Dear Editor,This letter presents a latent-factorization-of-tensors(LFT)-incorporated battery cycle life prediction framework.Data-driven prognosis and health management(PHM)for battery pack(BP)can boost the safety and...Dear Editor,This letter presents a latent-factorization-of-tensors(LFT)-incorporated battery cycle life prediction framework.Data-driven prognosis and health management(PHM)for battery pack(BP)can boost the safety and sustainability of a battery management system(BMS),which relies heavily on the quality of the measured BP data like the voltage(V),current(I),and temperature(T).展开更多
The operation of deep-sea underwater vehicles relies entirely on onboard batteries.However,the extreme deep-sea conditions,characterized by ultrahigh hydraulic pressure,low temperature,and seawater conductivity,pose s...The operation of deep-sea underwater vehicles relies entirely on onboard batteries.However,the extreme deep-sea conditions,characterized by ultrahigh hydraulic pressure,low temperature,and seawater conductivity,pose significant challenges for battery development.These conditions drive the need for specialized designs in deep-sea batteries,incorporating critical aspects of power generation,protection,distribution,and management.Over time,deep-sea battery technology has evolved through multiple generations,with lithium(Li)batteries emerging in recent decades as the preferred power source due to their high energy and reduced operational risks.Although the rapid progress of Li batteries has notably advanced the capabilities of underwater vehicles,critical technical issues remain unresolved.This review first systematically presents the whole picture of deep-sea battery manufacturing,focusing on Li batteries as the current mainstream solution for underwater power.It examines the key aspects of deep-sea Li battery development,including materials selection informed by electro-chemo-mechanics models,component modification and testing,and battery management systems specialized in software and hardware.Finally,it discusses the main challenges limiting the utilization of deep-sea batteries and outlines promising directions for future development.Based on the systematic reflection on deep-sea batteries and discussion on deep-sea Li batteries,this review aims to provide a research foundation for developing underwater power tailored for extreme environmental exploration.展开更多
Na-ion batteries are considered a promising next-generation battery alternative to Li-ion batteries,due to the abundant Na resources and low cost.Most efforts focus on developing new materials to enhance energy densit...Na-ion batteries are considered a promising next-generation battery alternative to Li-ion batteries,due to the abundant Na resources and low cost.Most efforts focus on developing new materials to enhance energy density and electrochemical performance to enable it comparable to Li-ion batteries,without considering thermal hazard of Na-ion batteries and comparison with Li-ion batteries.To address this issue,our work comprehensively compares commercial prismatic lithium iron phosphate(LFP) battery,lithium nickel cobalt manganese oxide(NCM523) battery and Na-ion battery of the same size from thermal hazard perspective using Accelerating Rate Calorimeter.The thermal hazard of the three cells is then qualitatively assessed from thermal stability,early warning and thermal runaway severity perspectives by integrating eight characteristic parameters.The Na-ion cell displays comparable thermal stability with LFP while LFP exhibits the lowest thermal runaway hazard and severity.However,the Na-ion cell displays the lowest safety venting temperature and the longest time interval between safety venting and thermal runaway,allowing the generated gas to be released as early as possible and detected in a timely manner,providing sufficient time for early warning.Finally,a database of thermal runaway characteristic temperature for Li-ion and Na-ion cells is collected and processed to delineate four thermal hazard levels for quantitative assessment.Overall,LFP cells exhibit the lowest thermal hazard,followed by the Na-ion cells and NCM523 cells.This work clarifies the thermal hazard discrepancy between the Na-ion cell and prevalent Li-ion cells,providing crucial guidance for development and application of Na-ion cell.展开更多
Exploration budgets for primary battery metals-nickel,lithium and cobalt-tempered in 2024 at$1.697 billion,reflecting a marginal 0.4%decline and a virtually flat annual total,compared to$1.704 billion in 2023.Below is...Exploration budgets for primary battery metals-nickel,lithium and cobalt-tempered in 2024 at$1.697 billion,reflecting a marginal 0.4%decline and a virtually flat annual total,compared to$1.704 billion in 2023.Below is an introduction to the 2024 global exploration trends and prospects for lithium,cobalt,and nickel battery metals.展开更多
The implementation of the standard is expected to help electric vehicle battery swap stations to adapt to diversified needs and vehicle models,promoting the industry’s orderly and healthy development.
As battery deployments in electric vehicles and energy storage systems grow, ensuring homogeneous performance across units is crucial. We propose a multi-derivative imaging fusion(MDIF) model, employing advanced imagi...As battery deployments in electric vehicles and energy storage systems grow, ensuring homogeneous performance across units is crucial. We propose a multi-derivative imaging fusion(MDIF) model, employing advanced imaging and machine learning to predict battery aging trajectories from minimal initial data, thus facilitating effective performance grouping before deployment. Utilizing a derivative strategy and Gramian Angular Difference Field for dimensional enhancement, the MDIF model uncovers subtle predictive features from discharge curve data after only ten cycles. The architecture includes a parallel convolutional neural network with lateral connections to enhance feature integration and extraction.Tested on a self-developed dataset, the model achieves an average root-mean-square error of 0.047 Ah and an average mean absolute percentage error of 1.60%, demonstrating high precision and reliability.Its robustness is further validated through transfer learning on two publicly available datasets, adapting with minimal retraining. This approach significantly reduces the testing cycles required, lowering both time and costs associated with battery testing. By enabling precise battery behavior predictions with limited data, the MDIF model optimizes battery utilization and deployment strategies, enhancing system efficiency and sustainability.展开更多
Aqueous hybrid-ion batteries(AHBs)are a promising class of energy storage devices characterized by low cost,high safety,and high energy density.However,aqueous Cu-Al hybrid-ion batteries face challenges such as sluggi...Aqueous hybrid-ion batteries(AHBs)are a promising class of energy storage devices characterized by low cost,high safety,and high energy density.However,aqueous Cu-Al hybrid-ion batteries face challenges such as sluggish reaction kinetics and severe structural collapse of cathode materials,which limit their practical application.Here,a high-performance aqueous Cu-Al hybrid-ion battery is developed using aluminum pre-inserted Cu_(9)S_(5)(Al-Cu_(9)S_(5))as the cathode material,derived from CuAl-layered double hydroxide(CuAl-LDH).The Al^(3+)pre-intercalation strategy narrows the band gap,enhancing electron transport and improving electrochemical kinetics.The battery exhibits excellent rate performance(463 and 408 mA h g^(-1)at current densities of 500 and 1000 mA g^(-1),respectively)and good cycle stability(with a capacity retention ratio of 81% after 300 cycles at a current density of 1000 mA g^(-1)).Its performance surpasses that of most reported Al-ion batteries.Ex situ characterization and density functional theory(DFT)calculations reveal that the pre-intercalated Al^(3+)in Al-Cu9S5participates in the reversible embedding/removal of Al ions during charge/discharge processes.These findings provide valuable insights for designing pre-intercalated cathodes in aqueous Cu-Al hybrid-ion batteries with stable cycle life.展开更多
