Designer uniform Li plating/stripping through lithium–cobalt alloying hierarchical scaffolds for scalable high-performance lithium-metal anodes

Lithium metal anodes are of great interest for advanced high-energy density batteries such as lithiumair, lithium-sulfur and solid-state batteries, due to their low electrode potential and ultra-high theoretical capacity. There are, however, several challenges limiting their practical applications, which include low coulombic efficiency, the uncontrollable growth of dendrites and poor rate capability. Here, a rational design of 3D structured lithium metal anodes comprising of in-situ growth of cobalt-decorated nitrogen-doped carbon nanotubes on continuous carbon nanofibers is demonstrated via electrospinning.The porous and free-standing scaffold can enhance the tolerance to stresses resulting from the intrinsic volume change during Li plating/stripping, delivering a significant boost in both charge/discharge rates and stable cycling performance. A binary Co-Li alloying phase was generated at the initial discharge process, creating more active sites for the Li nucleation and uniform deposition. Characterization and density functional theory calculations show that the conductive and uniformly distributed cobalt-decorated carbon nanotubes with hierarchical structure can effectively reduce the local current density and more easily absorb Li atoms, leading to more uniform Li nucleation during plating. The current work presents an advance on scalable and cost-effective strategies for novel electrode materials with 3D hierarchical microstructures and mechanical flexibility for lithium metal anodes.

End-cloud collaboration method enables accurate state of health and remaining useful life online estimation in lithium-ion batteries

Though the lithium-ion battery is universally applied,the reliability of lithium-ion batteries remains a challenge due to various physicochemical reactions,electrode material degradation,and even thermal runaway.Accurate estimation and prediction of battery health conditions are crucial for battery safety management.In this paper,an end-cloud collaboration method is proposed to approach the track of battery degradation process,integrating end-side empirical model with cloud-side data-driven model.Based on ensemble learning methods,the data-driven model is constructed by three base models to obtain cloud-side highly accurate results.The double exponential decay model is utilized as an empirical model to output highly real-time prediction results.With Kalman filter,the prediction results of end-side empirical model can be periodically updated by highly accurate results of cloud-side data-driven model to obtain highly accurate and real-time results.Subsequently,the whole framework can give an accurate prediction and tracking of battery degradation,with the mean absolute error maintained below 2%.And the execution time on the end side can reach 261μs.The proposed end-cloud collaboration method has the potential to approach highly accurate and highly real-time estimation for battery health conditions during battery full life cycle in architecture of cyber hierarchy and interactional network.

Enabling dendrite-free charging for lithium batteries based on transport-reaction competition mechanism in CHAIN framework

Worldwide trends in mobile electrification will skyrocket demands for lithium-based battery production,driven by the popularity of electric vehicles.However,both lithium metal batteries and lithium ion batteries face severe safety issues due to dendrite nucleation and growth process.Li deposition is significantly influenced by interfacial factors and charging conditions.In this paper,an electrochemical model considering the internal and external factors is proposed based on Monte Carlo method.The influence of internal solid electrolyte interphase(SEI)porosity,thickness and the external conditions on dendrite growth process is systematically described.The simulation results support that the three factors investigated in this model could synergistically regulate the dendrite growth process.Three competition mechanisms are proposed to tailor lithium deposition for Li-based batteries and numerical solutions for variation pattern of dendrite growth with time are fitted.A three-step process describing kinetic process of lithium deposition is proposed.To achieve dendrite-free charging process,charging strategies and emerging materials design should be considered,including physicochemical materials engineering,artificial SEI,and design for dynamic safety boundary.This work could contribute to the foundation for insights of Li deposition mechanism,which is promising to provide guidelines for next-generation high-energy-density and safe batteries in CHAIN framework.

