MOF-derived porous graphitic carbon with optimized plateau capacity and rate capability for high performance lithium-ion capacitors

The development of anode materials with high rate capability and long charge-discharge plateau is the key to improve per-formance of lithium-ion capacitors(LICs).Herein,the porous graphitic carbon(PGC-1300)derived from a new triply interpenetrated co-balt metal-organic framework(Co-MOF)was prepared through the facile and robust carbonization at 1300°C and washing by HCl solu-tion.The as-prepared PGC-1300 featured an optimized graphitization degree and porous framework,which not only contributes to high plateau capacity(105.0 mAh·g^(−1)below 0.2 V at 0.05 A·g^(−1)),but also supplies more convenient pathways for ions and increases the rate capability(128.5 mAh·g^(−1)at 3.2 A·g^(−1)).According to the kinetics analyses,it can be found that diffusion regulated surface induced capa-citive process and Li-ions intercalation process are coexisted for lithium-ion storage.Additionally,LIC PGC-1300//AC constructed with pre-lithiated PGC-1300 anode and activated carbon(AC)cathode exhibited an increased energy density of 102.8 Wh·kg^(−1),a power dens-ity of 6017.1 W·kg^(−1),together with the excellent cyclic stability(91.6%retention after 10000 cycles at 1.0 A·g^(−1)).

Investigation into Impedance Measurements for Rapid Capacity Estimation of Lithium-ion Batteries in Electric Vehicles

With the dramatic increase in electric vehicles(EVs)globally,the demand for lithium-ion batteries has grown dramatically,resulting in many batteries being retired in the future.Developing a rapid and robust capacity estimation method is a challenging work to recognize the battery aging level on service and provide regroup strategy of the retied batteries in secondary use.There are still limitations on the current rapid battery capacity estimation methods,such as direct current internal resistance(DCIR)and electrochemical impedance spectroscopy(EIS),in terms of efficiency and robustness.To address the challenges,this paper proposes an improved version of DCIR,named pulse impedance technique(PIT),for rapid battery capacity estimation with more robustness.First,PIT is carried out based on the transient current excitation and dynamic voltage measurement using the high sampling frequency,in which the coherence analysis is used to guide the selection of a reliable frequency band.The battery impedance can be extracted in a wide range of frequency bands compared to the traditional DCIR method,which obtains more information on the battery capacity evaluation.Second,various statistical variables are used to extract aging features,and Pearson correlation analysis is applied to determine the highly correlated features.Then a linear regression model is developed to map the relationship between extracted features and battery capacity.To validate the performance of the proposed method,the experimental system is designed to conduct comparative studies between PIT and EIS based on the two 18650 batteries connected in series.The results reveal that the proposed PIT can provide comparative indicators to EIS,which contributes higher estimation accuracy of the proposed PIT method than EIS technology with lower time and cost.

A comparative study of data-driven battery capacity estimation based on partial charging curves

With its generality and practicality, the combination of partial charging curves and machine learning(ML) for battery capacity estimation has attracted widespread attention. However, a clear classification,fair comparison, and performance rationalization of these methods are lacking, due to the scattered existing studies. To address these issues, we develop 20 capacity estimation methods from three perspectives:charging sequence construction, input forms, and ML models. 22,582 charging curves are generated from 44 cells with different battery chemistry and operating conditions to validate the performance. Through comprehensive and unbiased comparison, the long short-term memory(LSTM) based neural network exhibits the best accuracy and robustness. Across all 6503 tested samples, the mean absolute percentage error(MAPE) for capacity estimation using LSTM is 0.61%, with a maximum error of only 3.94%. Even with the addition of 3 m V voltage noise or the extension of sampling intervals to 60 s, the average MAPE remains below 2%. Furthermore, the charging sequences are provided with physical explanations related to battery degradation to enhance confidence in their application. Recommendations for using other competitive methods are also presented. This work provides valuable insights and guidance for estimating battery capacity based on partial charging curves.

