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
Silicon possesses a high theoretical capacity,making it a potential contender for lithium-ion battery(LIB)anodes.Nonetheless,its practical usage is challenged by low electrical conductivity and significant volume expa...Silicon possesses a high theoretical capacity,making it a potential contender for lithium-ion battery(LIB)anodes.Nonetheless,its practical usage is challenged by low electrical conductivity and significant volume expansion during cycling.Here,we synthesized a novel silicon/carbon(Si/C)anode doped with ZnO via a template-derived method and high-temperature carbonization.The carbon structure,originated from metal-organic frameworks(MOFs)and ZnO doping,substantially enhanced the electrochemical properties of the composite material.It exhibited an initial capacity of 2100.3 mA h g^(-1)at a current density of 0.2 A g^(-1)and demonstrated excellent capacity retention over successive cycles.Moreover,the composite material displayed superior rate performance at higher current densities of 2 A g^(-1)and 3 A g^(-1).To address the low initial Coulombic efficiency(ICE)of siliconbased materials,we adopted a direct contact prelithiation approach and optimized the lithiation process by controlling the prelithiation time.After 30 min of prelithiation,the ICE reached 97.9%,thereby reducing the initial irreversible capacity loss(ICL)and realizing stable discharge-charge in subsequent cycles.This rational design provides valuable insights for achieving high-performance silicon anode.展开更多
Commercial carbonate electrolytes suffer from ion transport difficulty in bulk electrolytes and interphase at low temperatures,bringing challenges to the application of lithium-ion batteries(LIBs)at low temperatures.H...Commercial carbonate electrolytes suffer from ion transport difficulty in bulk electrolytes and interphase at low temperatures,bringing challenges to the application of lithium-ion batteries(LIBs)at low temperatures.Herein,the ester solvent of methyl propionate(MP)with low melting point and low viscosity was used to tackle ion transport difficulty in electrolytes.Fluorinated ester was further added to accelerate interfacial transport through intermolecular interactions.The influence of fluorinated esters with different fluorination degrees on the solvation structure of electrolytes and the performance of batteries was further studied.As a result,methyl pentafluoropropionate(M5F)with five fluorine atoms was selected for its optimal interactions with both Li+and MP solvent in the primary solvation structure,contributing to desired solvation structure for fast interfacial transport.The LiFePO_(4)(LFP)||graphite cell with LiFSI-MP-M5F electrolyte exhibited a high cyclability of 85.8%after 120 cycles and retained 81.2%of room-temperature capacity when charged and discharged at−30℃.1 Ah LFP||graphite pouch cell with high cathode loading(20 mg/cm^(2))in LiFSI-MP-M5F electrolyte exhibited 0.85 Ah capacity when charged and discharged at−20℃.This work provides a guidance for electrolyte design by synergistic fluorinated and non-fluorinated solvents for LIBs at low-temperature application.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
Carbon coatings for silicon(Si)-based anode materials are essential for designing high-performance Li-ion batteries(LIBs).The coatings prevent direct contact with the electrolyte and enhance anode performance.However,...Carbon coatings for silicon(Si)-based anode materials are essential for designing high-performance Li-ion batteries(LIBs).The coatings prevent direct contact with the electrolyte and enhance anode performance.However,conventional carbon coatings are limited by their volume expansion and structural degradation,which lead to capacity fading and reduced durability.This study introduces a scalable and practical one-step carbon-coating strategy for directly coating silicon suboxide(SiO_(x))-based materials using aqueous quasi-defect-free reduced graphene oxide(QrGO)without post-treatment,unlike conventional graphene oxide(GO)-based coating methods.This simple process enables uniform encapsulation with QrGO for a highly adhesive and conductive coating.The QrGO-based composite anode material has several advantages,including reduced cracking due to volume expansion and enhanced charge carrier transport,as well as an increased Si content of 20 wt.%compared to the 5 wt.%in typical commercial Si-based active materials.In particular,the capacity retention of the QrGO-coated Si electrodes dramatically increases at high C-rate.The full cell exhibited long-term stability and capacity that were twice that of commercial SiO_(x)-based cells.Therefore,the QrGO-based one-step coating process represents a scalable,transformative,and commercially viable strategy for developing high-performance LIBs.展开更多
Battery energy storage systems bolster power grids’absorption capacity,however,battery safety issues remain a formidable challenge.Timely and pre-cise fault diagnosis,coupled with early-stage fault warn-ings,is cruci...Battery energy storage systems bolster power grids’absorption capacity,however,battery safety issues remain a formidable challenge.Timely and pre-cise fault diagnosis,coupled with early-stage fault warn-ings,is crucial.This study introduces an eigen decompo-sition-based multi-fault diagnosis approach for lithi-umion battery packs,enabling online diagnosis of short circuits,electrical connection faults,and voltage sensor malfunctions.By incorporating an interleaved measurement topology,precise fault type differentiation is achieved.Eigenvector matching analysis is employed to increase sensitivity to fault characteristics and enhance robustness.The interleaved topology can be seamlessly integrated using common voltage measurement solutions,eliminating the need for additional design complexities,while sensor number redundancy enhances fault tolerance of battery management systems(BMS).A cloud-side collaboration method is proposed,where the BMS functions as an edge device for specific data computations,while the parameters are fine-tuned by the server through big data analytics.This approach circumvents cumbersome server calculations,thereby curbing server cost escalation.The edge computing process is divided into two steps,with partial calculations often sufficient to evaluate battery safety,thus reducing the computational load on edge devices.Several battery tests are conducted,and the results confirm the method’s capability,feasibility,and validity in early-stage fault diagnosis.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