Flexible Zn-based batteries have attracted increasing research interest as essential components of wearable energy storage devices.However,the advancement of flexible aqueous Zn-based batteries based on Co-Ni layered ...Flexible Zn-based batteries have attracted increasing research interest as essential components of wearable energy storage devices.However,the advancement of flexible aqueous Zn-based batteries based on Co-Ni layered double hydroxide (CoNi-LDH) as the cathode material is hampered by their poor cycling stability and the corrosiveness of alkaline electrolytes.Herein,CoNi-LDH nanosheets enriched with H vacancies (CoNi-LDH(v)) were constructed on a flexible carbon cloth (CC) substrate via electrochemical deposition and activation.The Zn-based battery comprising CoNi-LDH(v)@CC as the cathode exhibited highly reversible conversion reactions and stable operation in 3 M ZnSO4electrolyte (pH=4).The battery delivered an excellent specific capacity (225 mA h g^(-1),0.26 mA h cm^(-2)),acceptable cycling stability(53.9%,900 cycles),and high discharging voltage.The abundant H vacancies served as active sites for the reversible intercalation of Zn^(2+)and the extravasation of NO_(3)-generated channels and space for Zn^(2+)transport and storage,together enabling an excellent Zn^(2+)storage capacity.Furthermore,a sandwich-structured solid-state CoNi-LDH(v)@CC//Zn@CC battery was fabricated and was found to exhibit a noteworthy electrochemical performance and mechanical durability.As a proof of concept,the unencapsulated battery powered a digital watch under various deformation conditions and operated stably for 80 h.Additionally,the flexible battery displayed outstanding customizability,maintaining an open-circuit voltage of 1.42 V even after being cut twice.The proposed engineering strategy contributes to the realization of textiles with truly wearable energy-storage devices.展开更多
Lithium-carbon dioxide(Li-CO_(2))batteries using high ion-conductive inorganic molten salt electrolytes have recently attracted much attention due to the high energy density and potential application of carbon neutral...Lithium-carbon dioxide(Li-CO_(2))batteries using high ion-conductive inorganic molten salt electrolytes have recently attracted much attention due to the high energy density and potential application of carbon neutrality.However,the poor Li-ion conductivity of the molten-salt electrolytes at room temperature(RT)makes these batteries lose most of their capacity and power as the temperature falls below 80℃.Here,inspired by the greenhouse effect,we report an RT molten salt Li-CO_(2)battery where solar energy can be efficiently harvested and converted into heat that is further localized on the cathode consisting of plasmonic ruthenium(Ru)catalysts and Li_(2)CO_(3)-based products via a greenhouse-like phenomenon.As a result,the solar-driven molten salt Li-CO_(2)battery demonstrates a larger full discharge/charge capacity of 9.5 mA h/8.1 mA h,and a longer cycle lifespan of 250 cycles at 500 mA/g with a limited capacity of 500 mA h/g at RT than the molten salt Li-CO_(2)battery at 130℃.Notably,the average temperature of the cathode increases by 8℃ after discharge to 0.75 mA h,which indicates the infrared radiation from Ru catalysts can be effectively suppressed by discharged Li_(2)CO_(3)-based products.This battery technology paves the way for developing low-temperature molten salt energy storage devices.展开更多
The increasing penetration of second-life battery energy storage systems(SLBESS)in power grids presents substantial challenges to system operation and control due to the heterogeneous characteristics and uncertain deg...The increasing penetration of second-life battery energy storage systems(SLBESS)in power grids presents substantial challenges to system operation and control due to the heterogeneous characteristics and uncertain degradation patterns of repurposed batteries.This paper presents a novel model-free adaptive voltage controlembedded dung beetle-inspired heuristic optimization algorithmfor optimal SLBESS capacity configuration and power dispatch.To simultaneously address the computational complexity and ensure system stability,this paper develops a comprehensive bilevel optimization framework.At the upper level,a dung beetle optimization algorithmdetermines the optimal SLBESS capacity configuration byminimizing total lifecycle costswhile incorporating the charging/discharging power trajectories derived from the model-free adaptive voltage control strategy.At the lower level,a health-priority power dispatch optimization model intelligently allocates power demands among heterogeneous battery groups based on their real-time operational states,state-of-health variations,and degradation constraints.The proposed model-free approach circumvents the need for complex battery charging/discharging power controlmodels and extensive historical data requirements whilemaintaining system stability through adaptive controlmechanisms.A novel cycle life degradation model is developed to quantify the relationship between remaining useful life,depth of discharge,and operational patterns.The integrated framework enables simultaneous strategic planning and operational control,ensuring both economic efficiency and extended battery lifespan.The effectiveness of the proposed method is validated through comprehensive case studies on hybrid energy storage systems,demonstrating superior computational efficiency,robust performance across different network configurations,and significant improvements in battery utilization compared to conventional approaches.展开更多
A sustainable approach for recovering battery grade FePO_(4) and Li_(2)CO_(3) from Al/F-bearing spent LiFePO_(4)/C powder was proposed,including acid leaching,fluorinated coordination precipitation,homogeneous precipi...A sustainable approach for recovering battery grade FePO_(4) and Li_(2)CO_(3) from Al/F-bearing spent LiFePO_(4)/C powder was proposed,including acid leaching,fluorinated coordination precipitation,homogeneous precipitation,and high-temperature precipitation.Under the optimal conditions,the leaching efficiencies of Li,Fe,P,Al,and F were 97.6%,97.1%,97.1%,72.5%,and 63.3%,respectively.The effects of different parameters on the removal of Al/F impurities were systematically evaluated,indicating about 99.4%Al and 96.4%F in the leachate were precipitated in the form of Na_(3)Li_(3)Al_(2)F_(12),and their residual concentrations were only 0.0124 and 0.328 g/L,respectively,which could be directly used to prepare battery grade FePO_(4)(99.68%in purity).Lithium in the Al/F-bearing residue could be extracted through CaCO_(3)−CaSO_(4) roasting followed by acid leaching,ultimately obtaining 99.87%purity of Li_(2)CO_(3).The recovery rates of Li and Fe were 96.88%and 92.85%,respectively.An economic evaluation demonstrated that the process was profitable.展开更多
