sweet-spot interval is interpreted to have functioned as a semi-open system during early stages,in which hydrocarbon generation and expulsion were broadly synchronous,leading to preferential loss of early-generated,biomarker-rich heavy components,whereas progressive shale diagenesis at later stages promoted the retention of highly mature,light hydrocarbons.In contrast,the lower sweet-spot interval represents a relatively closed system,where hydrocarbons generated during multiple stages continuously accumulated and were preserved as mixed charges;overprinting by multi-phase fluids progressively weakened sterane isomerization signals,rendering them unreliable indicators of individual charging events or final thermal maturity.This charging behavior provides a reasonable explanation for anomalously low or distorted biomarker parameters observed in intervals of low or similar maturity.Overall,the proposed charging model reconciles the observed reversal in crude-oil properties and,by shifting the interpretive focus from static maturity assessment to charging dynamics,offers a new theoretical basis for understanding lacustrine shale oil accumulation processes,and guiding sweet-spot selection and exploration-development strategies.展开更多
Achieving extreme fast charging(XFC,-6 C)capability remains a challenge for Li ion batteries in electric vehicle applications.This work employs time-resolved X-ray diffraction(XRD)to investigate the structural evoluti...Achieving extreme fast charging(XFC,-6 C)capability remains a challenge for Li ion batteries in electric vehicle applications.This work employs time-resolved X-ray diffraction(XRD)to investigate the structural evolution and capacity contributions of a series of LiNi_(x)Co_(y)Mn_(z)O_(2)(x+y+z=1,NCM)cathodes under XFC conditions.All NCM cathodes(NCM-92,NCM-83,and NCM-622)deliver -60%of their capacities with less than 2%unit cell volume expansion during the H1-H2 phase transition,but the subsequent H2-H3 phase transition exhibits significant compositional and rate dependence.The NCM-92 cathode shows a maximum d-spacing shrinkage of-5.3%at 6 C,which is larger than that of NCM-83(-4.1%)and NCM-622(-0.05%).Furthermore,NCM-92 follows a“phase heterogeneity”pathway for its structural evolution above 4.2 V,distinct from the“solid-solution”pathway observed in NCM-83 and NCM-622.This phase heterogeneity is evidenced by the splitting of the(003)diffraction peak and a decrease in intensity during the H2-H3 phase transition,indicating the formation of lithium-rich/depleted domains.These findings establish a direct correlation between cathode composition,structural dynamics,and XFC performance,highlighting a critical trade-off between structural stability and fast-charging capability in nickel-rich layered oxides.展开更多
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
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
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
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
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
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
文摘Rapid evolutions of the Internet of Electric Vehicles(IoEVs)are reshaping and modernizing transport systems,yet challenges remain in energy efficiency,better battery aging,and grid stability.Typical charging methods allow for EVs to be charged without thought being given to the condition of the battery or the grid demand,thus increasing energy costs and battery aging.This study proposes a smart charging station with an AI-powered Battery Management System(BMS),developed and simulated in MATLAB/Simulink,to increase optimality in energy flow,battery health,and impractical scheduling within the IoEV environment.The system operates through real-time communication,load scheduling based on priorities,and adaptive charging based on batterymathematically computed State of Charge(SOC),State of Health(SOH),and thermal state,with bidirectional power flow(V2G),thus allowing EVs’participation towards grid stabilization.Simulation results revealed that the proposed model can reduce peak grid load by 37.8%;charging efficiency is enhanced by 92.6%;battery temperature lessened by 4.4℃;SOH extended over 100 cycles by 6.5%,if compared against the conventional technique.By this way,charging time was decreased by 12.4% and energy costs dropped by more than 20%.These results showed that smart charging with intelligent BMS can boost greatly the operational efficiency and sustainability of the IoEV ecosystem.
基金Supported by State Grid Corporation of China Science and Technology Project:Research on Key Technologies for Intelligent Carbon Metrology in Vehicle-to-Grid Interaction(Project Number:B3018524000Q).