With the rapid development of electric vehicles and grid-scale renewable integration,the demand for lithium-ion batteries(LIBs)has significantly increased with high expectations on enhanced energy density,cycle stabil...With the rapid development of electric vehicles and grid-scale renewable integration,the demand for lithium-ion batteries(LIBs)has significantly increased with high expectations on enhanced energy density,cycle stability,and failure resilience.Electrochemical models(EMs),serving as pivotal mechanismdriven analytical frameworks in battery research and applications,demonstrate unprecedented quantitative fidelity in characterizing intricate multi-physics dynamics for the next-generation battery management systems(BMS).The breakthrough innovations in artificial intelligence(AI)driven methods have revolutionized the dynamic modeling of LIBs.However,the deployment of AI-augmented EMs in BMS faces significant identifiability challenges due to strong parameter coupling.In addition,research on model simplification,parameter determination,and dynamic parameter identification remains largely fragmented.There is a lack of a comprehensive review to pave the way for the cross-domain innovations in BMS.To fill this gap,this paper presents a systematic review of the EMs for LIBs and examines the advancements in parameter determination techniques from both experimental measurement and numerical simulation perspectives.Besides,a comprehensive assessment of the progress in parameter identification from the standpoint of dynamic recognition is presented,encompassing both modelbased approaches and intelligent methods.Additionally,from the BMS standpoint,the strengths and limitations of existing approaches are evaluated.Finally,a coordinated framework for multi-stage identification needs to be established in the future.The potential of digital twins(DT),deep reinforcement learning(DRL),and large language models(LLMs)in enhancing EMs also warrants further exploration.The purpose of this work is to provide insights and guidance for the future development of EMs in LIB applications.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
Lithium-ion(Li-ion)batteries stand as the dominant energy storage solution,despite their widespread adoption,precisely determining the state of charge(SOC)continues to pose significant difficulties,with direct implica...Lithium-ion(Li-ion)batteries stand as the dominant energy storage solution,despite their widespread adoption,precisely determining the state of charge(SOC)continues to pose significant difficulties,with direct implications for battery safety,operational reliability,and overall performance.Current SOC estimation techniques often demonstrate limited accuracy,particularly when confronted with complex operational scenarios and wide temperature variations,where their generalization capacity and dynamic adaptation prove insufficient.To address these shortcomings,this work presents a PSO-TCN-Transformer network model for SOC estimation.This research uses the Particle Swarm Optimization(PSO)method to automatically configure the architectural parameters of the Temporal Convolutional Network(TCN)and Transformer components.This automated optimization enhances the model’s ability to represent the dynamically evolving nature of SOC.Additionally,this integrated framework significantly increases the model’s capacity to capture SOC dynamics in complex operational scenarios.During training and evaluation using a comprehensive dataset that covers complex operating conditions and a broad temperature spanning from−20℃ to 40℃,the proposed model achieves a root mean square error(RMSE)of less than 0.6%,a maximum absolute error(MAXE)below 4.0%,and a coefficient of determination(R^(2))of 99.99%.Additional comparative experiments on data from an energy storage company further verify the model’s superior performance,with an RMSE of 1.18%and an MAXE of 1.95%.The implications of this work extend to the development of optimization strategies and hybrid architectures,providing insights that can be adapted for state estimation across a range of complex dynamic systems.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
Silicon(Si)-based anodes have emerged as promising candidates for the next-generation lithium-ion batteries(LIBs)due to their high theoretical capacity(4200 mAh g^(-1)).However,their further application is hindered by...Silicon(Si)-based anodes have emerged as promising candidates for the next-generation lithium-ion batteries(LIBs)due to their high theoretical capacity(4200 mAh g^(-1)).However,their further application is hindered by critical challenges,including severe volume expansion(~300%),formation of unstable solid electrolyte interphase(SEI),and inherently low conductivity.While extensive research has sought to alleviate the substantial internal stress caused by volume expansion through the rational design of Si-based anode structures,the underlying mechanisms that govern these improvements remain insufficiently understood,leaving significant gaps in mechanical and interface electrical failure.To build a comprehensive understanding relationship between structural design and performance enhancement of Si-based anodes,this review first analyzes the characteristics of various Sibased anode structures and their associated internal stresses.Subsequently,it summarizes effective strategies to optimize the performance of Si-based anodes,including doping design,novel electrolyte design,and fu nctional binder design.Additionally,we assess emerging technologies with high commercial potential for structural design and interfacial modification,such as porous carbon carriers,chemical vapor deposition(CVD),spray granulation,and pre-lithiation.Finally,this work provides perspectives on the structural design of Si-based anodes.Overall,this review systematically summarizes modification strategies for Si-based anodes through structural regulation and interface engineering,thereby providing a foundation for advanced structural and interfacial design.展开更多
