Accurate State-of-Health(SOH)prediction is critical for the safe and efficient operation of lithium-ion batteries(LiBs).However,conventional methods struggle with the highly nonlinear electrochemical dynamics and decl...Accurate State-of-Health(SOH)prediction is critical for the safe and efficient operation of lithium-ion batteries(LiBs).However,conventional methods struggle with the highly nonlinear electrochemical dynamics and declining accuracy over long-horizon forecasting.To address these limitations,this study proposes CIPF-Informer,a novel digital twin framework that integrates the Informer architecture with Channel Independence(CI)and a Particle Filter(PF).The CI mechanism enhances robustness by decoupling multivariate state dependencies,while the PF captures the complex stochastic variations missed by purely deterministic models.The proposed framework was evaluated using the Massachusetts Institute of Technology(MIT)battery dataset against benchmark deep learning models.Results demonstrate that CIPF-Informer consistently achieves superior performance,in multivariate and long sequence forecasting scenarios.By effectively synergizing a model-based method with a data-driven model,CIPF-Informer provides a more reliable pathway for advancing Battery Management System(BMS)technologies,contributing to the development of safer and more sustainable energy storage systems.展开更多
The operational Tropical Regional Atmospheric Model System(TRAMS)often underestimates initial typhoon intensity when using the global analysis field as the initial condition.The TRAMS tropical cyclone(TC)initializatio...The operational Tropical Regional Atmospheric Model System(TRAMS)often underestimates initial typhoon intensity when using the global analysis field as the initial condition.The TRAMS tropical cyclone(TC)initialization scheme,developed based on the incremental analysis updates(IAU)technique,effectively reduces initial bias.However,the original IAU-based TC initialization scheme only adjusts the wind field at the analysis moment,with other variables adjusted implicitly under the model's constraints according to a gradually inserted wind increment(named“univariate adjustment scheme”hereafter).The univariate adjustment scheme requires approximately 3 h to reach a dynamic equilibrium state,which constrains the assimilation of hourly TC observations and causes excessive dissipation of meaningful short-wave information in adjustment increments.To address this limitation,this study develops a multivariate adjustment IAU-based TC initialization scheme that incorporates gradient wind balance and hydrostatic balance as its largescale constraints.Numerical experiments with TC Hato(2017)demonstrate that the multivariate adjustment scheme reduces the IAU relaxation time to 1 h while marginally improving forecast skill.These findings are consistently replicated across 12 additional TC cases.The development of the IAU-based multivariate adjustment initialization scheme establishes a foundation for 4-D initialization using hourly TC observations.展开更多
The zero coprime system equivalence is one of important research in the theory of multidimensional system equivalence,and is closely related to zero coprime equivalence of multivariate polynomial matrices.We first dis...The zero coprime system equivalence is one of important research in the theory of multidimensional system equivalence,and is closely related to zero coprime equivalence of multivariate polynomial matrices.We first discuss the relation between zero coprime equivalence and unimodular equivalence for polynomial matrices.Then,we investigate the zero coprime equivalence problem for several classes of polynomial matrices,some novel findings and criteria on reducing these matrices to their Smith normal forms are obtained.Finally,an example is provided to illustrate the main results.展开更多
The significance of accurately forecasting natural gas prices is far-reaching and significant,not only for the stable operation of the energy market,but also as a key element in promoting sustainable development and a...The significance of accurately forecasting natural gas prices is far-reaching and significant,not only for the stable operation of the energy market,but also as a key element in promoting sustainable development and addressing environmental challenges.However,natural gas prices are affected by multiple source factors,presenting complex,unstable nonlinear characteristics hindering the improvement of the prediction accuracy of existing models.To address this issue,this study proposes an innovative multivariate combined forecasting model for natural gas prices.Initially,the study meticulously identifies and introduces 16 variables impacting natural gas prices across five crucial dimensions:the production,marketing,commodities,political and economic indicators of the United States and temperature.Subsequently,this study employs the least absolute shrinkage and selection operator,grey relation analysis,and random forest for dimensionality reduction,effectively screening out the most influential key variables to serve as input features for the subsequent learning model.Building upon this foundation,a suite of machine learning models is constructed to ensure precise natural gas price prediction.To further elevate the predictive performance,an intelligent algorithm for parameter optimization is incorporated,addressing potential limitations of individual models.To thoroughly assess the prediction accuracy of the proposed model,this study conducts three experiments using monthly natural gas trading prices.These experiments incorporate 19 benchmark models for comparative analysis,utilizing five evaluation metrics to quantify forecasting effectiveness.Furthermore,this study conducts in-depth validation of the proposed model's effectiveness through hypothesis testing,discussions on the improvement ratio of forecasting performance,and case studies on other energy prices.The empirical results demonstrate that the multivariate combined forecasting method developed in this study surpasses other comparative models in forecasting accuracy.It offers new perspectives and methodologies for natural gas price forecasting while also providing valuable insights for other energy price forecasting studies.展开更多