文摘To achieve low-carbon regulation of electric vehicle(EV)charging loads under the“dual carbon”goals,this paper proposes a coordinated scheduling strategy that integrates dynamic carbon factor prediction and multiobjective optimization.First,a dual-convolution enhanced improved Crossformer prediction model is constructed,which employs parallel 1×1 global and 3×3 local convolutionmodules(Integrated Convolution Block,ICB)formultiscale feature extraction,combinedwith anAdaptive Spectral Block(ASB)to enhance time-series fluctuationmodeling.Based on high-precision predictions,a carbon-electricity cost joint optimization model is further designed to balance economic,environmental,and grid-friendly objectives.The model’s superiority was validated through a case study using real-world data from a renewable-heavy grid.Simulation results show that the proposed multi-objective strategy demonstrated a superior balance compared to baseline and benchmark models,achieving a 15.8%reduction in carbon emissions and a 5.2%reduction in economic costs,while still providing a substantial 22.2%reduction in the peak-valley difference.Its balanced performance significantly outperformed both a single-objective strategy and a state-of-the-art Model Predictive Control(MPC)benchmark,highlighting the advantage of a global optimization approach.This study provides theoretical and technical pathways for dynamic carbon factor-driven EV charging optimization.
基金sponsored in part by the National Natural Science Foundation of China(52167014)in part by the Science and Technology Commission of Shanghai Municipality(23XD1422000,23QB1400500).
文摘Due to the centralization of charging stations(CSs),CSs are organized as charging station alliances(CSAs)in the commercial competition.Under this situation,this paper studies the profit-oriented dynamic pricing strategy of CSAs.As the practicability basis,a privacy-protected bidirectional real-time information interaction framework is designed,under which the status of EVs is utilized as the reference for pricing,and the prices of CSs are the reference for charging decisions.Based on this framework,the decision-making models of EVs and CSs are established,in which the uncertainty caused by the information asymmetry between EVs and CSs and the bounded rationality of EV users are integrated.To solve the pricing decision model,the evolutionary game theory is adopted to describe the dynamic pricing game among CSAs,the equilibrium of which gives the optimal pricing strategy.Finally,the case study conducted in an urban area of Shanghai,China,validates the practicability of the framework and the effectiveness of the dynamic pricing strategy.
基金supported by the Shanghai Pilot Program for Basic Research(22T01400100-18)the National Natural Science Foundation of China(22278127 and 12447149)+1 种基金the Fundamental Research Funds for the Central Universities(2022ZFJH004)the Postdoctoral Fellowship Program of CPSF(GZB20250159).
文摘Accurate state of health(SOH)estimation is essential for the safe and reliable operation of lithium-ion batteries.However,existing methods face significant challenges,primarily because they rely on complete charge–discharge cycles and fixed-form physical constraints,which limit adaptability to different chemistries and real-world conditions.To address these issues,this study proposes an approach that extracts features from segmented state of charge(SOC)intervals and integrates them into an enhanced physics-informed neural network(PINN).Specifically,voltage data within the 25%–75%SOC range during charging are used to derive statistical,time–frequency,and mechanism-based features that capture degradation trends.A hybrid PINN-Lasso-Transformer-BiLSTM architecture is developed,where Lasso regression enables sparse feature selection,and a nonlinear empirical degradation model is embedded as a learnable physical term within a dynamically scaled composite loss.This design adaptively balances data-driven accuracy with physical consistency,thereby enhancing estimation precision,robustness,and generalization.The results show that the proposed method outperforms conventional neural networks across four battery chemistries,achieving root mean square error and mean absolute error below 1%.Notably,features from partial charging segments exhibit higher robustness than those from full cycles.Furthermore,the model maintains strong performance under high temperatures and demonstrates excellent generalization capacity in transfer learning across chemistries,temperatures,and C-rates.This work establishes a scalable and interpretable solution for accurate SOH estimation under diverse practical operating conditions.