Micro-sized silicon(mSi)anodes offer high capacity for next-generation lithium-ion batteries but suffer from severe volume changes,causing unstable interphases and poor cycling.Traditional electrolytes derive unstable...Micro-sized silicon(mSi)anodes offer high capacity for next-generation lithium-ion batteries but suffer from severe volume changes,causing unstable interphases and poor cycling.Traditional electrolytes derive unstable electrolyte/electrolyte interphases,and flammable solvents pose safety risks.Here,we introduce a non-flammable molten salt electrolyte,which consists of lithium bis(fluorosulfonyl)imide,potassium bis(fluorosulfonyl)amide,and cesium bis(fluorosulfonyl)imide in a mole ratio of 0.3:0.35:0.35(noted as Li_(0.3)K_(0.35)Cs_(0.35)FSA),that forms an inorganic interphase on mSi,stabilizing the electrode/electrolyte interface.Computational and experimental insights elucidate the FSA-anion decomposition-derived SEI predominantly of LiF,Li_(3)N,Li_(2)O,and Li_(2)S,which exhibits mechanical resilience and low interfacial resistance,effectively accommodating the significant volume expansion of silicon during lithiation/delithiation.As a result,the Li||mSi half-cell achieves 60.7%capacity retention after 100 cycles with 99.5%average Coulombic efficiency.Overall,the Li_(0.3)K_(0.35)Cs_(0.35)FSA electrolyte eliminates flammability concerns while enabling robust cycling performance.This work demonstrates a safe,high-energy battery system by coupling mSi anodes with stable molten salt electrolytes,addressing both interfacial instability and safety challenges in mSi-based lithium-ion batteries.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 reliable operation of lithium-ion batteries(LIBs)in low temperatures has long been hindered by severe side reactions on graphite anodes.To develop a commercially viable low-temperature electrolyte,we design a solv...The reliable operation of lithium-ion batteries(LIBs)in low temperatures has long been hindered by severe side reactions on graphite anodes.To develop a commercially viable low-temperature electrolyte,we design a solvent-resistant Nitrate-coordinated electrolyte.The practical Ah-level graphite LiNi_(0.5)Co_(0.2)Mn_(0.3)O_(2) pouch cell with the newly developed electrolyte demonstrates a significant breakthrough in cycling stability,exhibiting negligible capacity fade after 250 cycles at-30℃ and 0.1 C.NO_(3)^(-),as the functional additive,compresses the electric field around Li^(+)through electrostatic interactions,mimicking the Debye-screening effect and inducing the coordinative exclusion of free ethyl acetate molecules at low temperatures.The transformation from contact ion pairs(CIPs)formed by Pto solventseparated ion pairs is significantly restrained,which mitigates the continuous reactions between the electrolyte and inevitable lithium deposition at low temperature.Additionally,this customized inert CIPs form a solid electrolyte interphase on graphite that exhibits remarkable ionic conductivity and rigidity,preventing excessive Li dendrite growth.This finding offers new insights into the relationship of microstructure-performance for low-temperature electrolytes,demonstrating that relying solely on inert CIPs can also inhibit the decomposition of the interfacial electrolyte,and inspires a unique design concept for high-performance,commercially viable LIBs that operate reliably in sub-zero environments.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
Precisely estimating the state of health(SOH)of lithium-ion batteries is essential for battery management systems(BMS),as it plays a key role in ensuring the safe and reliable operation of battery systems.However,curr...Precisely estimating the state of health(SOH)of lithium-ion batteries is essential for battery management systems(BMS),as it plays a key role in ensuring the safe and reliable operation of battery systems.However,current SOH estimation methods often overlook the valuable temperature information that can effectively characterize battery aging during capacity degradation.Additionally,the Elman neural network,which is commonly employed for SOH estimation,exhibits several drawbacks,including slow training speed,a tendency to become trapped in local minima,and the initialization of weights and thresholds using pseudo-random numbers,leading to unstable model performance.To address these issues,this study addresses the challenge of precise and effective SOH detection by proposing a method for estimating the SOH of lithium-ion batteries based on differential thermal voltammetry(DTV)and an SSA-Elman neural network.Firstly,two health features(HFs)considering temperature factors and battery voltage are extracted fromthe differential thermal voltammetry curves and incremental capacity curves.Next,the Sparrow Search Algorithm(SSA)is employed to optimize the initial weights and thresholds of the Elman neural network,forming the SSA-Elman neural network model.To validate the performance,various neural networks,including the proposed SSA-Elman network,are tested using the Oxford battery aging dataset.The experimental results demonstrate that the method developed in this study achieves superior accuracy and robustness,with a mean absolute error(MAE)of less than 0.9%and a rootmean square error(RMSE)below 1.4%.展开更多