Strategies for achieving high-energy-density lithium-ion batteries include using high-capacity materials such as high-nickel NCM,increasing the active material content in the electrode by utilizing high-conductivity c...Strategies for achieving high-energy-density lithium-ion batteries include using high-capacity materials such as high-nickel NCM,increasing the active material content in the electrode by utilizing high-conductivity carbon nanotubes(CNT)conductive materials,and electrode thickening.However,these methods are still limited due to the limitation in the capacity of high-nickel NCM,aggregation of CNT conductive materials,and nonuniform material distribution of thick-film electrodes,which ultimately damage the mechanical and electrical integrity of the electrode,leading to a decrease in electrochemical performance.Here,we present an integrated binder-CNT composite dispersion solution to realize a high-solids-content(>77 wt%)slurry for high-mass-loading electrodes and to mitigate the migration of binder and conductive additives.Indeed,the approach reduces solvent usage by approximately 30%and ensures uniform conductive additive-binder domain distribution during electrode manufacturing,resulting in improved coating quality and adhesive strength for high-mass-loading electrodes(>12 mAh cm^(−2)).In terms of various electrode properties,the presented electrode showed low resistance and excellent electrochemical properties despite the low CNT contents of 0.6 wt%compared to the pristine-applied electrode with 0.85 wt%CNT contents.Moreover,our strategy enables faster drying,which increases the coating speed,thereby offering potential energy savings and supporting carbon neutrality in wet-based electrode manufacturing processes.展开更多
Electrochemical models,characterized by high fidelity and physical interpretability,have been applied in var-ious fields such as fast charging,battery state estimation,and battery material design.Currently,widely util...Electrochemical models,characterized by high fidelity and physical interpretability,have been applied in var-ious fields such as fast charging,battery state estimation,and battery material design.Currently,widely utilized single particle-based model exhibits high computational efficiency but suffers from low simulation accuracy under high-rate charge/discharge conditions.In this work,an electrochemical model for lithium-ion batteries based on multi-particle hypothesis is developed.Two particles are employed to represent the electrode char-acteristics of the positive and negative electrodes,respectively.Through theoretical derivation,mathematical equations are established to describe various processes within the battery,including solid-phase diffusion,li-quidphase diffusion,reaction polarization,and ohmic polarization.In addition,a method for obtaining model parameters is proposed.Finally,the model is experimentally validated by using lithium iron phosphate and nickel-cobalt-manganese lithium-ion batteries under constant current conditions.The identified battery elec-trochemical model parameters are within reasonable accuracy as evidenced by the experimental validation results.展开更多
The growing use of lithium-ion batteries in electric transportation and grid-scale storage systems has intensified the need for accurate and highly generalizable state-of-health(SOH)estimation.Conventional approaches ...The growing use of lithium-ion batteries in electric transportation and grid-scale storage systems has intensified the need for accurate and highly generalizable state-of-health(SOH)estimation.Conventional approaches often suffer from reduced accuracy under dynamically uncertain state-of-charge(SOC)operating ranges and heterogeneous aging stresses.This study presents a unified SOH estimation framework that integrates physics-informed modeling,subspace identification,and Transformer-based learning.A reduced-order model is derived from simplified electrochemical dynamics,providing an interpretable and computationally efficient representation of battery behavior.Subspace identification across a wide SOC and SOH range yields degradation-sensitive features,which the Transformer uses to capture long-range aging dynamics via multi-head self-attention.Experiments on LiFePO4 cells under joint-cell training show consistently accurate SOH estimation,with a maximum error of 1.39%,demonstrating the framework’s effectiveness in decoupling SOC and SOH effects.In cross-cell validation,where training and validation are performed on different cells,the model maintains a maximum error of 2.06%,confirming strong generalization to unseen aging trajectories.Comparative experiments on LiFePO_(4)and public LiCoO_(2)datasets confirm the framework’s cross-chemistry applicability.By extracting low-dimensional,physically interpretable features via subspace identification,the framework significantly reduces training cost while maintaining high SOH estimation accuracy,outperforming conventional data-driven models lacking physical guidance.展开更多
Under complex working conditions,accurate prediction of the remaining useful life(RUL)of lithium-ion batteries is of great significance to ensure the stable operation of energy storage systems,the safe driving of elec...Under complex working conditions,accurate prediction of the remaining useful life(RUL)of lithium-ion batteries is of great significance to ensure the stable operation of energy storage systems,the safe driving of electric vehicles,and the continuous power supply of electronic devices.This paper systematically describes the RUL prediction methods of lithium-ion batteries and comprehensively summarizes the development status and future trends in this field.First,the battery degradation mechanisms and lightweight data acquisition are analyzed.Secondly,a systematic classification model is constructed for the more widely used lithium battery RUL prediction methods,and the application characteristics and implementation limitations of different methods are analyzed in detail.An innovative classification framework for hybrid methods is proposed based on the depth of physical-data interaction.Then,collaborative modelling of calendar ageing and cyclic ageing is discussed,revealing their coupled effects and corresponding RUL prediction methods.Finally,the technical bottlenecks faced by the current RUL prediction of lithium batteries are identified,potential solutions are proposed,and the future development trends are outlined.展开更多