Understanding the mechanism of capacity increase during early cycling of commercial NMC/graphite lithium-ion batteries

A capacity increase is often observed in the early stage of Li-ion battery cycling.This study explores the phenomena involved in the capacity increase from the full cell,electrodes,and materials perspective through a combination of non-destructive diagnostic methods in a full cell and post-mortem analysis in a coin cell.The results show an increase of 1%initial capacity for the battery aged at 100%depth of discharge(DOD)and 45℃.Furthermore,large DODs or high temperatures accelerate the capacity increase.From the incremental capacity and differential voltage(IC-DV)analysis,we concluded that the increased capacity in a full cell originates from the graphite anode.Furthermore,graphite/Li coin cells show an increased capacity for larger DODs and a decreased capacity for lower DODs,thus in agreement with the full cell results.Post-mortem analysis results show that a larger DOD enlarges the graphite dspace and separates the graphite layer structure,facilitating the Li+diffusion,hence increasing the battery capacity.

A Silicon Monoxide Lithium-Ion Battery Anode with Ultrahigh Areal Capacity

Silicon monoxide(SiO)is an attractive anode material for next-generation lithium-ion batteries for its ultra-high theoretical capacity of 2680 mAh g−1.The studies to date have been limited to electrodes with a rela-tively low mass loading(<3.5 mg cm^(−2)),which has seriously restricted the areal capacity and its potential in practical devices.Maximizing areal capacity with such high-capacity materials is critical for capitalizing their potential in practi-cal technologies.Herein,we report a monolithic three-dimensional(3D)large-sheet holey gra-phene framework/SiO(LHGF/SiO)composite for high-mass-loading electrode.By specifically using large-sheet holey graphene building blocks,we construct LHGF with super-elasticity and exceptional mechanical robustness,which is essential for accommodating the large volume change of SiO and ensuring the structure integrity even at ultrahigh mass loading.Additionally,the 3D porous graphene network structure in LHGF ensures excellent electron and ion transport.By systematically tailoring microstructure design,we show the LHGF/SiO anode with a mass loading of 44 mg cm^(−2)delivers a high areal capacity of 35.4 mAh cm^(−2)at a current of 8.8 mA cm^(−2)and retains a capacity of 10.6 mAh cm^(−2)at 17.6 mA cm^(−2),greatly exceeding those of the state-of-the-art commercial or research devices.Furthermore,we show an LHGF/SiO anode with an ultra-high mass loading of 94 mg cm^(−2)delivers an unprecedented areal capacity up to 140.8 mAh cm^(−2).The achievement of such high areal capacities marks a critical step toward realizing the full potential of high-capacity alloy-type electrode materials in practical lithium-ion batteries.

Battery pack capacity estimation for electric vehicles based on enhanced machine learning and field data

Accurate capacity estimation is of great importance for the reliable state monitoring,timely maintenance,and second-life utilization of lithium-ion batteries.Despite numerous works on battery capacity estimation using laboratory datasets,most of them are applied to battery cells and lack satisfactory fidelity when extended to real-world electric vehicle(EV)battery packs.The challenges intensify for large-sized EV battery packs,where unpredictable operating profiles and low-quality data acquisition hinder precise capacity estimation.To fill the gap,this study introduces a novel data-driven battery pack capacity estimation method grounded in field data.The proposed approach begins by determining labeled capacity through an innovative combination of the inverse ampere-hour integral,open circuit voltage-based,and resistance-based correction methods.Then,multiple health features are extracted from incremental capacity curves,voltage curves,equivalent circuit model parameters,and operating temperature to thoroughly characterize battery aging behavior.A feature selection procedure is performed to determine the optimal feature set based on the Pearson correlation coefficient.Moreover,a convolutional neural network and bidirectional gated recurrent unit,enhanced by an attention mechanism,are employed to estimate the battery pack capacity in real-world EV applications.Finally,the proposed method is validated with a field dataset from two EVs,covering approximately 35,000 kilometers.The results demonstrate that the proposed method exhibits better estimation performance with an error of less than 1.1%compared to existing methods.This work shows great potential for accurate large-sized EV battery pack capacity estimation based on field data,which provides significant insights into reliable labeled capacity calculation,effective features extraction,and machine learning-enabled health diagnosis.