基金supported by Science and Technology Project of China Southern Power Grid Company(036000KK52222007(GDKJXM20222121)).
文摘As renewable energy penetration continues to rise,enhancing power system flexibility has become a critical requirement.Photovoltaic–storage–charging stations(PSCSs)are key components for enhancing local regulation capability and promoting renewable integration.However,evaluating the adjustable capability of such hybrid stations while considering security constraints remains a major challenge.This paper first analyzes the adjustable capabilities of all the resources within such a station based on the power-energy boundary(PEB)model.Then,an optimal formulation is proposed to obtain the adjusted parameters of the aggregate feasible region(AFR)model,which embeds low-dimensional linear models within high-dimensional linear models to improve the accuracy.To solve this formulation,it is transformed using duality theory and an alternating optimization algorithm is designed to obtain the solution.Finally,a multi-station adjustable capability aggregation method considering security constraints is introduced.Simulation results verify that the proposed method effectively reduces infeasible regions and improves smoothness of aggregated boundaries,providing an accurate and practical tool for flexibility evaluation in PSCSs and offering guidance for aggregators and system planners.
文摘Under the background of achieving energy efficiency and carbon reduction,the use of Continuous Casting-Direct Hot Charging Rolling(CC-DHCR)technology was explored at the Hot Rolling Plant of Baosteel,China.As temperature variation and temperature uniformity directly affect the rolling quality of the billet,numerical simulations of the billet temperature profile changes in the CC-DHCR were conducted at the start of the industrial trial,and the billet temperature distribution and cross-section temperature difference during its transportation and throughout the heating process were analyzed.According to numerical simulation calculations,the average temperature of billet discharged from the heating furnace was 1150℃,which after subsequent controlled cooling met the final rolling temperature requirements of(880±30)℃for this kind of steel.The maximum temperature difference of the billet discharged from the furnace was within 35 K,which meets the billet heating uniformity requirements of the rolling process.The simulation results were compared with experimental results,and the rationality of the simulation was validated.In addition,the industrial trial billet was rolled,and the rolling quality was good during tracking.In this CC-DHCR industrial trial,the fuel consumption was 28.6 kgce/t(kilogram coal equivalent per ton),a reduction of 33.7%compared with the current traditional process,and CO 2 emissions were reduced by 38.09 kg/t.
基金Supported by the Technology Project of State Grid Corporation Headquarters(No.5100-202322029A-1-1-ZN)the 2024 Youth Science Foundation Project of China (No.62303006)。
文摘With the large-scale integration of new energy sources,various resources such as energy storage,electric vehicles(EVs),and photovoltaics(PV) have participated in the scheduling of active distribution networks(ADNs),posing new challenges to the operation and scheduling of distribution networks.Aiming at the uncertainty of PV and EV,an optimal scheduling model for ADNs based on multi-scenario fuzzy set based charging station resource forecasting is constructed.To address the scheduling uncertainties caused by PV and load forecasting errors,a day-ahead optimal scheduling model based on conditional value at risk(CVaR) for cost assessment is established,with the optimization objectives of minimizing the operation cost of distribution networks and the risk cost caused by forecasting errors.An improved subtractive optimizer algorithm is proposed to solve the model and formulate day-ahead optimization schemes.Secondly,a forecasting model for dispatchable resources in charging stations is constructed based on event-based fuzzy set theory.On this basis,an intraday scheduling model is built to comprehensively utilize the dispatchable resources of charging stations to coordinate with the output of distributed power sources,achieving optimal scheduling with the goal of minimizing operation costs.Finally,an experimental scenario based on the IEEE-33 node system is designed for simulation verification.The comparison of optimal scheduling results shows that the proposed method can fully exploit the potential scheduling resources of charging stations,improving the operation stability of ADNs and the accommodution capacity of new energy.