Graphdiyne(GDY)is a two-dimensional carbon allotrope with exceptional physical and chemical properties that is gaining increasing attention.However,its efficient and scalable synthesis remains a significant challenge....Graphdiyne(GDY)is a two-dimensional carbon allotrope with exceptional physical and chemical properties that is gaining increasing attention.However,its efficient and scalable synthesis remains a significant challenge.We present a microwave-assisted approach for its continuous,large-scale production which enables synthesis at a rate of 0.6 g/h,with a yield of up to 90%.The synthesized GDY nanosheets have an average diameter of 246 nm and a thickness of 4 nm.We used GDY as a stable coating for potassium(K)metal anodes(K@GDY),taking advantage of its unique molecular structure to provide favorable paths for K-ion transport.This modification significantly inhibited dendrite formation and improved the cycling stability of K metal batteries.Full-cells with perylene-3,4,9,10-tetracarboxylic dianhydride(PTCDA)cathodes showed the clear superiority of the K@GDY anodes over bare K anodes in terms of performance,stability,and cycle life.The K@GDY maintained a stable voltage plateau and gave an excellent capacity retention after 600 cycles with nearly 100%Coulombic efficiency.This work not only provides a scalable and efficient way for GDY synthesis but also opens new possibilities for its use in energy storage and other advanced technologies.展开更多
This study shows that sulfide solid-state electrolytes,β-Li_(3)PS_(4)and Li_(6)PS_(5)Cl,are flammable solids.Both solid-state electrolytes release sulfur vapor in a dry,oxidizing environment at elevated temperature&l...This study shows that sulfide solid-state electrolytes,β-Li_(3)PS_(4)and Li_(6)PS_(5)Cl,are flammable solids.Both solid-state electrolytes release sulfur vapor in a dry,oxidizing environment at elevated temperature<300℃.Sulfur vapor is a highly flammable gas,which then auto-ignites to produce a flame.This behavior suggests that an O_(2)-S gas-gas reaction mechanism may contribute to all-solid-state battery thermal runaway.To improve all-solid-state battery safety,current work focuses on eliminating the O_(2)source by changing the cathode active material.The conclusion of this study suggests that all-solidstate battery safety can also be realized by the development of solid-state electrolytes with less susceptibility to sulfur volatilization.展开更多
The burgeoning market for lithium-ion batteries has stimulated a growing need for more reliable battery performance monitoring. Accurate state-of-health(SOH) estimation is critical for ensuring battery operational per...The burgeoning market for lithium-ion batteries has stimulated a growing need for more reliable battery performance monitoring. Accurate state-of-health(SOH) estimation is critical for ensuring battery operational performance. Despite numerous data-driven methods reported in existing research for battery SOH estimation, these methods often exhibit inconsistent performance across different application scenarios. To address this issue and overcome the performance limitations of individual data-driven models,integrating multiple models for SOH estimation has received considerable attention. Ensemble learning(EL) typically leverages the strengths of multiple base models to achieve more robust and accurate outputs. However, the lack of a clear review of current research hinders the further development of ensemble methods in SOH estimation. Therefore, this paper comprehensively reviews multi-model ensemble learning methods for battery SOH estimation. First, existing ensemble methods are systematically categorized into 6 classes based on their combination strategies. Different realizations and underlying connections are meticulously analyzed for each category of EL methods, highlighting distinctions, innovations, and typical applications. Subsequently, these ensemble methods are comprehensively compared in terms of base models, combination strategies, and publication trends. Evaluations across 6 dimensions underscore the outstanding performance of stacking-based ensemble methods. Following this, these ensemble methods are further inspected from the perspectives of weighted ensemble and diversity, aiming to inspire potential approaches for enhancing ensemble performance. Moreover, addressing challenges such as base model selection, measuring model robustness and uncertainty, and interpretability of ensemble models in practical applications is emphasized. Finally, future research prospects are outlined, specifically noting that deep learning ensemble is poised to advance ensemble methods for battery SOH estimation. The convergence of advanced machine learning with ensemble learning is anticipated to yield valuable avenues for research. Accelerated research in ensemble learning holds promising prospects for achieving more accurate and reliable battery SOH estimation under real-world conditions.展开更多
Accurate prediction of the remaining useful life(RUL)is crucial for the design and management of lithium-ion batteries.Although various machine learning models offer promising predictions,one critical but often overlo...Accurate prediction of the remaining useful life(RUL)is crucial for the design and management of lithium-ion batteries.Although various machine learning models offer promising predictions,one critical but often overlooked challenge is their demand for considerable run-to-failure data for training.Collection of such training data leads to prohibitive testing efforts as the run-to-failure tests can last for years.Here,we propose a semi-supervised representation learning method to enhance prediction accuracy by learning from data without RUL labels.Our approach builds on a sophisticated deep neural network that comprises an encoder and three decoder heads to extract time-dependent representation features from short-term battery operating data regardless of the existence of RUL labels.The approach is validated using three datasets collected from 34 batteries operating under various conditions,encompassing over 19,900 charge and discharge cycles.Our method achieves a root mean squared error(RMSE)within 25 cycles,even when only 1/50 of the training dataset is labelled,representing a reduction of 48%compared to the conventional approach.We also demonstrate the method's robustness with varying numbers of labelled data and different weights assigned to the three decoder heads.The projection of extracted features in low space reveals that our method effectively learns degradation features from unlabelled data.Our approach highlights the promise of utilising semi-supervised learning to reduce the data demand for reliability monitoring of energy devices.展开更多