Lithium-ion batteries(LIBs),while dominant in energy storage due to high energy density and cycling stability,suffer from severe capacity decay,rate capability degradation,and lithium dendrite formation under low-temp...Lithium-ion batteries(LIBs),while dominant in energy storage due to high energy density and cycling stability,suffer from severe capacity decay,rate capability degradation,and lithium dendrite formation under low-temperature(LT)operation.Therefore,a more comprehensive and systematic understanding of LIB behavior at LT is urgently required.This review article comprehensively reviews recent advancements in electrolyte engineering strategies aimed at improving the low-temperature operational capabilities of LIBs.The study methodically examines critical performance-limiting mechanisms through fundamental analysis of four primary challenges:insufficient ionic conductivity under cryogenic conditions,kinetically hindered charge transfer processes,Li+transport limitations across the solidelectrolyte interphase(SEI),and uncontrolled lithium dendrite growth.The work elaborates on innovative optimization approaches encompassing lithium salt molecular design with tailored dissociation characteristics,solvent matrix optimization through dielectric constant and viscosity regulation,interfacial engineering additives for constructing low-impedance SEI layers,and gel-polymer composite electrolyte systems.Notably,particular emphasis is placed on emerging machine learning-guided electrolyte formulation strategies that enable high-throughput virtual screening of constituent combinations and prediction of structure-property relationships.These artificial intelligence-assisted rational design frameworks demonstrate significant potential for accelerating the development of next-generation LT electrolytes by establishing quantitative composition-performance correlations through advanced data-driven methodologies.展开更多
The accurate state of health(SOH)estimation in lithium-ion batteries represents a critical technological challenge with profound implications for electric vehicle performance and user experience.Precise SOH assessment...The accurate state of health(SOH)estimation in lithium-ion batteries represents a critical technological challenge with profound implications for electric vehicle performance and user experience.Precise SOH assessment not only enables reliable mileage prediction but also ensures operational safety.However,the complex and non-linear capacity fading process during battery cycling poses a challenge to obtaining accurate SOH.To address this issue,this study proposes an effective health factor derived from the local voltage range during the battery charging phase.First,the battery charging phase is divided evenly with reference to voltage intervals,and an importance analysis is conducted on each voltage interval.From these,the voltage interval with the strongest correlation to State of Health(SOH)is extracted as the feature interval.Then,a data-driven framework integrating variational mode decomposition(VMD)with gated recurrent unit(GRU)neural networks enables comprehensive multi-scale temporal feature analysis for enhanced SOH estimation.The methodology begins with rigorous feature engineering to identify and extract optimal health indicators demonstrating superior correlation.Subsequently,the VMD algorithm performs sophisticated signal processing to decompose both the measured capacity and derived health indicators into their constituent intrinsic mode functions and residual components.Finally,a GRU-based neural network is implemented to establish a robust SOH estimation model.Experimental validation using cycling data from different datasets shows that the root mean square error of the estimation results is consistently below 3%,demonstrating the good accuracy and generalisation of the proposed method,using only local data from the charging phase.展开更多
Operating Lithium-ion batteries at their temperature limits is a challenging design task due to explosion risk at high temperatures and rapid degradation at low temperatures.Depending on the battery package design,tho...Operating Lithium-ion batteries at their temperature limits is a challenging design task due to explosion risk at high temperatures and rapid degradation at low temperatures.Depending on the battery package design,those risks can be solved with passive solutions,which require no active cooling or heating.Thecurrentwork aims to optimize the pack design and materials of the type-NCR18650B battery based on a wide range of operation temperature.The lower limit was denoted by cold case while the maximum limit was expressed by hot case.A combined analyticalnumerical approach was developed to model the heat generation inside the battery.A thermal resistance analysis was used to determine the boundary conditions of the numerical model.The governing differential equations for the 1-D heat generation model were solved analytically.The numerical analysis was considered to determine the best battery pack design based on material parameters,number of batteries,and geometrical arrangement.The analytical results revealedthat the cold case canbe selectedas theworst case and thebestmodel wasobtainedusing thehexagonal-shaped 10-battery pack that was covered with Delrin of 1.8 mm in thickness.The numerical results showed that the best model was the hexagonal-shaped 10-battery pack with Delrin of 2 mm in thickness that achieved the largest temperature of−20.6℃ in the cold case.展开更多
To enhance the electrochemical performance of lithium-ion battery anodes with higher silicon content,it is essential to engineer their microstructure for better lithium-ion transport and mitigated volume change as wel...To enhance the electrochemical performance of lithium-ion battery anodes with higher silicon content,it is essential to engineer their microstructure for better lithium-ion transport and mitigated volume change as well.Herein,we suggest an effective approach to control the micropore structure of silicon oxide(SiO_(x))/artificial graphite(AG)composite electrodes using a perforated current collector.The electrode features a unique pore structure,where alternating high-porosity domains and low-porosity domains markedly reduce overall electrode resistance,leading to a 20%improvement in rate capability at a 5C-rate discharge condition.Using microstructure-resolved modeling and simulations,we demonstrate that the patterned micropore structure enhances lithium-ion transport,mitigating the electrolyte concentration gradient of lithium-ion.Additionally,perforating current collector with a chemical etching process increases the number of hydrogen bonding sites and enlarges the interface with the SiO_(x)/AG composite electrode,significantly improving adhesion strength.This,in turn,suppresses mechanical degradation and leads to a 50%higher capacity retention.Thus,regularly arranged micropore structure enabled by the perforated current collector successfully improves both rate capability and cycle life in SiO_(x)/AG composite electrodes,providing valuable insights into electrode engineering.展开更多