Highly Aligned Ultra-Thick Gel-Based Cathodes Unlocking Ultra-High Energy Density Batteries

Increasing electrode thickness can substantially enhance the specific energy of lithium-ion batteries;however,ionic transport,electronic conductivity,and ink rheology are current barriers to adoption.Here,a novel approach using a mixed xanthan gum and locust bean gum binder to construct ultrathick electrodes is proposed to address above issues.After combining aqueous binder with single-walled carbon nanotubes(SWCNT),active material(LiNi_(0.8)Co_(0.1)Mn_(0.1)O_(2)) and subsequent vacuum freeze-drying,highly aligned,and low-tortuosity structures with a porosity of ca.50%can be achieved with an average pore size of 10μm,whereby the gum binder-SWCNT-NMC811 forms vertical structures supported by tissue-like binder/SWCNT networks allowing for excellent electronic conducting phase percolation.As a result,ultra-thick electrodes with a mass loading of about 511 mg cm^(−2) and 99.5 wt%active materials have been demonstrated with a remarkable areal capacity of 79.3 mAh cm^(−2),which is the highest value reported so far.This represents a>25×improvement compared with conventional electrodes with an areal capacity of about 3 mAh cm^(−2).This route also can be expanded to other electrode materials,such as LiFePO_(4) and Li_(4)Ti_(5)O_(12),and thus opens the possibility for low-cost and sustainable ultra-thick electrodes with increased specific energy for future lithium-ion batteries.

Atomic/nano-scale in-situ probing the shuttling effect of redox mediator in Na-O_(2) batteries

Sodium-oxygen batteries(Na-O_(2))have attracted extensive attention as promising energy storage systems due to their high energy density and low cost.Redox mediators are often employed to improve Na-O_(2) battery performance,however,their effect on the formation mechanism of the oxygen reduction product(NaO_(2))is still unclear.Here,we have investigated the formation mechanism of NaO_(2) during the discharge process in the presence of a redox mediator with the help of atomic/nano-scale in-situ characterization tools used in concert(e.g.atomic force microscope,electrochemical quartz crystal microbalance(EQCM)and laser nano-particle analyzer).As a result,real-time observations on different time scales show that by shuttling electrons to the electrolyte,the redox mediator enables formation of NaO_(2) in the solution-phase instead of within a finite region near the electrode surface.These findings provide new fundamental insights on the understanding of Na-O_(2) batteries and new consequently perspectives on designing high performance metal-O_(2) batteries and other related functions.

Biological Characteristics of Morel and Analysis of Its Cultivation Adaptability in Tianjin

Biological characteristics of morel,climatic conditions and characteristics of Tianjin,and facilities of greenhouses in Tianjin were introduced firstly,and then the cultivation adaptability of morel in Tianjin was analyzed. This study aims to let farmers learn more about the rare edible fungus——morel and provide relevant theoretical basis and feasibility reference for the promotion and application of morel cultivation in Tianjin.

微型学习:成人教育的新途径

随着我国义务教育的广泛实行及时间加长,我国公民的整体教育水平持续升高。成人教育的提出和发展也有助于我国公民整体综合素质的提高,成人教育也受到社会和人们的高度关注,成为我国教育事业密不可分的一部分。在所有成人教育中,微型学习以其使用方便、操作便捷等优点发挥其强大的优势。通过对微型学习的内涵进行了解,对我国成人教育的发展现状进行分析,发现微型学习对于成人教育的重要性,并且针对如何更好使用微型学习提出合理的建议,为我国成人教育事业工作者提供一些参考,以便更好的服务于成人教育。

KLF4 facilitates chromatin accessibility remodeling in porcine early embryos

Chromatin accessibility remodeling driven by pioneer factors is critical for the development of early embryos.Current studies have illustrated several pioneer factors as being important for agricultural animals,but what are the pioneer factors and how the pioneer factors remodel the chromatin accessibility in porcine early embryos is not clear.By employing low-input DNase-seq(liDNase-seq),we profiled the landscapes of chromatin accessibility in porcine early embryos and uncovered a unique chromatin accessibility reprogramming pattern during porcine preimplantation development.Our data revealed that KLF4 played critical roles in remodeling chromatin accessibility in porcine early embryos.Knocking down of KLF4 led to the reduction of chromatin accessibility in early embryos,whereas KLF4 overexpression promoted the chromatin openness in porcine blastocysts.Furthermore,KLF4 deficiency resulted in mitochondrial dysfunction and developmental failure of porcine embryos.In addition,we found that overexpression of KLF4 in blastocysts promoted lipid droplet accumulation,whereas knockdown of KLF4 disrupted this process.Taken together,our study revealed the chromatin accessibility dynamics and identified KLF4 as a key regulator in chromatin accessibility and cellular metabolism during porcine preimplantation embryo development.