The swelling behavior and stability in solid electrolyte interphase(SEI)have been proved to determine the battery cycle life.A high swollen,unstable SEI shows a high permeability to electrolyte,which results in the ra...The swelling behavior and stability in solid electrolyte interphase(SEI)have been proved to determine the battery cycle life.A high swollen,unstable SEI shows a high permeability to electrolyte,which results in the rapid battery performance degradation.Here,we customize two SEIs with different spatial structures(bilayer and mosaic)by simply regulating the proportion of additive fluoroethylene carbonate.Surprisingly,due to the uniform distribution of dense inorganic nano-crystals in the inner,the bilayer SEI exhibits low-swelling and excellent mechanical properties,so the undesirable side reactions of the electrolyte are effectively suppressed.In addition,we put forward the growth rate of swelling ratio(GSR)as a key indicator to reveal the swelling change in SEI.The GSR of bilayer SEI merely increases from1.73 to 3.16 after the 300th cycle,which enables the corresponding graphite‖Li battery to achieve longer cycle stability.The capacity retention is improved by 47.5% after 300 cycles at 0.5 C.The correlation among SEI spatial structure,swelling behavior,and battery performance provides a new direction for electrolyte optimization and interphase structure design of high energy density batteries.展开更多
As energy demands continue to rise in modern society,the development of high-performance lithium-ion batteries(LIBs)has become crucial.However,traditional research methods of material science face challenges such as l...As energy demands continue to rise in modern society,the development of high-performance lithium-ion batteries(LIBs)has become crucial.However,traditional research methods of material science face challenges such as lengthy timelines and complex processes.In recent years,the integration of machine learning(ML)in LIB materials,including electrolytes,solid-state electrolytes,and electrodes,has yielded remarkable achievements.This comprehensive review explores the latest applications of ML in predicting LIB material performance,covering the core principles and recent advancements in three key inverse material design strategies:high-throughput virtual screening,global optimization,and generative models.These strategies have played a pivotal role in fostering LIB material innovations.Meanwhile,the paper briefly discusses the challenges associated with applying ML to materials research and offers insights and directions for future research.展开更多
This paper introduces a high-precision bandgap reference(BGR)designed for battery management systems(BMS),fea-turing an ultra-low temperature coefficient(TC)and line sensitivity(LS).The BGR employs a current-mode sche...This paper introduces a high-precision bandgap reference(BGR)designed for battery management systems(BMS),fea-turing an ultra-low temperature coefficient(TC)and line sensitivity(LS).The BGR employs a current-mode scheme with chopped op-amps and internal clock generators to eliminate op-amp offset.A low dropout regulator(LDO)and a pre-regula-tor enhance output driving and LS,respectively.Curvature compensation enhances the TC by addressing higher-order nonlinear-ity.These approaches,effective near room temperature,employs trimming at both 20 and 60°C.When combined with fixed cur-vature correction currents,it achieves an ultra-low TC for each chip.Implemented in a CMOS 180 nm process,the BGR occu-pies 0.548 mm²and operates at 2.5 V with 84μA current draw from a 5 V supply.An average TC of 2.69 ppm/℃ with two-point trimming and 0.81 ppm/℃ with multi-point trimming are achieved over the temperature range of-40 to 125℃.It accommo-dates a load current of 1 mA and an LS of 42 ppm/V,making it suitable for precise BMS applications.展开更多
As batteries become increasingly essential for energy storage technologies,battery prognosis,and diagnosis remain central to ensure reliable operation and effective management,as well as to aid the in-depth investigat...As batteries become increasingly essential for energy storage technologies,battery prognosis,and diagnosis remain central to ensure reliable operation and effective management,as well as to aid the in-depth investigation of degradation mechanisms.However,dynamic operating conditions,cell-to-cell inconsistencies,and limited availability of labeled data have posed significant challenges to accurate and robust prognosis and diagnosis.Herein,we introduce a time-series-decomposition-based ensembled lightweight learning model(TELL-Me),which employs a synergistic dual-module framework to facilitate accurate and reliable forecasting.The feature module formulates features with physical implications and sheds light on battery aging mechanisms,while the gradient module monitors capacity degradation rates and captures aging trend.TELL-Me achieves high accuracy in end-of-life prediction using minimal historical data from a single battery without requiring offline training dataset,and demonstrates impressive generality and robustness across various operating conditions and battery types.Additionally,by correlating feature contributions with degradation mechanisms across different datasets,TELL-Me is endowed with the diagnostic ability that not only enhances prediction reliability but also provides critical insights into the design and optimization of next-generation batteries.展开更多
The future large-scale application of sodium-ion batteries(SIBs)is inseparable from their excellent electrochemical performance and reliable safety characteristics.At present,there are few studies focusing on their sa...The future large-scale application of sodium-ion batteries(SIBs)is inseparable from their excellent electrochemical performance and reliable safety characteristics.At present,there are few studies focusing on their safety performance.The analysis of thermal stability and structural changes within a single material cannot systematically describe the complex interplay of components within the battery system during the thermal runaway process.Furthermore,the reaction between the battery materials themselves and their counterparts within the system can stimulate more intense exothermic behavior,thereby affecting the safety of the entire battery system.Therefore,this study delved into the thermal generation and gas evolution characteristics of the positive electrode(Na_(x)Ni_(1/3)Fe_(1/3)Mn_(1/3)O_(2),NFM111)and the negative electrode(hard carbon,HC)in SIBs,utilizing various material combinations.Through the integration of microscopic and macroscopic characterization techniques,the underlying reaction mechanisms of the positive and negative electrode materials within the battery during the heating process were elucidated.Three important results are derived from this study:(Ⅰ)The instability of the solid electrolyte interphase(SEI)leads to its decomposition at temperatures below 100℃,followed by extensive decomposition within the range of 100-150℃,yielding heat and the formation of inorganic compounds,such as Na_(2)CO_(3)and Na_(2)O;(Ⅱ)The reaction between NFM111 and the electrolyte constitutes the primary exothermic event during thermal abuse,with a discernible reaction also occurring between sodium metal and the electrolyte throughout the heating process;(Ⅲ)The heat production and gas generation behaviors of multi-component reactions do not exhibit complete correlation,and the occurrence of gas production does not necessarily coincide with thermal behavior.The results presented in this study can provide useful guidance for the safety improvement of SIBs.展开更多
文摘Dear Editor,This letter presents a latent-factorization-of-tensors(LFT)-incorporated battery cycle life prediction framework.Data-driven prognosis and health management(PHM)for battery pack(BP)can boost the safety and sustainability of a battery management system(BMS),which relies heavily on the quality of the measured BP data like the voltage(V),current(I),and temperature(T).