A hierarchical enhanced data-driven battery pack capacity estimation framework for real-world operating conditions with fewer labeled data

Battery pack capacity estimation under real-world operating conditions is important for battery performance optimization and health management,contributing to the reliability and longevity of batterypowered systems.However,complex operating conditions,coupling cell-to-cell inconsistency,and limited labeled data pose great challenges to accurate and robust battery pack capacity estimation.To address these issues,this paper proposes a hierarchical data-driven framework aimed at enhancing the training of machine learning models with fewer labeled data.Unlike traditional data-driven methods that lack interpretability,the hierarchical data-driven framework unveils the“mechanism”of the black box inside the data-driven framework by splitting the final estimation target into cell-level and pack-level intermediate targets.A generalized feature matrix is devised without requiring all cell voltages,significantly reducing the computational cost and memory resources.The generated intermediate target labels and the corresponding features are hierarchically employed to enhance the training of two machine learning models,effectively alleviating the difficulty of learning the relationship from all features due to fewer labeled data and addressing the dilemma of requiring extensive labeled data for accurate estimation.Using only 10%of degradation data,the proposed framework outperforms the state-of-the-art battery pack capacity estimation methods,achieving mean absolute percentage errors of 0.608%,0.601%,and 1.128%for three battery packs whose degradation load profiles represent real-world operating conditions.Its high accuracy,adaptability,and robustness indicate the potential in different application scenarios,which is promising for reducing laborious and expensive aging experiments at the pack level and facilitating the development of battery technology.

Analysis on the capacity degradation mechanism of a series lithium-ion power battery pack based on inconsistency of capacity

The lithium-ion battery has been widely used as an energy source. Charge rate, discharge rate, and operating tem- perature are very important factors for the capacity degradations of power batteries and battery packs. Firstly, in this paper we make use of an accelerated life test and a statistical analysis method to establish the capacity accelerated degradation model under three constant stress parameters according to the degradation data, which are charge rate, discharge rate, and operating temperature, and then we propose a capacity degradation model according to the current residual capacity of a Li-ion cell under dynamic stress parameters. Secondly, we analyze the charge and discharge process of a series power battery pack and interpret the correlation between the capacity degradations of the battery pack and its charge/discharge rate. According to this cycling condition, we establish a capacity degradation model of a series power battery pack under inconsistent capacity of cells, and analyze the degradation mechanism with capacity variance and operating temperature difference. The comparative analysis of test results shows that the inconsistent operating temperatures of cells in the series power battery pack are the main cause of its degradation; when the difference between inconsistent temperatures is narrowed by 5 ℃, the cycle life can be improved by more than 50%. Therefore, it effectively improves the cycle life of the series battery pack to reasonably assemble the batteries according to their capacities and to narrow the differences in operating temperature among cells.

A thin Si nanowire network anode for high volumetric capacity and long-life lithium-ion batteries

Silicon nanowires(Si NWs)have been widely researched as the best alternative to graphite anodes for the next-generation of high-performance lithium-ion batteries(LIBs)owing to their high capacity and low discharge potential.However,growing binder-free Si NW anodes with adequate mass loading and stable capacity is severely limited by the low surface area of planar current collectors(CCs),and is particularly challenging to achieve on standard pure-Cu substrates due to the ubiquitous formation of Li+inactive silicide phases.Here,the growth of densely-interwoven In-seeded Si NWs is facilitated by a thin-film of copper-silicide(CS)network in situ grown on a Cu-foil,allowing for a thin active NW layer(<10μm thick)and high areal loading(≈1.04 mg/cm^(2))binder-free electrode architecture.The electrode exhibits an average Coulombic efficiency(CE)of>99.6%and stable performance for>900 cycles with≈88.7%capacity retention.More significantly,it delivers a volumetric capacity of≈1086.1 m A h/cm^(3)at 5C.The full-cell versus lithium manganese oxide(LMO)cathode delivers a capacity of≈1177.1 m A h/g at 1C with a stable rate capability.This electrode architecture represents significant advances toward the development of binder-free Si NW electrodes for LIB application.

Fast Remaining Capacity Estimation for Lithium-ion Batteries Based on Short-time Pulse Test and Gaussian Process Regression

It remains challenging to effectively estimate the remaining capacity of the secondary lithium-ion batteries that have been widely adopted for consumer electronics,energy storage,and electric vehicles.Herein,by integrating regular real-time current short pulse tests with data-driven Gaussian process regression algorithm,an efficient battery estimation has been successfully developed and validated for batteries with capacity ranging from 100%of the state of health(SOH)to below 50%,reaching an average accuracy as high as 95%.Interestingly,the proposed pulse test strategy for battery capacity measurement could reduce test time by more than 80%compared with regular long charge/discharge tests.The short-term features of the current pulse test were selected for an optimal training process.Data at different voltage stages and state of charge(SOC)are collected and explored to find the most suitable estimation model.In particular,we explore the validity of five different machine-learning methods for estimating capacity driven by pulse features,whereas Gaussian process regression with Matern kernel performs the best,providing guidance for future exploration.The new strategy of combining short pulse tests with machine-learning algorithms could further open window for efficiently forecasting lithium-ion battery remaining capacity.