基金financially supported by the National Natural Science Foundation of China (Grant No. 52372191)the National Natural Science Foundation of China (Grant No. 22271106)+2 种基金the National Science Foundation of China (Grant Nos. 52073286 (C.-Z.L.), 22275185 (C.-Z.L.))the Fujian Science&Technology Innovation Laboratory for Optoelectronic Information of China(2021ZZ115 (C.-Z.L.)the XMIREM Autonomously Deployment Project (2023GG01 (C.-Z.L.))
文摘To address the performance limitations of conventional LiPF6-carbonate electrolytes under extreme temperatures and high-rate charging,lithium difluoro(oxalato)borate(LiDFOB)is introduced into the LiPF6-carbonate electrolyte to form a dual-salt system.The optimization mechanism enhancing the fast-charging capability of LiNi_(0.52)Co_(0.2)Mn_(0.28)O_(2)(NCM523)cathode is systematically explored.Molecular dynamics simulations and electrochemical characterization demonstrate the reconstruction of Li+solvation structures,expanding the voltage window and reducting Li^(+)desolvation barriers.In addition,the incorporation of LiDFOB induces the generation of a LiF/Li_(x)BO_(y)F_(z)-enriched cathode-electrolyte interphase,which effectively suppresses the dissolution of transition metals.In situ impedance measurements reveal the accelerated interfacial charge transfer kinetics.As expected,the NCM523 cathode achieves an 82%state-of-charge(SOC)in 12 min at 5 C(25°C)with 87%capacity retention after 100 cycles,and exhibits a 65%higher discharge capacity at 1 C than the baseline at−20°C.The 1 Ah pouch cells based on LiNi_(0.52)Co_(0.2)Mn_(0.28)O_(2)cathodes,graphite anodes,and 0.5 wt%LiDFOB-modified electrolyte demonstrate fast-charging capabilities:charging 97%of the pouch cell capacity within 30 min(2 C)and 80%within 15 min(4 C)at 25°C.This study offers a practical electrolyte design strategy that enhances the fast-charging performance of lithium-ion batteries(LIBs)over a wide temperature range(from−20 to 25°C).
基金supported by the Leading Edge Technology of Jiangsu Province(BK20222009-X.Z.,BK20202008-X.Z.)Priority Academic Program Development of Jiangsu Higher Education Institutions(PAPD)National Undergraduate Innovation Training Program of NUAA(202410287179Y).
文摘Zn-based thermal charging devices,utilizing the synergistic effect of ion thermoextraction and thermodiffusion,are able to efficiently convert thermal energy into electrical energy and storage in the devices,making them a highly promising technology for low-grade heat recovery and utilization.However,the low output power density and energy conversion efficiency resulted by the slow diffusion kinetics of Zn^(2+)hinder their development.Herein,we present a highperformance thermal charging cell design using Zn^(2+)/NH_(4)^(+)hybrid ion electrolyte,which not only maintains the high output voltage of the Zn-based thermoelectric system,but also significantly enhances the output power density due to the fast diffusion kinetics of NH_(4)^(+).Based on this strategy,the thermal charging cell displays a high thermopower of 12.5 mV K^(-1)and an excellent normalized power density of 19.6 mW m^(-2)K^(-2)at a temperature difference of 35 K.The Carnot-relative efficiency is as high as 12.74%.Moreover,it can operate continuously for over 72 h when the temperature difference persists,achieving a balance between thermoelectric conversion and output.This work provides a simple and effective strategy for the design of high-performance thermal charging cells for low-grade heat conversion and utilization.
基金Funded by the National Natural Science Foundation of China(No.22075035)the Technology Planning Project of Liaoning Province(No.2020JH2/10700008)the Dalian Science and Technology Innovation Fund Project(No.2022JJ11CG005)。
文摘We proposed a strategy to address the issue by synthesizing MnO with half-filled 3 d electron orbitals.That is,MnO nanocubes with an edge length of 61.82 nm were successfully prepared through electros-pinning and one-step pyrolysis as the cathode electrode for Li-O_(2)batteries.It is observed that the intermediate LiMnO_(4)rather than Li_(2)O_(2)is formed when LiO_(2)interactes with MnO(111)during the discharge process.It is precisely because of LiMnO_(4)that reduces its charge overpotential to 0.29 V.The novel reaction mechanism dominated by LiMnO_(4)further facilitates the lower charge overpotential,thereby enhancing the energy efficiency of the batteries.