The methods to determine time delays and embedding dimensions in the phase space delay reconstruction of multivariate chaotic time series are proposed. Three nonlinear prediction methods of multivariate chaotic tim...The methods to determine time delays and embedding dimensions in the phase space delay reconstruction of multivariate chaotic time series are proposed. Three nonlinear prediction methods of multivariate chaotic time series including local mean prediction, local linear prediction and BP neural networks prediction are considered. The simulation results obtained by the Lorenz system show that no matter what nonlinear prediction method is used, the prediction error of multivariate chaotic time series is much smaller than the prediction error of univariate time series, even if half of the data of univariate time series are used in multivariate time series. The results also verify that methods to determine the time delays and the embedding dimensions are correct from the view of minimizing the prediction error.展开更多
[Objective] The plankton and macrobenthos samples in municipal polluted river were analyzed by different methods, so as to explore the method suitable for biological data analysis in heavy polluted area. [Method] Shan...[Objective] The plankton and macrobenthos samples in municipal polluted river were analyzed by different methods, so as to explore the method suitable for biological data analysis in heavy polluted area. [Method] Shannon-Wiener diversity index, cluster analysis of multivariate statistical analysis and MDS （Non-matric Multi- dimentional Scaling）analysis were used to analyze biological data of phytoplankton, zooplankton and Zoobenthos collected from the representative municipal polluted river in Pearl River Delta. The sediment samples were also collected to determine. Pb, Cd, Hg, Cr, As, Cu, Ni, Zn, as well as CODe, and NH3-N of porewater. Hakanson potential ecological risk index method was used to evaluate the ecological risk. [Re- suit] Shannon-Wiener diversity index analysis results can not effectively reflect the difference of pollution status of various stations in heavy polluted area; despite the presence of some problems, multivariate analysis method is superior to the Shannon-Wiener diversity index method in biological monitoring of heavy polluted river in the city. [Conclusion] The paper provided theoretical basis for biological data analysis in heavy polluted area.展开更多
The analysis result of absolute degree of grey incidence for multivariate time series is often inconsistent with the qualitative analysis. To overcome this shortage, a multivariate absolute degree of grey incidence ba...The analysis result of absolute degree of grey incidence for multivariate time series is often inconsistent with the qualitative analysis. To overcome this shortage, a multivariate absolute degree of grey incidence based on distribution characteristics of points is proposed. Based on the geometric description of multivariate time se- ries, the neighborhood extrema are extracted in the different regions, and a characteristic point set is constructed. Then according to the distribution of the characteristic point set, a characteristic point sequence reflecting the ge- ometric features of multivariate time series is obtained. The incidence analysis between multivariate time series is transformed into the relational analysis between characteristic point sequences, and a grey incidence model is established. The model possesses the properties of translational invariance, transpose and rank transform invari- ance, and satisfies the grey incidence analysis axioms. Finally, two cases are studied and the results prove the ef- fectiveness of the model.展开更多
This paper considers the upper orthant and extremal tail dependence indices for multivariate t-copula. Where, the multivariate t-copula is defined under a correlation structure. The explicit representations of the tai...This paper considers the upper orthant and extremal tail dependence indices for multivariate t-copula. Where, the multivariate t-copula is defined under a correlation structure. The explicit representations of the tail dependence parameters are deduced since the copula of continuous variables is invariant under strictly increasing transformation about the random variables, which are more simple than those obtained in previous research. Then, the local monotonicity of these indices about the correlation coefficient is discussed, and it is concluded that the upper extremal dependence index increases with the correlation coefficient, but the monotonicity of the upper orthant tail dependence index is complex. Some simulations are performed by the Monte Carlo method to verify the obtained results, which are found to be satisfactory. Meanwhile, it is concluded that the obtained conclusions can be extended to any distribution family in which the generating random variable has a regularly varying distribution.展开更多
Abstract Using the method of stepwise multivariate linear regression (SMLR), the quantitative structure activity relationships (QSAR) of two isomeric series of taxol and its derivatives have been studied. It was foun...Abstract Using the method of stepwise multivariate linear regression (SMLR), the quantitative structure activity relationships (QSAR) of two isomeric series of taxol and its derivatives have been studied. It was found that the molar refractivity of the C3′substituent of the C13 side chain has significant correlation with its activity. We deduce that structural changes in the C3′substituents may be critical to the anticancer function. It would be useful to the design and synthesis of taxol like compounds with improved activities.展开更多
基金supported by the Human Resources Development of the Korea Institute of Energy Technology Evaluation and Planning(KETEP)grant funded by the Korea government Ministry of Knowledge Economy(No.RS-2023-00244330)the National Research Foundation of Korea(NRF)grant funded by the Korea government(MSIT)(No.NRF RS-2023-00219052).
文摘Accurate State-of-Health(SOH)prediction is critical for the safe and efficient operation of lithium-ion batteries(LiBs).However,conventional methods struggle with the highly nonlinear electrochemical dynamics and declining accuracy over long-horizon forecasting.To address these limitations,this study proposes CIPF-Informer,a novel digital twin framework that integrates the Informer architecture with Channel Independence(CI)and a Particle Filter(PF).The CI mechanism enhances robustness by decoupling multivariate state dependencies,while the PF captures the complex stochastic variations missed by purely deterministic models.The proposed framework was evaluated using the Massachusetts Institute of Technology(MIT)battery dataset against benchmark deep learning models.Results demonstrate that CIPF-Informer consistently achieves superior performance,in multivariate and long sequence forecasting scenarios.By effectively synergizing a model-based method with a data-driven model,CIPF-Informer provides a more reliable pathway for advancing Battery Management System(BMS)technologies,contributing to the development of safer and more sustainable energy storage systems.