基金support provided by National Key Research and Development Program of China(2023YFE0203000 and 2016YFC0300200)the NSAF(Grant No.U2330205)+3 种基金Full-Sea-Depth Battery Project(2020-XXXX-XX-246-00)Open project of Shaanxi Laboratory of Aerospace Power(2022ZY2-JCYJ-01-09)Fundamental Research Funds for the Central Universities,ND Basic Research Funds(G2022WD)the Innovation Team of Shaanxi Province。
文摘The operation of deep-sea underwater vehicles relies entirely on onboard batteries.However,the extreme deep-sea conditions,characterized by ultrahigh hydraulic pressure,low temperature,and seawater conductivity,pose significant challenges for battery development.These conditions drive the need for specialized designs in deep-sea batteries,incorporating critical aspects of power generation,protection,distribution,and management.Over time,deep-sea battery technology has evolved through multiple generations,with lithium(Li)batteries emerging in recent decades as the preferred power source due to their high energy and reduced operational risks.Although the rapid progress of Li batteries has notably advanced the capabilities of underwater vehicles,critical technical issues remain unresolved.This review first systematically presents the whole picture of deep-sea battery manufacturing,focusing on Li batteries as the current mainstream solution for underwater power.It examines the key aspects of deep-sea Li battery development,including materials selection informed by electro-chemo-mechanics models,component modification and testing,and battery management systems specialized in software and hardware.Finally,it discusses the main challenges limiting the utilization of deep-sea batteries and outlines promising directions for future development.Based on the systematic reflection on deep-sea batteries and discussion on deep-sea Li batteries,this review aims to provide a research foundation for developing underwater power tailored for extreme environmental exploration.
基金supported by the National Key R&D Program of China(No.2022YFE0207400)supported by the Xiaomi Young Talents Programsupported by the Youth Innovation Promotion Association CAS(No.Y201768)。
文摘Na-ion batteries are considered a promising next-generation battery alternative to Li-ion batteries,due to the abundant Na resources and low cost.Most efforts focus on developing new materials to enhance energy density and electrochemical performance to enable it comparable to Li-ion batteries,without considering thermal hazard of Na-ion batteries and comparison with Li-ion batteries.To address this issue,our work comprehensively compares commercial prismatic lithium iron phosphate(LFP) battery,lithium nickel cobalt manganese oxide(NCM523) battery and Na-ion battery of the same size from thermal hazard perspective using Accelerating Rate Calorimeter.The thermal hazard of the three cells is then qualitatively assessed from thermal stability,early warning and thermal runaway severity perspectives by integrating eight characteristic parameters.The Na-ion cell displays comparable thermal stability with LFP while LFP exhibits the lowest thermal runaway hazard and severity.However,the Na-ion cell displays the lowest safety venting temperature and the longest time interval between safety venting and thermal runaway,allowing the generated gas to be released as early as possible and detected in a timely manner,providing sufficient time for early warning.Finally,a database of thermal runaway characteristic temperature for Li-ion and Na-ion cells is collected and processed to delineate four thermal hazard levels for quantitative assessment.Overall,LFP cells exhibit the lowest thermal hazard,followed by the Na-ion cells and NCM523 cells.This work clarifies the thermal hazard discrepancy between the Na-ion cell and prevalent Li-ion cells,providing crucial guidance for development and application of Na-ion cell.
文摘Exploration budgets for primary battery metals-nickel,lithium and cobalt-tempered in 2024 at$1.697 billion,reflecting a marginal 0.4%decline and a virtually flat annual total,compared to$1.704 billion in 2023.Below is an introduction to the 2024 global exploration trends and prospects for lithium,cobalt,and nickel battery metals.
文摘The implementation of the standard is expected to help electric vehicle battery swap stations to adapt to diversified needs and vehicle models,promoting the industry’s orderly and healthy development.
基金National Natural Science Foundation of China (Grant No. 52225705)。
文摘As battery deployments in electric vehicles and energy storage systems grow, ensuring homogeneous performance across units is crucial. We propose a multi-derivative imaging fusion(MDIF) model, employing advanced imaging and machine learning to predict battery aging trajectories from minimal initial data, thus facilitating effective performance grouping before deployment. Utilizing a derivative strategy and Gramian Angular Difference Field for dimensional enhancement, the MDIF model uncovers subtle predictive features from discharge curve data after only ten cycles. The architecture includes a parallel convolutional neural network with lateral connections to enhance feature integration and extraction.Tested on a self-developed dataset, the model achieves an average root-mean-square error of 0.047 Ah and an average mean absolute percentage error of 1.60%, demonstrating high precision and reliability.Its robustness is further validated through transfer learning on two publicly available datasets, adapting with minimal retraining. This approach significantly reduces the testing cycles required, lowering both time and costs associated with battery testing. By enabling precise battery behavior predictions with limited data, the MDIF model optimizes battery utilization and deployment strategies, enhancing system efficiency and sustainability.
基金supported by the National Natural Science Foundation of China(Nos.22278020 and 2177060378)the Fundamental Research Funds for the Central Universities(Nos.12060093063 and XK1803-05)the Program for Changjiang Scholars and Innovative Research Teams in University(No.IRT1205)。
文摘Aqueous hybrid-ion batteries(AHBs)are a promising class of energy storage devices characterized by low cost,high safety,and high energy density.However,aqueous Cu-Al hybrid-ion batteries face challenges such as sluggish reaction kinetics and severe structural collapse of cathode materials,which limit their practical application.Here,a high-performance aqueous Cu-Al hybrid-ion battery is developed using aluminum pre-inserted Cu_(9)S_(5)(Al-Cu_(9)S_(5))as the cathode material,derived from CuAl-layered double hydroxide(CuAl-LDH).The Al^(3+)pre-intercalation strategy narrows the band gap,enhancing electron transport and improving electrochemical kinetics.The battery exhibits excellent rate performance(463 and 408 mA h g^(-1)at current densities of 500 and 1000 mA g^(-1),respectively)and good cycle stability(with a capacity retention ratio of 81% after 300 cycles at a current density of 1000 mA g^(-1)).Its performance surpasses that of most reported Al-ion batteries.Ex situ characterization and density functional theory(DFT)calculations reveal that the pre-intercalated Al^(3+)in Al-Cu9S5participates in the reversible embedding/removal of Al ions during charge/discharge processes.These findings provide valuable insights for designing pre-intercalated cathodes in aqueous Cu-Al hybrid-ion batteries with stable cycle life.