Energy storage is a key factor in the drive for carbon neutrality and carbon nanotubes(CNTs)may have an important role in this.Their intrinsic sp2 covalent structure gives them excellent electrical conductivity,mechan...Energy storage is a key factor in the drive for carbon neutrality and carbon nanotubes(CNTs)may have an important role in this.Their intrinsic sp2 covalent structure gives them excellent electrical conductivity,mechanical strength,and chemical stability,making them suitable for many uses in energy storage,such as lithium-ion batteries(LIBs).Currently,their use in LIBs mainly focuses on conductive networks,current collectors,and dry electrodes.The review outlines advances in the use of CNTs in the cathodes and anodes of LIBs,especially in the electrode fabrication and mechanical sensors,as well as providing insights into their future development.展开更多
Electrospinning technology has emerged as a promising method for fabricating flexible lithium-ion batter-ies(FLIBs)due to its ability to create materials with desir-able properties for energy storage applications.FLIB...Electrospinning technology has emerged as a promising method for fabricating flexible lithium-ion batter-ies(FLIBs)due to its ability to create materials with desir-able properties for energy storage applications.FLIBs,which are foldable and have high energy densities,are be-coming increasingly important as power sources for wear-able devices,flexible electronics,and mobile energy applica-tions.Carbon materials,especially carbon nanofibers,are pivotal in improving the performance of FLIBs by increas-ing electrical conductivity,chemical stability,and surface area,as well as reducing costs.These materials also play a significant role in establishing conducting networks and im-proving structural integrity,which are essential for extend-ing the cycle life and enhancing the safety of the batteries.This review considers the role of electrospinning in the fabrication of critical FLIB components,with a particular emphasis on the integration of carbon materials.It explores strategies to optimize FLIB performance by fine-tuning the electrospinning para-meters,such as electric field strength,spinning rate,solution concentration,and carbonization process.Precise control over fiber properties is crucial for enhancing battery reliability and stability during folding and bending.It also highlights the latest research findings in carbon-based electrode materials,high-performance electrolytes,and separator structures,discussing the practical challenges and opportunities these materials present.It underscores the significant impact of carbon materials on the evolution of FLIBs and their potential to shape future energy storage technologies.展开更多
Carbon nanotubes(CNTs)have many excellent properties that make them ideally suited for use in lithium-ion batteries(LIBs).In this review,the recent research on applications of CNTs in LIBs,including their usage as fre...Carbon nanotubes(CNTs)have many excellent properties that make them ideally suited for use in lithium-ion batteries(LIBs).In this review,the recent research on applications of CNTs in LIBs,including their usage as freestanding anodes,conductive additives,and current collectors,are discussed.Challenges,strategies,and progress are analyzed by selecting typical examples.Particularly,when CNTs are used with relatively large mass fractions,the relevant interfacial electrochemistry in such a CNT-based electrode,which dictates the quality of the resulting solid-electrolyte interface,becomes a concern.Hence,in this review the different lithium-ion adsorption and insertion mechanisms inside and outside of CNTs are compared;the influence of not only CNT structural features(including their length,defect density,diameter,and wall thickness)but also the electrolyte composition on the solid-electrolyte interfacial reactions is analyzed in detail.Strategies to optimize the solid-solid interface between CNTs and the other solid components in various composite electrodes are also covered.By emphasizing the importance of such a structure-performance relationship,the merits and weaknesses of various applications of CNTs in various advanced LIBs are clarified.展开更多
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 Key R&D Program of China(No.2022YFA1504100)the Anhui Provincial Major Science and Technology Project(No.202203a05020017)+4 种基金the National Natural Science Foundation of China(Nos.52222210,51925207,U1910210,52161145101,51972067,51902062,and 52002083)the“Transformational Technologies for Clean Energy and Demonstration”Strategic Priority Research Program of Chinese Academy of Sciences(No.XDA21000000)the National Synchrotron Radiation Laboratory(No.KY2060000173)the Joint Fund of the Yulin University and the Dalian National Laboratory for Clean Energy(No.YLU-DNL Fund 2021002)the Fundamental Research Funds for the Central Universities(No.WK2060140026)。
文摘Silicon possesses a high theoretical capacity,making it a potential contender for lithium-ion battery(LIB)anodes.Nonetheless,its practical usage is challenged by low electrical conductivity and significant volume expansion during cycling.Here,we synthesized a novel silicon/carbon(Si/C)anode doped with ZnO via a template-derived method and high-temperature carbonization.The carbon structure,originated from metal-organic frameworks(MOFs)and ZnO doping,substantially enhanced the electrochemical properties of the composite material.It exhibited an initial capacity of 2100.3 mA h g^(-1)at a current density of 0.2 A g^(-1)and demonstrated excellent capacity retention over successive cycles.Moreover,the composite material displayed superior rate performance at higher current densities of 2 A g^(-1)and 3 A g^(-1).To address the low initial Coulombic efficiency(ICE)of siliconbased materials,we adopted a direct contact prelithiation approach and optimized the lithiation process by controlling the prelithiation time.After 30 min of prelithiation,the ICE reached 97.9%,thereby reducing the initial irreversible capacity loss(ICL)and realizing stable discharge-charge in subsequent cycles.This rational design provides valuable insights for achieving high-performance silicon anode.