基金supported by the National University of Defense Technology(NUDT)Research Initiation Funding for High-Level Scientific and Technological Innovative Talents(202402-YJRC-LJ-001)the National Natural Science Foundation of China(Grant No.U2142213)+1 种基金the Basic and Applied Basic Research Foundation of Guangdong Province(Grants 2025A1515011835,2022A1515011870)the National Natural Science Foundation of China(Grant No.42305167)。
文摘The operational Tropical Regional Atmospheric Model System(TRAMS)often underestimates initial typhoon intensity when using the global analysis field as the initial condition.The TRAMS tropical cyclone(TC)initialization scheme,developed based on the incremental analysis updates(IAU)technique,effectively reduces initial bias.However,the original IAU-based TC initialization scheme only adjusts the wind field at the analysis moment,with other variables adjusted implicitly under the model's constraints according to a gradually inserted wind increment(named“univariate adjustment scheme”hereafter).The univariate adjustment scheme requires approximately 3 h to reach a dynamic equilibrium state,which constrains the assimilation of hourly TC observations and causes excessive dissipation of meaningful short-wave information in adjustment increments.To address this limitation,this study develops a multivariate adjustment IAU-based TC initialization scheme that incorporates gradient wind balance and hydrostatic balance as its largescale constraints.Numerical experiments with TC Hato(2017)demonstrate that the multivariate adjustment scheme reduces the IAU relaxation time to 1 h while marginally improving forecast skill.These findings are consistently replicated across 12 additional TC cases.The development of the IAU-based multivariate adjustment initialization scheme establishes a foundation for 4-D initialization using hourly TC observations.
基金Supported by the National Natural Science Foundation of China(12271154)the Natural Science Foundation of Hunan Province(2022JJ30234)the Postgraduate Scientific Research Innovation Project of Hunan Province(CX20231032)。
文摘The zero coprime system equivalence is one of important research in the theory of multidimensional system equivalence,and is closely related to zero coprime equivalence of multivariate polynomial matrices.We first discuss the relation between zero coprime equivalence and unimodular equivalence for polynomial matrices.Then,we investigate the zero coprime equivalence problem for several classes of polynomial matrices,some novel findings and criteria on reducing these matrices to their Smith normal forms are obtained.Finally,an example is provided to illustrate the main results.
基金supported by the funding from the Humanities and Social Science Fund of Ministry of Education of China(No.22YJCZH028)National Natural Science Foundation of China(Grant No.72303001)+3 种基金Fundamental Research Funds for the Central Universities(No.JUSRP124043)Anhui Provincial Excellent Young Scientists Fund for Universities(No.2024AH030001)Anhui Education Department Excellent Young Teachers Fund(No.YQYB2024021)Basic Research Program of Jiangsu(No.BK20251593)。
文摘The significance of accurately forecasting natural gas prices is far-reaching and significant,not only for the stable operation of the energy market,but also as a key element in promoting sustainable development and addressing environmental challenges.However,natural gas prices are affected by multiple source factors,presenting complex,unstable nonlinear characteristics hindering the improvement of the prediction accuracy of existing models.To address this issue,this study proposes an innovative multivariate combined forecasting model for natural gas prices.Initially,the study meticulously identifies and introduces 16 variables impacting natural gas prices across five crucial dimensions:the production,marketing,commodities,political and economic indicators of the United States and temperature.Subsequently,this study employs the least absolute shrinkage and selection operator,grey relation analysis,and random forest for dimensionality reduction,effectively screening out the most influential key variables to serve as input features for the subsequent learning model.Building upon this foundation,a suite of machine learning models is constructed to ensure precise natural gas price prediction.To further elevate the predictive performance,an intelligent algorithm for parameter optimization is incorporated,addressing potential limitations of individual models.To thoroughly assess the prediction accuracy of the proposed model,this study conducts three experiments using monthly natural gas trading prices.These experiments incorporate 19 benchmark models for comparative analysis,utilizing five evaluation metrics to quantify forecasting effectiveness.Furthermore,this study conducts in-depth validation of the proposed model's effectiveness through hypothesis testing,discussions on the improvement ratio of forecasting performance,and case studies on other energy prices.The empirical results demonstrate that the multivariate combined forecasting method developed in this study surpasses other comparative models in forecasting accuracy.It offers new perspectives and methodologies for natural gas price forecasting while also providing valuable insights for other energy price forecasting studies.