基金National Natural Science Foundation of China (52003191,5247317, 52473275)Young Elite Scientists Sponsorship Program by CAST (2022QNRC001)+3 种基金Natural Science Foundation of Jiangsu Province (BK20221539)Postgraduate Research&Practice Innovation Program of Jiangsu Province (KYCX22_2341)Program of Introducing Talents of Jiangnan University (1065219032210150)Program of China Scholarship Council (202306790065)。
文摘Flexible Zn-based batteries have attracted increasing research interest as essential components of wearable energy storage devices.However,the advancement of flexible aqueous Zn-based batteries based on Co-Ni layered double hydroxide (CoNi-LDH) as the cathode material is hampered by their poor cycling stability and the corrosiveness of alkaline electrolytes.Herein,CoNi-LDH nanosheets enriched with H vacancies (CoNi-LDH(v)) were constructed on a flexible carbon cloth (CC) substrate via electrochemical deposition and activation.The Zn-based battery comprising CoNi-LDH(v)@CC as the cathode exhibited highly reversible conversion reactions and stable operation in 3 M ZnSO4electrolyte (pH=4).The battery delivered an excellent specific capacity (225 mA h g^(-1),0.26 mA h cm^(-2)),acceptable cycling stability(53.9%,900 cycles),and high discharging voltage.The abundant H vacancies served as active sites for the reversible intercalation of Zn^(2+)and the extravasation of NO_(3)-generated channels and space for Zn^(2+)transport and storage,together enabling an excellent Zn^(2+)storage capacity.Furthermore,a sandwich-structured solid-state CoNi-LDH(v)@CC//Zn@CC battery was fabricated and was found to exhibit a noteworthy electrochemical performance and mechanical durability.As a proof of concept,the unencapsulated battery powered a digital watch under various deformation conditions and operated stably for 80 h.Additionally,the flexible battery displayed outstanding customizability,maintaining an open-circuit voltage of 1.42 V even after being cut twice.The proposed engineering strategy contributes to the realization of textiles with truly wearable energy-storage devices.
基金supported by the National Natural Science Foundation of China(NSFC,62104099,61921005,62105048,62204117 and 62073299)the Science and Technology Research Program of Chongqing Education Commission(KJQN202100633)+5 种基金the Postdoctoral Science Foundation of China(2021M693768 and 2021M701057)the Key Scientific Research Project in Colleges and Universities of Henan Province,China(21A416001)the Key Laboratory for Special Functional Materials(KEKT2022-06)the Natural Science Foundation of Jiangsu Province(BK20210275 and BK20230498)the support from Jiangsu Province Science Foundation for Youths(BK20210275)National Natural Science Foundation of China(NSFC,62204117)。
文摘Lithium-carbon dioxide(Li-CO_(2))batteries using high ion-conductive inorganic molten salt electrolytes have recently attracted much attention due to the high energy density and potential application of carbon neutrality.However,the poor Li-ion conductivity of the molten-salt electrolytes at room temperature(RT)makes these batteries lose most of their capacity and power as the temperature falls below 80℃.Here,inspired by the greenhouse effect,we report an RT molten salt Li-CO_(2)battery where solar energy can be efficiently harvested and converted into heat that is further localized on the cathode consisting of plasmonic ruthenium(Ru)catalysts and Li_(2)CO_(3)-based products via a greenhouse-like phenomenon.As a result,the solar-driven molten salt Li-CO_(2)battery demonstrates a larger full discharge/charge capacity of 9.5 mA h/8.1 mA h,and a longer cycle lifespan of 250 cycles at 500 mA/g with a limited capacity of 500 mA h/g at RT than the molten salt Li-CO_(2)battery at 130℃.Notably,the average temperature of the cathode increases by 8℃ after discharge to 0.75 mA h,which indicates the infrared radiation from Ru catalysts can be effectively suppressed by discharged Li_(2)CO_(3)-based products.This battery technology paves the way for developing low-temperature molten salt energy storage devices.
基金Financial support was provided by the State Grid Sichuan Electric Power Company Science and Technology Project“Key Research on Development Path Planning and Key Operation Technologies of New Rural Electrification Construction”under Grant No.52199623000G.
文摘The increasing penetration of second-life battery energy storage systems(SLBESS)in power grids presents substantial challenges to system operation and control due to the heterogeneous characteristics and uncertain degradation patterns of repurposed batteries.This paper presents a novel model-free adaptive voltage controlembedded dung beetle-inspired heuristic optimization algorithmfor optimal SLBESS capacity configuration and power dispatch.To simultaneously address the computational complexity and ensure system stability,this paper develops a comprehensive bilevel optimization framework.At the upper level,a dung beetle optimization algorithmdetermines the optimal SLBESS capacity configuration byminimizing total lifecycle costswhile incorporating the charging/discharging power trajectories derived from the model-free adaptive voltage control strategy.At the lower level,a health-priority power dispatch optimization model intelligently allocates power demands among heterogeneous battery groups based on their real-time operational states,state-of-health variations,and degradation constraints.The proposed model-free approach circumvents the need for complex battery charging/discharging power controlmodels and extensive historical data requirements whilemaintaining system stability through adaptive controlmechanisms.A novel cycle life degradation model is developed to quantify the relationship between remaining useful life,depth of discharge,and operational patterns.The integrated framework enables simultaneous strategic planning and operational control,ensuring both economic efficiency and extended battery lifespan.The effectiveness of the proposed method is validated through comprehensive case studies on hybrid energy storage systems,demonstrating superior computational efficiency,robust performance across different network configurations,and significant improvements in battery utilization compared to conventional approaches.
基金financially supported by the Key Research and Development Program of Guangxi,China(No.GUIKE AB23026051)the Science and Technology Innovation Program of Hunan Province,China(No.2023RC3039)the Fundamental Research Funds for the Central Universities of Central South University,China.