CHAINS:CHAIN-based fusion safety system framework for intelligent connected vehicle

Intelligent connected vehicles,as the focus of the global automotive industry,are currently at a critical stage of large-scale commercialization.However,during the development process of vehicles from mechanical systems with limited functions to mobile intelligence with complex and multiple functions,the issues of functional safety,cybersecurity,and safety of the intended functionality are the main challenges of the industrialization of intelligent connected vehicles,including multiple safety risks such as hardware and software failures,insufficient performance in edge scenarios,cyber-attacks and data leakage.In this paper,the safety and security issues of intelligent connected vehicles,the challenges posed by emerging technology applications,and related solutions are systematically reviewed and summarized.A fusion safety system framework with the safety cube as the core of protection and control is proposed innovatively based on a field-vehicle-human safety interactional model,realizing stereoscopic,deep,and comprehensive safety protection through end-cloud collaboration.Meanwhile,an X-shaped fusion safety development process based on CHAIN is proposed.Through the empowerment of digital twin and AI technologies,it could approach interaction between physical entities and digital twin models and the automation of the development process,thereby satisfying the demands of fusion safety system design,intelligent development,rapid delivery,and continuous iteration.The fusion safety system framework and X-shaped development process proposed in this paper can provide important insight into intelligent transportation vehicles and systems'safety and security design and development.

Recent development and progress of structural energy devices

In order to fully replace the traditional fossil energy supply system, the efficiency of electrochemical energy conversion and storage of new energy technology needs to be continuously improved to enhance its market competitiveness. The structural design of energy devices can achieve satisfactory energy conversion and storage performance. To achieve lightweight design, improve mechanical support, enhance electrochemical performance, and adapt to the special shape of the device, the structural energy devices develop very quickly. To help researchers analyze the development and get clear on developing trend,this review is prepared. This review summarizes the latest developments in structural energy devices, including special attention to fuel cells, lithium-ion batteries, lithium metal batteries, and supercapacitors.Finally, the existing problems of structural energy devices are discussed, and the current challenges and future opportunities are summarized and prospected. Structural energy devices can undoubtedly overcome the performance bottlenecks of traditional energy devices, break the limitations of existing materials and structures, and provide a guidance for the development of equipment with high performance,light weight and low cost in the future.

Review of Abnormality Detection and Fault Diagnosis Methods for Lithium‑Ion Batteries

Electric vehicles are developing prosperously in recent years.Lithium-ion batteries have become the dominant energy storage device in electric vehicle application because of its advantages such as high power density and long cycle life.To ensure safe and efficient battery operations and to enable timely battery system maintenance,accurate and reliable detection and diagnosis of battery faults are necessitated.In this paper,the state-of-the-art battery fault diagnosis methods are comprehensively reviewed.First,the degradation and fault mechanisms are analyzed and common abnormal behaviors are summarized.Then,the fault diagnosis methods are categorized into the statistical analysis-,model-,signal processing-,and data-driven methods.Their distinctive characteristics and applications are summarized and compared.Finally,the challenges facing the existing fault diagnosis methods are discussed and the future research directions are pointed out.

Perspective on solid‐electrolyte interphase regulation for lithium metal batteries

The solid‐electrolyte interphase(SEI)generated between the electrode and the electrolyte strongly influences the performance of batteries.As the most at-tractive next‐generation energy storage system with ultrahigh energy density,the development of lithium metal batteries(LMBs)has been greatly plagued by the uncontrollable lithium(Li)dendrite and serious electrolyte decom-position resulting from the self‐derived unstable SEI with poor properties.In this perspective,the recent progress of regulating the nature and composition of the SEI to stabilize the Li metal in LMBs is summarized,followed by a discussion of the formation mechanism and the property of the SEI.The strategies for constructing a stable SEI are summarized,for example,design of a compatible electrolyte with the anode,adding self‐sacrificing additives or solvation control additives,and the regulation of nonfaradaic electric ad-sorption and desorption progress.Finally,the guideline for the rational design of the SEI is proposed.