基金supported by the National Key R&D Program of China(No.2022YFB3803400)National Natural Science Foundation of China(Nos.52102054,52020105010,51927803,52188101 and 52072378)+1 种基金Liaoning Province Science and Technology Planning Project(No.2022-BS-007)Fujian Science and Technology Program(No.2023T3025).
文摘Commercial carbonate electrolytes suffer from ion transport difficulty in bulk electrolytes and interphase at low temperatures,bringing challenges to the application of lithium-ion batteries(LIBs)at low temperatures.Herein,the ester solvent of methyl propionate(MP)with low melting point and low viscosity was used to tackle ion transport difficulty in electrolytes.Fluorinated ester was further added to accelerate interfacial transport through intermolecular interactions.The influence of fluorinated esters with different fluorination degrees on the solvation structure of electrolytes and the performance of batteries was further studied.As a result,methyl pentafluoropropionate(M5F)with five fluorine atoms was selected for its optimal interactions with both Li+and MP solvent in the primary solvation structure,contributing to desired solvation structure for fast interfacial transport.The LiFePO_(4)(LFP)||graphite cell with LiFSI-MP-M5F electrolyte exhibited a high cyclability of 85.8%after 120 cycles and retained 81.2%of room-temperature capacity when charged and discharged at−30℃.1 Ah LFP||graphite pouch cell with high cathode loading(20 mg/cm^(2))in LiFSI-MP-M5F electrolyte exhibited 0.85 Ah capacity when charged and discharged at−20℃.This work provides a guidance for electrolyte design by synergistic fluorinated and non-fluorinated solvents for LIBs at low-temperature application.
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 Korea Electrotechnology Research Institute(KERI)Primary research program through the National Research Council of Science&Technology(NST)funded by the Ministry of Science and ICT(MSIT)(No.25A01015)by the Technology Innovation Program(20019091)funded by the Ministry of Trade,Industry&Energy(MOTIE,Korea)by the National Research Council of Science&Technology(NST)grant from the Korea government(MSIT)(No.GTL24012-000).
文摘Carbon coatings for silicon(Si)-based anode materials are essential for designing high-performance Li-ion batteries(LIBs).The coatings prevent direct contact with the electrolyte and enhance anode performance.However,conventional carbon coatings are limited by their volume expansion and structural degradation,which lead to capacity fading and reduced durability.This study introduces a scalable and practical one-step carbon-coating strategy for directly coating silicon suboxide(SiO_(x))-based materials using aqueous quasi-defect-free reduced graphene oxide(QrGO)without post-treatment,unlike conventional graphene oxide(GO)-based coating methods.This simple process enables uniform encapsulation with QrGO for a highly adhesive and conductive coating.The QrGO-based composite anode material has several advantages,including reduced cracking due to volume expansion and enhanced charge carrier transport,as well as an increased Si content of 20 wt.%compared to the 5 wt.%in typical commercial Si-based active materials.In particular,the capacity retention of the QrGO-coated Si electrodes dramatically increases at high C-rate.The full cell exhibited long-term stability and capacity that were twice that of commercial SiO_(x)-based cells.Therefore,the QrGO-based one-step coating process represents a scalable,transformative,and commercially viable strategy for developing high-performance LIBs.
基金supported in part by the National Natural Science Foundation of China(No.62133007)Shandong Provincial Key Research and Development Program(No.2024CXPT052).
文摘Battery energy storage systems bolster power grids’absorption capacity,however,battery safety issues remain a formidable challenge.Timely and pre-cise fault diagnosis,coupled with early-stage fault warn-ings,is crucial.This study introduces an eigen decompo-sition-based multi-fault diagnosis approach for lithi-umion battery packs,enabling online diagnosis of short circuits,electrical connection faults,and voltage sensor malfunctions.By incorporating an interleaved measurement topology,precise fault type differentiation is achieved.Eigenvector matching analysis is employed to increase sensitivity to fault characteristics and enhance robustness.The interleaved topology can be seamlessly integrated using common voltage measurement solutions,eliminating the need for additional design complexities,while sensor number redundancy enhances fault tolerance of battery management systems(BMS).A cloud-side collaboration method is proposed,where the BMS functions as an edge device for specific data computations,while the parameters are fine-tuned by the server through big data analytics.This approach circumvents cumbersome server calculations,thereby curbing server cost escalation.The edge computing process is divided into two steps,with partial calculations often sufficient to evaluate battery safety,thus reducing the computational load on edge devices.Several battery tests are conducted,and the results confirm the method’s capability,feasibility,and validity in early-stage fault diagnosis.