文摘A sustainable approach for recovering battery grade FePO_(4) and Li_(2)CO_(3) from Al/F-bearing spent LiFePO_(4)/C powder was proposed,including acid leaching,fluorinated coordination precipitation,homogeneous precipitation,and high-temperature precipitation.Under the optimal conditions,the leaching efficiencies of Li,Fe,P,Al,and F were 97.6%,97.1%,97.1%,72.5%,and 63.3%,respectively.The effects of different parameters on the removal of Al/F impurities were systematically evaluated,indicating about 99.4%Al and 96.4%F in the leachate were precipitated in the form of Na_(3)Li_(3)Al_(2)F_(12),and their residual concentrations were only 0.0124 and 0.328 g/L,respectively,which could be directly used to prepare battery grade FePO_(4)(99.68%in purity).Lithium in the Al/F-bearing residue could be extracted through CaCO_(3)−CaSO_(4) roasting followed by acid leaching,ultimately obtaining 99.87%purity of Li_(2)CO_(3).The recovery rates of Li and Fe were 96.88%and 92.85%,respectively.An economic evaluation demonstrated that the process was profitable.
基金supported by National Natural Science Foundation of China(52302034,52402060,52202201,52021006)Beijing National Laboratory for Molecular Sciences(BNLMS-CXTD202001)+1 种基金Shenzhen Science and Technology Innovation Commission(KQTD20221101115627004)China Postdoctoral Science Foundation(2024T170972)。
文摘Graphdiyne(GDY)is a two-dimensional carbon allotrope with exceptional physical and chemical properties that is gaining increasing attention.However,its efficient and scalable synthesis remains a significant challenge.We present a microwave-assisted approach for its continuous,large-scale production which enables synthesis at a rate of 0.6 g/h,with a yield of up to 90%.The synthesized GDY nanosheets have an average diameter of 246 nm and a thickness of 4 nm.We used GDY as a stable coating for potassium(K)metal anodes(K@GDY),taking advantage of its unique molecular structure to provide favorable paths for K-ion transport.This modification significantly inhibited dendrite formation and improved the cycling stability of K metal batteries.Full-cells with perylene-3,4,9,10-tetracarboxylic dianhydride(PTCDA)cathodes showed the clear superiority of the K@GDY anodes over bare K anodes in terms of performance,stability,and cycle life.The K@GDY maintained a stable voltage plateau and gave an excellent capacity retention after 600 cycles with nearly 100%Coulombic efficiency.This work not only provides a scalable and efficient way for GDY synthesis but also opens new possibilities for its use in energy storage and other advanced technologies.
文摘This study shows that sulfide solid-state electrolytes,β-Li_(3)PS_(4)and Li_(6)PS_(5)Cl,are flammable solids.Both solid-state electrolytes release sulfur vapor in a dry,oxidizing environment at elevated temperature<300℃.Sulfur vapor is a highly flammable gas,which then auto-ignites to produce a flame.This behavior suggests that an O_(2)-S gas-gas reaction mechanism may contribute to all-solid-state battery thermal runaway.To improve all-solid-state battery safety,current work focuses on eliminating the O_(2)source by changing the cathode active material.The conclusion of this study suggests that all-solidstate battery safety can also be realized by the development of solid-state electrolytes with less susceptibility to sulfur volatilization.
基金National Natural Science Foundation of China (52075420)Fundamental Research Funds for the Central Universities (xzy022023049)National Key Research and Development Program of China (2023YFB3408600)。
文摘The burgeoning market for lithium-ion batteries has stimulated a growing need for more reliable battery performance monitoring. Accurate state-of-health(SOH) estimation is critical for ensuring battery operational performance. Despite numerous data-driven methods reported in existing research for battery SOH estimation, these methods often exhibit inconsistent performance across different application scenarios. To address this issue and overcome the performance limitations of individual data-driven models,integrating multiple models for SOH estimation has received considerable attention. Ensemble learning(EL) typically leverages the strengths of multiple base models to achieve more robust and accurate outputs. However, the lack of a clear review of current research hinders the further development of ensemble methods in SOH estimation. Therefore, this paper comprehensively reviews multi-model ensemble learning methods for battery SOH estimation. First, existing ensemble methods are systematically categorized into 6 classes based on their combination strategies. Different realizations and underlying connections are meticulously analyzed for each category of EL methods, highlighting distinctions, innovations, and typical applications. Subsequently, these ensemble methods are comprehensively compared in terms of base models, combination strategies, and publication trends. Evaluations across 6 dimensions underscore the outstanding performance of stacking-based ensemble methods. Following this, these ensemble methods are further inspected from the perspectives of weighted ensemble and diversity, aiming to inspire potential approaches for enhancing ensemble performance. Moreover, addressing challenges such as base model selection, measuring model robustness and uncertainty, and interpretability of ensemble models in practical applications is emphasized. Finally, future research prospects are outlined, specifically noting that deep learning ensemble is poised to advance ensemble methods for battery SOH estimation. The convergence of advanced machine learning with ensemble learning is anticipated to yield valuable avenues for research. Accelerated research in ensemble learning holds promising prospects for achieving more accurate and reliable battery SOH estimation under real-world conditions.
基金supported by the National Natural Science Foundation of China(No.52207229)the Key Research and Development Program of Ningxia Hui Autonomous Region of China(No.2024BEE02003)+1 种基金the financial support from the AEGiS Research Grant 2024,University of Wollongong(No.R6254)the financial support from the China Scholarship Council(No.202207550010).
文摘Accurate prediction of the remaining useful life(RUL)is crucial for the design and management of lithium-ion batteries.Although various machine learning models offer promising predictions,one critical but often overlooked challenge is their demand for considerable run-to-failure data for training.Collection of such training data leads to prohibitive testing efforts as the run-to-failure tests can last for years.Here,we propose a semi-supervised representation learning method to enhance prediction accuracy by learning from data without RUL labels.Our approach builds on a sophisticated deep neural network that comprises an encoder and three decoder heads to extract time-dependent representation features from short-term battery operating data regardless of the existence of RUL labels.The approach is validated using three datasets collected from 34 batteries operating under various conditions,encompassing over 19,900 charge and discharge cycles.Our method achieves a root mean squared error(RMSE)within 25 cycles,even when only 1/50 of the training dataset is labelled,representing a reduction of 48%compared to the conventional approach.We also demonstrate the method's robustness with varying numbers of labelled data and different weights assigned to the three decoder heads.The projection of extracted features in low space reveals that our method effectively learns degradation features from unlabelled data.Our approach highlights the promise of utilising semi-supervised learning to reduce the data demand for reliability monitoring of energy devices.