Capacity Contribution Mechanism of rGO for SnO_(2)/rGO Composite as Anode of Lithium-ion Batteries

Compared with ordinary graphite anode,SnO_(2) possesses higher theoretical specifc capacity,rich raw materials and low price.While the severe volume expansion of SnO_(2) during lithium-ion extraction/intercalation limits its further application.To solve this problem,in this work the reduced graphene oxide(rGO)was introduced as volume bufer matrix of SnO_(2).Herein,SnO_(2)/rGO composite is obtained through one-step hydrothermal method.Three-dimensional structure of rGO could efectively hinder the polymerization of SnO_(2) nanoparticles and provide more lithium storage sites attributed to high specifc surface area and density defects.The initial discharge capacity of the composite cathode is 959 mA·h·g^(-1) and the capacity remained at 300 mA·h·g^(-1) after 1000 cycles at 1 C.It proved that the rGO added in the anode has a capacity contribution to the lithium-ion battery.It changes the capacity contribution mechanism from difusion process dominance to surface driven capacitive contribution.Due to the addition of rGO,the anode material gains stable structure and great conductivity.

Analysis of Capacity Decay, Impedance, and Heat Generation of Lithium-ionBatteries Experiencing Multiple Simultaneous Abuse Conditions

Abuse of Lithium-ion batteries,both physical and electrochemical,can lead to significantly reduced operational capabilities.In some instances,abuse can cause catastrophic failure,including thermal runaway,combustion,and explosion.Many different test standards that include abuse conditions have been developed,but these generally consider only one condition at a time and only provide go/no-go criteria.In this work,different types of cell abuse are implemented concurrently to determine the extent to which simultaneous abuse conditions aggravate cell degradation and failure.Vibrational loading is chosen to be the consistent type of physical abuse,and the first group of cells is cycled at different vibrational frequencies.The next group of cells is cycled at the same frequencies,with multiple charge pulses occurring during each discharge.The final group of cells is cycled at the same frequencies,with a partial nail puncture occurring near the beginning of cycling.The results show that abusing cells with vibrational loading or vibrational loading with current pulses does not cause a significant decrease in operational capabilities while abusing cells with vibrational loading and a nail puncture drastically reduces operational capabilities.The cells with vibration only experience an increase in internal resistance by a factor of 1.09–1.26,the cells with vibration and current pulses experience an increase in internal resistance by a factor of 1.16–1.23,and all cells from each group reach their rated lifetime of 500 cycles without reaching their end-of-life capacity.However,the cells with vibration and nail puncture experience an increase in internal resistance by a factor of 6.83–22.1,and each cell reaches its end-of-life capacity within 50 cycles.Overall,the results show that testing multiple abuse conditions simultaneously provides a better representation of the extreme limitations of cell operation and should be considered for inclusion in reference test standards.

Semi-supervised estimation of capacity degradation for lithium ion batteries with electrochemical impedance spectroscopy

Machine learning-based methods have emerged as a promising solution to accurate battery capacity estimation for battery management systems.However,they are generally developed in a supervised manner which requires a considerable number of input features and corresponding capacities,leading to prohibitive costs and efforts for data collection.In response to this issue,this study proposes a convolutional neural network(CNN)based method to perform end-to-end capacity estimation by taking only raw impedance spectra as input.More importantly,an input reconstruction module is devised to effectively exploit impedance spectra without corresponding capacities in the training process,thereby significantly alleviating the cost of collecting training data.Two large battery degradation datasets encompassing over 4700 impedance spectra are developed to validate the proposed method.The results show that accurate capacity estimation can be achieved when substantial training samples with measured capacities are given.However,the estimation performance of supervised machine learning algorithms sharply deteriorates when fewer samples with measured capacities are available.In this case,the proposed method outperforms supervised benchmarks and can reduce the root mean square error by up to 50.66%.A further validation under different current rates and states of charge confirms the effectiveness of the proposed method.Our method provides a flexible approach to take advantage of unlabelled samples for developing data-driven models and is promising to be generalised to other battery management tasks.