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supported by the National Natural Science Foundation of China(52477222)the Key Research and Development Program of Shaanxi Province(2024GX-YBXM-442)the Xinjiang Uygur Autonomous Region Key R&D Program under Grant(2022B01019-2)。
文摘With the rapid development of electric vehicles and grid-scale renewable integration,the demand for lithium-ion batteries(LIBs)has significantly increased with high expectations on enhanced energy density,cycle stability,and failure resilience.Electrochemical models(EMs),serving as pivotal mechanismdriven analytical frameworks in battery research and applications,demonstrate unprecedented quantitative fidelity in characterizing intricate multi-physics dynamics for the next-generation battery management systems(BMS).The breakthrough innovations in artificial intelligence(AI)driven methods have revolutionized the dynamic modeling of LIBs.However,the deployment of AI-augmented EMs in BMS faces significant identifiability challenges due to strong parameter coupling.In addition,research on model simplification,parameter determination,and dynamic parameter identification remains largely fragmented.There is a lack of a comprehensive review to pave the way for the cross-domain innovations in BMS.To fill this gap,this paper presents a systematic review of the EMs for LIBs and examines the advancements in parameter determination techniques from both experimental measurement and numerical simulation perspectives.Besides,a comprehensive assessment of the progress in parameter identification from the standpoint of dynamic recognition is presented,encompassing both modelbased approaches and intelligent methods.Additionally,from the BMS standpoint,the strengths and limitations of existing approaches are evaluated.Finally,a coordinated framework for multi-stage identification needs to be established in the future.The potential of digital twins(DT),deep reinforcement learning(DRL),and large language models(LLMs)in enhancing EMs also warrants further exploration.The purpose of this work is to provide insights and guidance for the future development of EMs in LIB applications.
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funded in part by the Doctoral Scientific Research Foundation of Beijing University of Civil Engineering and Architecture under Grant ZF15054in part by the Pyramid Talent Training Project of Beijing University of Civil Engineering and Architecture under Grant GJZJ20220802in part by the BUCEA Post Graduate Innovation Project under Grant PG2024095.
文摘Lithium-ion(Li-ion)batteries stand as the dominant energy storage solution,despite their widespread adoption,precisely determining the state of charge(SOC)continues to pose significant difficulties,with direct implications for battery safety,operational reliability,and overall performance.Current SOC estimation techniques often demonstrate limited accuracy,particularly when confronted with complex operational scenarios and wide temperature variations,where their generalization capacity and dynamic adaptation prove insufficient.To address these shortcomings,this work presents a PSO-TCN-Transformer network model for SOC estimation.This research uses the Particle Swarm Optimization(PSO)method to automatically configure the architectural parameters of the Temporal Convolutional Network(TCN)and Transformer components.This automated optimization enhances the model’s ability to represent the dynamically evolving nature of SOC.Additionally,this integrated framework significantly increases the model’s capacity to capture SOC dynamics in complex operational scenarios.During training and evaluation using a comprehensive dataset that covers complex operating conditions and a broad temperature spanning from−20℃ to 40℃,the proposed model achieves a root mean square error(RMSE)of less than 0.6%,a maximum absolute error(MAXE)below 4.0%,and a coefficient of determination(R^(2))of 99.99%.Additional comparative experiments on data from an energy storage company further verify the model’s superior performance,with an RMSE of 1.18%and an MAXE of 1.95%.The implications of this work extend to the development of optimization strategies and hybrid architectures,providing insights that can be adapted for state estimation across a range of complex dynamic systems.
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.