基金supported by the National Research Foundation of Korea(NRF)grant funded by the Korea government(MSIT)(No.2022M3H4A6A0103720142)the National Research Council of Science&Technology(NST)grant by the Korea government(MSIT)(No.GTL24011-000)+1 种基金the Technology Innovation Program(RS-2024-00404165)through the Korea Planning&Evaluation Institute of Industrial Technology(KEIT)funded by the Ministry of Trade,Industry&Energy(MOTIE,Korea)supported by the Samsung SDI Co.Ltd.and the Korea Institute of Science and Technology(KIST)institutional program(2E33942,2E3394B)。
文摘Strategies for achieving high-energy-density lithium-ion batteries include using high-capacity materials such as high-nickel NCM,increasing the active material content in the electrode by utilizing high-conductivity carbon nanotubes(CNT)conductive materials,and electrode thickening.However,these methods are still limited due to the limitation in the capacity of high-nickel NCM,aggregation of CNT conductive materials,and nonuniform material distribution of thick-film electrodes,which ultimately damage the mechanical and electrical integrity of the electrode,leading to a decrease in electrochemical performance.Here,we present an integrated binder-CNT composite dispersion solution to realize a high-solids-content(>77 wt%)slurry for high-mass-loading electrodes and to mitigate the migration of binder and conductive additives.Indeed,the approach reduces solvent usage by approximately 30%and ensures uniform conductive additive-binder domain distribution during electrode manufacturing,resulting in improved coating quality and adhesive strength for high-mass-loading electrodes(>12 mAh cm^(−2)).In terms of various electrode properties,the presented electrode showed low resistance and excellent electrochemical properties despite the low CNT contents of 0.6 wt%compared to the pristine-applied electrode with 0.85 wt%CNT contents.Moreover,our strategy enables faster drying,which increases the coating speed,thereby offering potential energy savings and supporting carbon neutrality in wet-based electrode manufacturing processes.
基金Supported by the National Natural Science Foundation of China(Grant Nos.52407238,52177210)the Youth Foundation of Shandong Provincial Natural Science Foundation(Grant No.ZR2023QE036).
文摘Electrochemical models,characterized by high fidelity and physical interpretability,have been applied in var-ious fields such as fast charging,battery state estimation,and battery material design.Currently,widely utilized single particle-based model exhibits high computational efficiency but suffers from low simulation accuracy under high-rate charge/discharge conditions.In this work,an electrochemical model for lithium-ion batteries based on multi-particle hypothesis is developed.Two particles are employed to represent the electrode char-acteristics of the positive and negative electrodes,respectively.Through theoretical derivation,mathematical equations are established to describe various processes within the battery,including solid-phase diffusion,li-quidphase diffusion,reaction polarization,and ohmic polarization.In addition,a method for obtaining model parameters is proposed.Finally,the model is experimentally validated by using lithium iron phosphate and nickel-cobalt-manganese lithium-ion batteries under constant current conditions.The identified battery elec-trochemical model parameters are within reasonable accuracy as evidenced by the experimental validation results.
基金supported by the National Natural Science Foundation of China(No.52207228)the Beijing Natural Science Foundation,China(No.3224070)the National Natural Science Foundation of China(No.52077208).
文摘The growing use of lithium-ion batteries in electric transportation and grid-scale storage systems has intensified the need for accurate and highly generalizable state-of-health(SOH)estimation.Conventional approaches often suffer from reduced accuracy under dynamically uncertain state-of-charge(SOC)operating ranges and heterogeneous aging stresses.This study presents a unified SOH estimation framework that integrates physics-informed modeling,subspace identification,and Transformer-based learning.A reduced-order model is derived from simplified electrochemical dynamics,providing an interpretable and computationally efficient representation of battery behavior.Subspace identification across a wide SOC and SOH range yields degradation-sensitive features,which the Transformer uses to capture long-range aging dynamics via multi-head self-attention.Experiments on LiFePO4 cells under joint-cell training show consistently accurate SOH estimation,with a maximum error of 1.39%,demonstrating the framework’s effectiveness in decoupling SOC and SOH effects.In cross-cell validation,where training and validation are performed on different cells,the model maintains a maximum error of 2.06%,confirming strong generalization to unseen aging trajectories.Comparative experiments on LiFePO_(4)and public LiCoO_(2)datasets confirm the framework’s cross-chemistry applicability.By extracting low-dimensional,physically interpretable features via subspace identification,the framework significantly reduces training cost while maintaining high SOH estimation accuracy,outperforming conventional data-driven models lacking physical guidance.
基金supported by the National Natural Science Foundation of China(No.U23A20651)the Central Government Guides Local Science and Technology Development Foundation(No.2023ZYDF022)+1 种基金the Sichuan Science and Technology Program(2024ZDZX0031)the Open Fund Project of State Key Laboratory of Mining Response and Disaster Prevention and Control in Deep Coal Mines(No.SKLMRDPC23KF19).
文摘Under complex working conditions,accurate prediction of the remaining useful life(RUL)of lithium-ion batteries is of great significance to ensure the stable operation of energy storage systems,the safe driving of electric vehicles,and the continuous power supply of electronic devices.This paper systematically describes the RUL prediction methods of lithium-ion batteries and comprehensively summarizes the development status and future trends in this field.First,the battery degradation mechanisms and lightweight data acquisition are analyzed.Secondly,a systematic classification model is constructed for the more widely used lithium battery RUL prediction methods,and the application characteristics and implementation limitations of different methods are analyzed in detail.An innovative classification framework for hybrid methods is proposed based on the depth of physical-data interaction.Then,collaborative modelling of calendar ageing and cyclic ageing is discussed,revealing their coupled effects and corresponding RUL prediction methods.Finally,the technical bottlenecks faced by the current RUL prediction of lithium batteries are identified,potential solutions are proposed,and the future development trends are outlined.