基金Supported by the National Natural Science Foundation of China(42173030,42302161,42473034)State Science and Technology Major Project for New Oil and Gas Exploration and Development,Ministry of Science and Technology(2025ZD1400803)。
文摘In the Jimusaer Sag of the Junggar Basin,crude oils from the upper and lower sweet-spot intervals of the Permian Lucaogou Formation display a pronounced“light-heavy reversal”in oil properties that indicates a fundamental mismatch between oil composition and host rock maturity.To resolve this anomaly,this study integrates geological,geochemical,and petrophysical datasets and systematically evaluates the combined roles of thermal evolution,organofacies,wettability,abnormal overpressure,and migration-related fractionation on shale oil composition.On this basis,a“staged charging-cumulative charging”model is proposed to explain compositional heterogeneity in lacustrine shale oils.The results demonstrate that crude-oil compositions are jointly controlled by the extent of biomarker depletion,the temporal evolution of hydrocarbon charging,and the openness of the source-reservoir system,rather than by thermal maturity or organofacies alone.The upper sweet-spot interval is interpreted to have functioned as a semi-open system during early stages,in which hydrocarbon generation and expulsion were broadly synchronous,leading to preferential loss of early-generated,biomarker-rich heavy components,whereas progressive shale diagenesis at later stages promoted the retention of highly mature,light hydrocarbons.In contrast,the lower sweet-spot interval represents a relatively closed system,where hydrocarbons generated during multiple stages continuously accumulated and were preserved as mixed charges;overprinting by multi-phase fluids progressively weakened sterane isomerization signals,rendering them unreliable indicators of individual charging events or final thermal maturity.This charging behavior provides a reasonable explanation for anomalously low or distorted biomarker parameters observed in intervals of low or similar maturity.Overall,the proposed charging model reconciles the observed reversal in crude-oil properties and,by shifting the interpretive focus from static maturity assessment to charging dynamics,offers a new theoretical basis for understanding lacustrine shale oil accumulation processes,and guiding sweet-spot selection and exploration-development strategies.
基金financially supported by Fujian Science&Technology Innovation Laboratory for Energy Devices of China(21C LAB)。
文摘Achieving extreme fast charging(XFC,-6 C)capability remains a challenge for Li ion batteries in electric vehicle applications.This work employs time-resolved X-ray diffraction(XRD)to investigate the structural evolution and capacity contributions of a series of LiNi_(x)Co_(y)Mn_(z)O_(2)(x+y+z=1,NCM)cathodes under XFC conditions.All NCM cathodes(NCM-92,NCM-83,and NCM-622)deliver -60%of their capacities with less than 2%unit cell volume expansion during the H1-H2 phase transition,but the subsequent H2-H3 phase transition exhibits significant compositional and rate dependence.The NCM-92 cathode shows a maximum d-spacing shrinkage of-5.3%at 6 C,which is larger than that of NCM-83(-4.1%)and NCM-622(-0.05%).Furthermore,NCM-92 follows a“phase heterogeneity”pathway for its structural evolution above 4.2 V,distinct from the“solid-solution”pathway observed in NCM-83 and NCM-622.This phase heterogeneity is evidenced by the splitting of the(003)diffraction peak and a decrease in intensity during the H2-H3 phase transition,indicating the formation of lithium-rich/depleted domains.These findings establish a direct correlation between cathode composition,structural dynamics,and XFC performance,highlighting a critical trade-off between structural stability and fast-charging capability in nickel-rich layered oxides.
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.
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 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.