基金supported by the Science and Technology Plan of Fujian Provincial,China(2022G02020 and 2022H6002)the Collaborative Innovation Platform Project for Advanced Electrochemical Energy Storage Technology,Fuxiaquan National Independent Innovation Demonstration Zone,China(3502ZCQXT2022001)+1 种基金the Significant Science and Technology Project of Xiamen(the Future Industrial Area),China(3502Z20231058)the Scientific Research Startup Funding for Special Professor of Minjiang Scholars。
文摘Silicon(Si)-based anodes have emerged as promising candidates for the next-generation lithium-ion batteries(LIBs)due to their high theoretical capacity(4200 mAh g^(-1)).However,their further application is hindered by critical challenges,including severe volume expansion(~300%),formation of unstable solid electrolyte interphase(SEI),and inherently low conductivity.While extensive research has sought to alleviate the substantial internal stress caused by volume expansion through the rational design of Si-based anode structures,the underlying mechanisms that govern these improvements remain insufficiently understood,leaving significant gaps in mechanical and interface electrical failure.To build a comprehensive understanding relationship between structural design and performance enhancement of Si-based anodes,this review first analyzes the characteristics of various Sibased anode structures and their associated internal stresses.Subsequently,it summarizes effective strategies to optimize the performance of Si-based anodes,including doping design,novel electrolyte design,and fu nctional binder design.Additionally,we assess emerging technologies with high commercial potential for structural design and interfacial modification,such as porous carbon carriers,chemical vapor deposition(CVD),spray granulation,and pre-lithiation.Finally,this work provides perspectives on the structural design of Si-based anodes.Overall,this review systematically summarizes modification strategies for Si-based anodes through structural regulation and interface engineering,thereby providing a foundation for advanced structural and interfacial design.
基金supported by the Strategic Priority Research Program of the Chinese Academy of Sciences(Grant No.XDA0400000)the One Hundred Person Project of the Chinese Academy of Sciences,the Shanghai Magnolia Talent Plan Pujiang Project(Grant No.23PJ1415600)the Shanghai International S&T Cooperation Program(Grant No.23160711700).
文摘Micro-sized silicon(mSi)anodes offer high capacity for next-generation lithium-ion batteries but suffer from severe volume changes,causing unstable interphases and poor cycling.Traditional electrolytes derive unstable electrolyte/electrolyte interphases,and flammable solvents pose safety risks.Here,we introduce a non-flammable molten salt electrolyte,which consists of lithium bis(fluorosulfonyl)imide,potassium bis(fluorosulfonyl)amide,and cesium bis(fluorosulfonyl)imide in a mole ratio of 0.3:0.35:0.35(noted as Li_(0.3)K_(0.35)Cs_(0.35)FSA),that forms an inorganic interphase on mSi,stabilizing the electrode/electrolyte interface.Computational and experimental insights elucidate the FSA-anion decomposition-derived SEI predominantly of LiF,Li_(3)N,Li_(2)O,and Li_(2)S,which exhibits mechanical resilience and low interfacial resistance,effectively accommodating the significant volume expansion of silicon during lithiation/delithiation.As a result,the Li||mSi half-cell achieves 60.7%capacity retention after 100 cycles with 99.5%average Coulombic efficiency.Overall,the Li_(0.3)K_(0.35)Cs_(0.35)FSA electrolyte eliminates flammability concerns while enabling robust cycling performance.This work demonstrates a safe,high-energy battery system by coupling mSi anodes with stable molten salt electrolytes,addressing both interfacial instability and safety challenges in mSi-based lithium-ion batteries.
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.
基金support from the Heilongjiang Touyan Innovation Team Program(HITTY-20190033)National Natural Science Foundation of China(22278096)Innovation Special Project on Science and Technology for Carbon Peaking and Carbon Neutrality in Jiangsu Province(WSSJH20230015)。
文摘The reliable operation of lithium-ion batteries(LIBs)in low temperatures has long been hindered by severe side reactions on graphite anodes.To develop a commercially viable low-temperature electrolyte,we design a solvent-resistant Nitrate-coordinated electrolyte.The practical Ah-level graphite LiNi_(0.5)Co_(0.2)Mn_(0.3)O_(2) pouch cell with the newly developed electrolyte demonstrates a significant breakthrough in cycling stability,exhibiting negligible capacity fade after 250 cycles at-30℃ and 0.1 C.NO_(3)^(-),as the functional additive,compresses the electric field around Li^(+)through electrostatic interactions,mimicking the Debye-screening effect and inducing the coordinative exclusion of free ethyl acetate molecules at low temperatures.The transformation from contact ion pairs(CIPs)formed by Pto solventseparated ion pairs is significantly restrained,which mitigates the continuous reactions between the electrolyte and inevitable lithium deposition at low temperature.Additionally,this customized inert CIPs form a solid electrolyte interphase on graphite that exhibits remarkable ionic conductivity and rigidity,preventing excessive Li dendrite growth.This finding offers new insights into the relationship of microstructure-performance for low-temperature electrolytes,demonstrating that relying solely on inert CIPs can also inhibit the decomposition of the interfacial electrolyte,and inspires a unique design concept for high-performance,commercially viable LIBs that operate reliably in sub-zero environments.
文摘Lithium-ion batteries(LIBs)are an electrochemical energy storage technology that has been widely used for portable electrical devices,electric vehicles,and grid storage,etc.To satisfy the demand for user convenience especially for electric vehicles,the development of a fast-charging technology for LIBs has become a critical focus.In commercial LIBs,the slow kinetics of Li+intercalation into the graphite anode from the electrolyte solution is known as the main restriction for fast-charging.We summarize the recent advances in obtaining fast-charging graphite-based anodes,mainly involving modifications of the electrolyte solution and graphite anode.Specifically,strategies for increasing the ionic conductivity and regulating the Li+solvation/desolvation state in the electrolyte solution,as well as optimizing the fabrication and the intrinsic activity of graphite-based anodes are discussed in detail.This review considers practical ways to obtain fast Li+intercalation kinetics into a graphite anode from the electrolyte as well as analysing progress in the commercialization of fast-charging LIBs.