基金the financial support from the Key Project of Shaanxi Provincial Natural Science Foundation-Key Project of Laboratory(2025SYS-SYSZD-117)the Natural Science Basic Research Program of Shaanxi(2025JCYBQN-125)+8 种基金Young Talent Fund of Xi'an Association for Science and Technology(0959202513002)the Key Industrial Chain Technology Research Program of Xi'an(24ZDCYJSGG0048)the Key Research and Development Program of Xianyang(L2023-ZDYF-SF-077)Postdoctoral Fellowship Program of CPSF(GZC20241442)Shaanxi Postdoctoral Science Foundation(2024BSHSDZZ070)Research Funds for the Interdisciplinary Projects,CHU(300104240913)the Fundamental Research Funds for the Central Universities,CHU(300102385739,300102384201,300102384103)the Scientific Innovation Practice Project of Postgraduate of Chang'an University(300103725063)the financial support from the Australian Research Council。
文摘Lithium-ion batteries(LIBs),while dominant in energy storage due to high energy density and cycling stability,suffer from severe capacity decay,rate capability degradation,and lithium dendrite formation under low-temperature(LT)operation.Therefore,a more comprehensive and systematic understanding of LIB behavior at LT is urgently required.This review article comprehensively reviews recent advancements in electrolyte engineering strategies aimed at improving the low-temperature operational capabilities of LIBs.The study methodically examines critical performance-limiting mechanisms through fundamental analysis of four primary challenges:insufficient ionic conductivity under cryogenic conditions,kinetically hindered charge transfer processes,Li+transport limitations across the solidelectrolyte interphase(SEI),and uncontrolled lithium dendrite growth.The work elaborates on innovative optimization approaches encompassing lithium salt molecular design with tailored dissociation characteristics,solvent matrix optimization through dielectric constant and viscosity regulation,interfacial engineering additives for constructing low-impedance SEI layers,and gel-polymer composite electrolyte systems.Notably,particular emphasis is placed on emerging machine learning-guided electrolyte formulation strategies that enable high-throughput virtual screening of constituent combinations and prediction of structure-property relationships.These artificial intelligence-assisted rational design frameworks demonstrate significant potential for accelerating the development of next-generation LT electrolytes by establishing quantitative composition-performance correlations through advanced data-driven methodologies.
基金supported by the Scientific Research Program Funded by Shaanxi Provincial Education Department(Program No.23JP100)the Key Natural Science Research Project of Shaanxi Energy Institute:Comprehensive Characterization Study of Lithium-Ion Batteries for New Energy Vehicles(Project No.23QNZRZ01).
文摘The accurate state of health(SOH)estimation in lithium-ion batteries represents a critical technological challenge with profound implications for electric vehicle performance and user experience.Precise SOH assessment not only enables reliable mileage prediction but also ensures operational safety.However,the complex and non-linear capacity fading process during battery cycling poses a challenge to obtaining accurate SOH.To address this issue,this study proposes an effective health factor derived from the local voltage range during the battery charging phase.First,the battery charging phase is divided evenly with reference to voltage intervals,and an importance analysis is conducted on each voltage interval.From these,the voltage interval with the strongest correlation to State of Health(SOH)is extracted as the feature interval.Then,a data-driven framework integrating variational mode decomposition(VMD)with gated recurrent unit(GRU)neural networks enables comprehensive multi-scale temporal feature analysis for enhanced SOH estimation.The methodology begins with rigorous feature engineering to identify and extract optimal health indicators demonstrating superior correlation.Subsequently,the VMD algorithm performs sophisticated signal processing to decompose both the measured capacity and derived health indicators into their constituent intrinsic mode functions and residual components.Finally,a GRU-based neural network is implemented to establish a robust SOH estimation model.Experimental validation using cycling data from different datasets shows that the root mean square error of the estimation results is consistently below 3%,demonstrating the good accuracy and generalisation of the proposed method,using only local data from the charging phase.
文摘Operating Lithium-ion batteries at their temperature limits is a challenging design task due to explosion risk at high temperatures and rapid degradation at low temperatures.Depending on the battery package design,those risks can be solved with passive solutions,which require no active cooling or heating.Thecurrentwork aims to optimize the pack design and materials of the type-NCR18650B battery based on a wide range of operation temperature.The lower limit was denoted by cold case while the maximum limit was expressed by hot case.A combined analyticalnumerical approach was developed to model the heat generation inside the battery.A thermal resistance analysis was used to determine the boundary conditions of the numerical model.The governing differential equations for the 1-D heat generation model were solved analytically.The numerical analysis was considered to determine the best battery pack design based on material parameters,number of batteries,and geometrical arrangement.The analytical results revealedthat the cold case canbe selectedas theworst case and thebestmodel wasobtainedusing thehexagonal-shaped 10-battery pack that was covered with Delrin of 1.8 mm in thickness.The numerical results showed that the best model was the hexagonal-shaped 10-battery pack with Delrin of 2 mm in thickness that achieved the largest temperature of−20.6℃ in the cold case.
基金supported by the National Research Foundation of Korea(NRF)grant funded by the Korean government(MSIT)(No.NRF-2021M3H4A1A02048529)the Ministry of Trade,Industry and Energy(MOTIE)of the Korean government under grant No.RS-2022-00155854support from the DGIST Supercomputing and Big Data Center.