Understanding the phenomenon of capacity increasing along cycles: In the case of an ultralong-life and high-rate SnSe-Mo-C anode for lithium storage

A good cycling stability is a prerequisite for the application of metal-based materials in lithium-ion batteries(LIBs). However, an abnormal increase in capacity is often observed, which has rarely been focused on in many studies. In our SnSe-Mo-C composite anode, a high reversible capacity of 737.4 mAh g^(-1)remained after 5000 cycles at 5 A g^(-1)between 0.01 and 3.0 V versus Li/Li+. However, a continuous capacity increase occurred in the initial cycles, with 1086.9 mAh g^(-1)after 1000 cycles and 1216.9 mAh g^(-1)after 1500 cycles, respectively. Further studies revealed that the electrolyte decomposed at high potentials(2.5–3.0 V) and provided additional capacities. The cut-off voltage and electrolyte filling were controlled, which eliminated the impact of electrolyte decomposition, prevented rapid capacity decay, and provided a stable cycling performance for SnSe-Mo-C anodes in LIBs. This work shows that the composite anode is promising for lithium storage and the findings provide new insights into understanding and controlling the phenomenon of capacity increase with cycling in metal-based anode materials.

Flexible path planning-based reconfiguration strategy for maximum capacity utilization of battery pack

Maximizing the utilization of lithium-ion battery capacity is an important means to alleviate the range anxiety of electric vehicles.Battery pack inconsistency is the main limiting factor for improving battery pack capacity utilization,and poses major safety hazards to energy storage systems.To solve this problem,a maximum capacity utilization scheme based on a path planning algorithm is proposed.Specifically,the reconfigurable topology proposed is highly flexible and fault-tolerant,enabling battery pack consistency through alternating cell discharge and reducing the increased risk of short circuits due to relay error.The Dijkstra algorithm is used to find the optimal energy path,which can effectively remove faulty cells and find the current path with the best consistency of the battery pack and the lowest relay loss.Finally,the effectiveness of the scheme is verified by hardware-in-the-loop experiments,and the experimental results show that the state-of-charge SOC consistency of the battery pack at the end of discharge is increased by 34.18%,the relay energy loss is reduced by 0.16%,and the fault unit is effectively isolated.

Amorphous phosphorus chalcogenide as an anode material for lithiumion batteries with high capacity and long cycle life

The ever-increasing demands for modern energy storage applications drive the search for novel anode materials of lithium(Li)-ion batteries(LIBs) with high storage capacity and long cycle life, to outperform the conventional LIBs anode materials. Hence, we report amorphous ternary phosphorus chalcogenide(aP_(4)SSe_(2)) as an anode material with high performance for LIBs. Synthesized via the mechanochemistry method, the a-P_(4)SSe_(2) compound is endowed with amorphous feature and offers excellent cycling stability(over 1500 mA h g^(-1) capacity after 425 cycles at 0.3 A g^(-1)), owing to the advantages of isotropic nature and synergistic effect of multielement forming Li-ion conductors during battery operation. Furthermore,as confirmed by ex situ X-ray diffraction(XRD) and transmission electron microscope(TEM), the a-P_(4)SSe_(2)anode material has a reversible and multistage Li-storage mechanism, which is extremely beneficial to long cycle life for batteries. Moreover, the autogenous intermediate electrochemical products with fast ionic conductivity can facilitate Li-ion diffusion effectively. Thus, the a-P_(4)SSe_(2)electrode delivers excellent rate capability(730 mA h g^(-1)capacity at 3 A g^(-1)). Through in situ electrochemical impedance spectra(EIS) measurements, it can be revealed that the resistances of charge transfer(R_(SEI)) and solid electrolyte interphase(R_(Ct)) decrease along with the formation of Li-ion conductors whilst the ohmic resistance(R_(Ω)) remains unchanged during the whole electrochemical process, thus resulting in rapid reaction kinetics and stable electrode to obtain excellent rate performance and cycling ability for LIBs. Moreover, the formation mechanism and electrochemical superiority of the a-P_(4)SSe_(2)phase, and its expansion to P_(4)S_(3-x)Se_(x)(x = 0, 1, 2, 3) family can prove its significance for LIBs.