基金supported by the National Natural Science Foundation of China(22369011)the Gansu Key Research and Development Program(23YFGA0053 and 24YFGA025)the Hongliu Outstanding Youth Talent Support Program of Lanzhou University of Technology and Postgraduate research exploration project of Lanzhou University of Technology(256017)。
文摘The swelling behavior and stability in solid electrolyte interphase(SEI)have been proved to determine the battery cycle life.A high swollen,unstable SEI shows a high permeability to electrolyte,which results in the rapid battery performance degradation.Here,we customize two SEIs with different spatial structures(bilayer and mosaic)by simply regulating the proportion of additive fluoroethylene carbonate.Surprisingly,due to the uniform distribution of dense inorganic nano-crystals in the inner,the bilayer SEI exhibits low-swelling and excellent mechanical properties,so the undesirable side reactions of the electrolyte are effectively suppressed.In addition,we put forward the growth rate of swelling ratio(GSR)as a key indicator to reveal the swelling change in SEI.The GSR of bilayer SEI merely increases from1.73 to 3.16 after the 300th cycle,which enables the corresponding graphite‖Li battery to achieve longer cycle stability.The capacity retention is improved by 47.5% after 300 cycles at 0.5 C.The correlation among SEI spatial structure,swelling behavior,and battery performance provides a new direction for electrolyte optimization and interphase structure design of high energy density batteries.
基金supported by the National Natural Science Foundation of China(Grant Nos.22225801,W2441009,22408228)。
文摘As energy demands continue to rise in modern society,the development of high-performance lithium-ion batteries(LIBs)has become crucial.However,traditional research methods of material science face challenges such as lengthy timelines and complex processes.In recent years,the integration of machine learning(ML)in LIB materials,including electrolytes,solid-state electrolytes,and electrodes,has yielded remarkable achievements.This comprehensive review explores the latest applications of ML in predicting LIB material performance,covering the core principles and recent advancements in three key inverse material design strategies:high-throughput virtual screening,global optimization,and generative models.These strategies have played a pivotal role in fostering LIB material innovations.Meanwhile,the paper briefly discusses the challenges associated with applying ML to materials research and offers insights and directions for future research.
基金supported by the National Natural Science Foundation of China(NSFC)under grant No.62204235。
文摘This paper introduces a high-precision bandgap reference(BGR)designed for battery management systems(BMS),fea-turing an ultra-low temperature coefficient(TC)and line sensitivity(LS).The BGR employs a current-mode scheme with chopped op-amps and internal clock generators to eliminate op-amp offset.A low dropout regulator(LDO)and a pre-regula-tor enhance output driving and LS,respectively.Curvature compensation enhances the TC by addressing higher-order nonlinear-ity.These approaches,effective near room temperature,employs trimming at both 20 and 60°C.When combined with fixed cur-vature correction currents,it achieves an ultra-low TC for each chip.Implemented in a CMOS 180 nm process,the BGR occu-pies 0.548 mm²and operates at 2.5 V with 84μA current draw from a 5 V supply.An average TC of 2.69 ppm/℃ with two-point trimming and 0.81 ppm/℃ with multi-point trimming are achieved over the temperature range of-40 to 125℃.It accommo-dates a load current of 1 mA and an LS of 42 ppm/V,making it suitable for precise BMS applications.
基金supported by the National Natural Science Foundation of China(22379021 and 22479021)。
文摘As batteries become increasingly essential for energy storage technologies,battery prognosis,and diagnosis remain central to ensure reliable operation and effective management,as well as to aid the in-depth investigation of degradation mechanisms.However,dynamic operating conditions,cell-to-cell inconsistencies,and limited availability of labeled data have posed significant challenges to accurate and robust prognosis and diagnosis.Herein,we introduce a time-series-decomposition-based ensembled lightweight learning model(TELL-Me),which employs a synergistic dual-module framework to facilitate accurate and reliable forecasting.The feature module formulates features with physical implications and sheds light on battery aging mechanisms,while the gradient module monitors capacity degradation rates and captures aging trend.TELL-Me achieves high accuracy in end-of-life prediction using minimal historical data from a single battery without requiring offline training dataset,and demonstrates impressive generality and robustness across various operating conditions and battery types.Additionally,by correlating feature contributions with degradation mechanisms across different datasets,TELL-Me is endowed with the diagnostic ability that not only enhances prediction reliability but also provides critical insights into the design and optimization of next-generation batteries.
基金supported by the National Natural Science Foundation of China(52404259)supported by Youth Innovation Promotion Association CAS(Y201768)。
文摘The future large-scale application of sodium-ion batteries(SIBs)is inseparable from their excellent electrochemical performance and reliable safety characteristics.At present,there are few studies focusing on their safety performance.The analysis of thermal stability and structural changes within a single material cannot systematically describe the complex interplay of components within the battery system during the thermal runaway process.Furthermore,the reaction between the battery materials themselves and their counterparts within the system can stimulate more intense exothermic behavior,thereby affecting the safety of the entire battery system.Therefore,this study delved into the thermal generation and gas evolution characteristics of the positive electrode(Na_(x)Ni_(1/3)Fe_(1/3)Mn_(1/3)O_(2),NFM111)and the negative electrode(hard carbon,HC)in SIBs,utilizing various material combinations.Through the integration of microscopic and macroscopic characterization techniques,the underlying reaction mechanisms of the positive and negative electrode materials within the battery during the heating process were elucidated.Three important results are derived from this study:(Ⅰ)The instability of the solid electrolyte interphase(SEI)leads to its decomposition at temperatures below 100℃,followed by extensive decomposition within the range of 100-150℃,yielding heat and the formation of inorganic compounds,such as Na_(2)CO_(3)and Na_(2)O;(Ⅱ)The reaction between NFM111 and the electrolyte constitutes the primary exothermic event during thermal abuse,with a discernible reaction also occurring between sodium metal and the electrolyte throughout the heating process;(Ⅲ)The heat production and gas generation behaviors of multi-component reactions do not exhibit complete correlation,and the occurrence of gas production does not necessarily coincide with thermal behavior.The results presented in this study can provide useful guidance for the safety improvement of SIBs.