文摘To enhance the electrochemical performance of lithium-ion battery anodes with higher silicon content,it is essential to engineer their microstructure for better lithium-ion transport and mitigated volume change as well.Herein,we suggest an effective approach to control the micropore structure of silicon oxide(SiO_(x))/artificial graphite(AG)composite electrodes using a perforated current collector.The electrode features a unique pore structure,where alternating high-porosity domains and low-porosity domains markedly reduce overall electrode resistance,leading to a 20%improvement in rate capability at a 5C-rate discharge condition.Using microstructure-resolved modeling and simulations,we demonstrate that the patterned micropore structure enhances lithium-ion transport,mitigating the electrolyte concentration gradient of lithium-ion.Additionally,perforating current collector with a chemical etching process increases the number of hydrogen bonding sites and enlarges the interface with the SiO_(x)/AG composite electrode,significantly improving adhesion strength.This,in turn,suppresses mechanical degradation and leads to a 50%higher capacity retention.Thus,regularly arranged micropore structure enabled by the perforated current collector successfully improves both rate capability and cycle life in SiO_(x)/AG composite electrodes,providing valuable insights into electrode engineering.
文摘The methods to determine time delays and embedding dimensions in the phase space delay reconstruction of multivariate chaotic time series are proposed. Three nonlinear prediction methods of multivariate chaotic time series including local mean prediction, local linear prediction and BP neural networks prediction are considered. The simulation results obtained by the Lorenz system show that no matter what nonlinear prediction method is used, the prediction error of multivariate chaotic time series is much smaller than the prediction error of univariate time series, even if half of the data of univariate time series are used in multivariate time series. The results also verify that methods to determine the time delays and the embedding dimensions are correct from the view of minimizing the prediction error.
基金Supported by National Natural Science Foundation of China(41001341)Natural Science Foundation of Guangdong Province(9152800001000007)+1 种基金Open Fund ofState Key Laboratory of Subtropical Building Science(2011KB12)Basic Scientific Research Expenses Project of Central Universities(2012ZM0082)~~
文摘[Objective] The plankton and macrobenthos samples in municipal polluted river were analyzed by different methods, so as to explore the method suitable for biological data analysis in heavy polluted area. [Method] Shannon-Wiener diversity index, cluster analysis of multivariate statistical analysis and MDS （Non-matric Multi- dimentional Scaling）analysis were used to analyze biological data of phytoplankton, zooplankton and Zoobenthos collected from the representative municipal polluted river in Pearl River Delta. The sediment samples were also collected to determine. Pb, Cd, Hg, Cr, As, Cu, Ni, Zn, as well as CODe, and NH3-N of porewater. Hakanson potential ecological risk index method was used to evaluate the ecological risk. [Re- suit] Shannon-Wiener diversity index analysis results can not effectively reflect the difference of pollution status of various stations in heavy polluted area; despite the presence of some problems, multivariate analysis method is superior to the Shannon-Wiener diversity index method in biological monitoring of heavy polluted river in the city. [Conclusion] The paper provided theoretical basis for biological data analysis in heavy polluted area.
基金Supported by the National Natural Science Foundation of China(71101043,70901041,71171113)the Joint Research Project of National Natural Science Foundation of China and Royal Society of UK(71111130211)+4 种基金the Major Program of National Funds of Social Science of China(10ZD&014,11&ZD168)the Doctoral Fundof Ministry of Education of China(20093218120032,200802870020)the Qinglan Project for Excellent Youth Teacherin Jiangsu Province(China)Research Funding in Nanjing University of Aeronautics and Astronautics(NR2011002)the Central University Scientific Research Expenses of HoHai University(2011B09914,2010B11114)~~
文摘The analysis result of absolute degree of grey incidence for multivariate time series is often inconsistent with the qualitative analysis. To overcome this shortage, a multivariate absolute degree of grey incidence based on distribution characteristics of points is proposed. Based on the geometric description of multivariate time se- ries, the neighborhood extrema are extracted in the different regions, and a characteristic point set is constructed. Then according to the distribution of the characteristic point set, a characteristic point sequence reflecting the ge- ometric features of multivariate time series is obtained. The incidence analysis between multivariate time series is transformed into the relational analysis between characteristic point sequences, and a grey incidence model is established. The model possesses the properties of translational invariance, transpose and rank transform invari- ance, and satisfies the grey incidence analysis axioms. Finally, two cases are studied and the results prove the ef- fectiveness of the model.
基金The National Natural Science Foundation of China(No.11001052,11171065)the National Science Foundation of Jiangsu Province(No.BK2011058)the Science Foundation of Nanjing University of Posts and Telecommunications(No.JG00710JX57)
文摘This paper considers the upper orthant and extremal tail dependence indices for multivariate t-copula. Where, the multivariate t-copula is defined under a correlation structure. The explicit representations of the tail dependence parameters are deduced since the copula of continuous variables is invariant under strictly increasing transformation about the random variables, which are more simple than those obtained in previous research. Then, the local monotonicity of these indices about the correlation coefficient is discussed, and it is concluded that the upper extremal dependence index increases with the correlation coefficient, but the monotonicity of the upper orthant tail dependence index is complex. Some simulations are performed by the Monte Carlo method to verify the obtained results, which are found to be satisfactory. Meanwhile, it is concluded that the obtained conclusions can be extended to any distribution family in which the generating random variable has a regularly varying distribution.
文摘Abstract Using the method of stepwise multivariate linear regression (SMLR), the quantitative structure activity relationships (QSAR) of two isomeric series of taxol and its derivatives have been studied. It was found that the molar refractivity of the C3′substituent of the C13 side chain has significant correlation with its activity. We deduce that structural changes in the C3′substituents may be critical to the anticancer function. It would be useful to the design and synthesis of taxol like compounds with improved activities.