Preparation of biomass-derived carbon loaded with MnO_(2) as lithium-ion battery anode for improving its reversible capacity and cycling performance

Biomass-derived carbon materials for lithiumion batteries emerge as one of the most promising anodes from sustainable perspective.However,improving the reversible capacity and cycling performance remains a long-standing challenge.By combining the benefits of K2CO_(3) activation and KMnO_(4) hydrothermal treatment,this work proposes a two-step activation method to load MnO_(2) charge transfer onto biomass-derived carbon(KAC@MnO_(2)).Comprehensive analysis reveals that KAC@MnO_(2) has a micro-mesoporous coexistence structure and uniform surface distribution of MnO_(2),thus providing an improved electrochemical performance.Specifically,KAC@MnO_(2) exhibits an initial chargedischarge capacity of 847.3/1813.2 mAh·g^(-1) at 0.2 A·g^(-1),which is significantly higher than that of direct pyrolysis carbon and K2CO_(3) activated carbon,respectively.Furthermore,the KAC@MnO_(2) maintains a reversible capacity of 652.6 mAh·g^(-1) after 100 cycles.Even at a high current density of 1.0 A·g^(-1),KAC@MnO_(2) still exhibits excellent long-term cycling stability and maintains a stable reversible capacity of 306.7 mAh·g^(-1) after 500 cycles.Compared with reported biochar anode materials,the KAC@MnO_(2) prepared in this work shows superior reversible capacity and cycling performance.Additionally,the Li+insertion and de-insertion mechanisms are verified by ex situ X-ray diffraction analysis during the chargedischarge process,helping us better understand the energy storage mechanism of KAC@MnO_(2).

Capacity degradation analysis and knee point prediction for lithium-ion batteries

Analyzing capacity degradation characteristics and accurately predicting the knee point of capacity are crucial for the safety management of lithium-ion batteries(LIBs).However,the degradation mechanism of LIBs is complex.A key but challenging problem is how to clarify the degradation mechanism and predict the knee point.According to the external characteristics such as capacity decline gradievnt and the peak value of increment capacity curve(IC curve),the capacity degradation can be divided into four stages,including initial decline stage,slow decline stage,transition stage and high-speed decline stage.The degradation mechanism of LIBs is compared from the longitudinal and horizontal aspects,respectively.Among them,the battery usage from the initial stage to the end of life(EOL)is longitudinal analysis.The battery under different conditions,such as charging and discharging,different discharge rate,different cathode material degradation mechanism is horizontal analysis.Moreover,a method based on neural network is proposed to predict the knee point.Two features are used to predict the capacity and cycle of the knee point,which are the gradient of the capacity degradation curve and the difference of the IC curve with the maximum correlation.The experimental results show that a two-dimensional surface can be obtained using only the first 100 cycles,which can provide a reference for the position of the knee point accurately prediction.

Mn-based MXene with high lithium-ion storage capacity

3d-transition metal(Fe,Co,Ni,and Mn)-based MXene materials have been predicted to demonstrate exceptional electrochemical performance because of their good electrical conductivity and the presence of metallic atoms with multiple charge states.However,until now,there have been no reports on MXenes based on Fe,Co,Ni,and Mn,due to the lack of 3d-metal-layered precursors.Herein,we successfully synthesized the first 3d-transition metal-based MXenes,Mn_(2)CT_(x) by exfoliating a layered precursor derived from the anti-perovskite bulk Mn3GaC.The as-prepared Mn_(2)CT_(x) MXene nanosheets were employed as anode materials in lithium-ion batteries,which exhibited stable storage capacity of 764.7 mAh·g^(-1) at 0.5 C,placing its storage capacities at an upper-middle level compared with other reported MXene materials as well as other Mn-based anode materials.Overall,this study opens a new avenue for MXene research by synthesizing 3d-transition metal-based MXenes for electrochemical applications.

Practical evaluation of prelithiation strategies for next-generation lithium-ion batteries

With the increasing market demand for high-performance lithium-ion batteries with high-capacity electrode materials,reducing the irreversible capacity loss in the initial cycle and compensating for the active lithium loss during the cycling process are critical challenges.In recent years,various prelithiation strategies have been developed to overcome these issues.Since these approaches are carried out under a wide range of conditions,it is essential to evaluate their suitability for large-scale commercial applications.In this review,these strategies are categorized based on different battery assembling stages that they are implemented in,including active material synthesis,the slurry mixing process,electrode pretreatment,and battery fabrication.Furthermore,their advantages and disadvantages in commercial production are discussed from the perspective of thermodynamics and kinetics.This review aims to provide guidance for the future development of prelithiation strategies toward commercialization,which will potentially promote the practical application of next-generation high-energy-density lithium-ion batteries.