基金supported by the National Natural Science Foundation of China(NSFC)under Grant(No.51677058).
文摘Precisely estimating the state of health(SOH)of lithium-ion batteries is essential for battery management systems(BMS),as it plays a key role in ensuring the safe and reliable operation of battery systems.However,current SOH estimation methods often overlook the valuable temperature information that can effectively characterize battery aging during capacity degradation.Additionally,the Elman neural network,which is commonly employed for SOH estimation,exhibits several drawbacks,including slow training speed,a tendency to become trapped in local minima,and the initialization of weights and thresholds using pseudo-random numbers,leading to unstable model performance.To address these issues,this study addresses the challenge of precise and effective SOH detection by proposing a method for estimating the SOH of lithium-ion batteries based on differential thermal voltammetry(DTV)and an SSA-Elman neural network.Firstly,two health features(HFs)considering temperature factors and battery voltage are extracted fromthe differential thermal voltammetry curves and incremental capacity curves.Next,the Sparrow Search Algorithm(SSA)is employed to optimize the initial weights and thresholds of the Elman neural network,forming the SSA-Elman neural network model.To validate the performance,various neural networks,including the proposed SSA-Elman network,are tested using the Oxford battery aging dataset.The experimental results demonstrate that the method developed in this study achieves superior accuracy and robustness,with a mean absolute error(MAE)of less than 0.9%and a rootmean square error(RMSE)below 1.4%.
文摘Energy storage is a key factor in the drive for carbon neutrality and carbon nanotubes(CNTs)may have an important role in this.Their intrinsic sp2 covalent structure gives them excellent electrical conductivity,mechanical strength,and chemical stability,making them suitable for many uses in energy storage,such as lithium-ion batteries(LIBs).Currently,their use in LIBs mainly focuses on conductive networks,current collectors,and dry electrodes.The review outlines advances in the use of CNTs in the cathodes and anodes of LIBs,especially in the electrode fabrication and mechanical sensors,as well as providing insights into their future development.
文摘Electrospinning technology has emerged as a promising method for fabricating flexible lithium-ion batter-ies(FLIBs)due to its ability to create materials with desir-able properties for energy storage applications.FLIBs,which are foldable and have high energy densities,are be-coming increasingly important as power sources for wear-able devices,flexible electronics,and mobile energy applica-tions.Carbon materials,especially carbon nanofibers,are pivotal in improving the performance of FLIBs by increas-ing electrical conductivity,chemical stability,and surface area,as well as reducing costs.These materials also play a significant role in establishing conducting networks and im-proving structural integrity,which are essential for extend-ing the cycle life and enhancing the safety of the batteries.This review considers the role of electrospinning in the fabrication of critical FLIB components,with a particular emphasis on the integration of carbon materials.It explores strategies to optimize FLIB performance by fine-tuning the electrospinning para-meters,such as electric field strength,spinning rate,solution concentration,and carbonization process.Precise control over fiber properties is crucial for enhancing battery reliability and stability during folding and bending.It also highlights the latest research findings in carbon-based electrode materials,high-performance electrolytes,and separator structures,discussing the practical challenges and opportunities these materials present.It underscores the significant impact of carbon materials on the evolution of FLIBs and their potential to shape future energy storage technologies.
基金Xiamen Science and Technology Project,Grant/Award Number:3502Z20231057National Key Research and Development Program of China,Grant/Award Number:3502Z20231057National Natural Science Foundation of China,Grant/Award Numbers:22279107,22288102。
文摘Carbon nanotubes(CNTs)have many excellent properties that make them ideally suited for use in lithium-ion batteries(LIBs).In this review,the recent research on applications of CNTs in LIBs,including their usage as freestanding anodes,conductive additives,and current collectors,are discussed.Challenges,strategies,and progress are analyzed by selecting typical examples.Particularly,when CNTs are used with relatively large mass fractions,the relevant interfacial electrochemistry in such a CNT-based electrode,which dictates the quality of the resulting solid-electrolyte interface,becomes a concern.Hence,in this review the different lithium-ion adsorption and insertion mechanisms inside and outside of CNTs are compared;the influence of not only CNT structural features(including their length,defect density,diameter,and wall thickness)but also the electrolyte composition on the solid-electrolyte interfacial reactions is analyzed in detail.Strategies to optimize the solid-solid interface between CNTs and the other solid components in various composite electrodes are also covered.By emphasizing the importance of such a structure-performance relationship,the merits and weaknesses of various applications of CNTs in various advanced LIBs are clarified.