Implementation for a cloud battery management system based on the CHAIN framework

An intelligent battery management system is a crucial enabler for energy storage systems with high power output,increased safety and long lifetimes.With recent developments in cloud computing and the proliferation of big data,machine learning approaches have begun to deliver invaluable insights,which drives adaptive control of battery management systems(BMS)with improved performance.In this paper,a general framework utilizing an end-edge-cloud architecture for a cloud-based BMS is proposed,with the composition and function of each link described.Cloud-based BMS leverages from the Cyber Hierarchy and Interactional Network(CHAIN)framework to provide multi-scale insights,more advanced and efficient algorithms can be used to realize the state-of-X es-timation,thermal management,cell balancing,fault diagnosis and other functions of traditional BMS system.The battery intelligent monitoring and management platform can visually present battery performance,store working-data to help in-depth understanding of the microscopic evolutionary law,and provide support for the development of control strategies.Currently,the cloud-based BMS requires more effects on the multi-scale inte-grated modeling methods and remote upgrading capability of the controller,these two aspects are very important for the precise management and online upgrade of the system.The utility of this approach is highlighted not only for automotive applications,but for any battery energy storage system,providing a holistic framework for future intelligent and connected battery management.

Multi‑scale Battery Modeling Method for Fault Diagnosis

Fault diagnosis is key to enhancing the performance and safety of battery storage systems.However,it is challenging to realize efficient fault diagnosis for lithium-ion batteries because the accuracy diagnostic algorithm is limited and the features of the different faults are similar.The model-based method has been widely used for degradation mechanism analysis,state estimation,and life prediction of lithium-ion battery systems due to the fast speed and high development efficiency.This paper reviews the mainstream modeling approaches used for battery diagnosis.First,a review of the battery’s degradation mechanisms and the external factors affecting the aging rate is presented.Second,the different modeling approaches are summarized,from microscopic to macroscopic scales,including density functional theory,molecular dynamics,X-ray computed tomography technology,electrochemical model,equivalent circuit model,distributed model and neural network algorithm.Subsequently,the advantages and disadvantages of these model approaches are discussed for fault detection and diagnosis of batteries in different application scenarios.Finally,the remaining challenges of model-based battery diagnosis and the future perspective of using cloud control and battery intelligent networking to enhance diagnostic performance are discussed.

An Innovative State-of-charge Estimation Method of Lithium-ion Battery Based on 5th-order Cubature Kalman Filter

The lithium-ion batteries have drawn much attention as the major energy storage system.However,the battery state estimation still suffers from inaccuracy under dynamic operational conditions,with the unstable environmental noise influencing the robustness of estimation.This paper presents a 5th-order cubature Kalman filter with improvements on adaptivity for real-time state-of-charge estimation.The second-order equivalent circuit model is developed for describing the characteristics of battery,and parameter identification is carried out according to particle swarm optimization.The developed method is validated in stable and dynamic conditions,and simulation results show a satisfactory consistency with the experimental results.The maximum estimation error under static conditions is less than 3%and the maximum error under dynamic conditions is 5%.Numerical analysis indicates that the method has better convergence and robustness than the traditional method under the disturbances of initial error,which demonstrates the potential for EV applications in harsh environments.The proposed method shows application potential for both online estimations and cloud-computing system,indicating its diverse application prospect in electric vehicles.

Battery digital twins: Perspectives on the fusion of models, data and artificial intelligence for smart battery management systems

Effective management of lithium-ion batteries is a key enabler for a low carbon future,with applications including electric vehicles and grid scale energy storage.The lifetime of these devices depends greatly on the materials used,the system design and the operating conditions.This complexity has therefore made real-world control of battery systems challenging.However,with the recent advances in understanding battery degradation,modelling tools and diagnostics,there is an opportunity to fuse this knowledge with emerging machine learning techniques towards creating a battery digital twin.In this cyber-physical system,there is a close interaction between a physical and digital embodiment of a battery,which enables smarter control and longer lifetime.This perspectives paper thus presents the state-of-the-art in battery modelling,in-vehicle diagnostic tools,data driven modelling approaches,and how these elements can be combined in a framework for creating a battery digital twin.The challenges,emerging techniques and perspective comments provided here,will enable scientists and engineers from industry and academia with a framework towards more intelligent and interconnected battery management in the future.

Cyber Hierarchy Multiscale Integrated Energy Management of Intelligent Hybrid Electric Vehicles

The full-lifespan management concept provides a new pathway to seeking solutions from macro-application scenarios to micro-mechanism levels.This paper presents a cyber hierarchy multiscale optimal control method for multiple intelligent hybrid vehicles to fully release the potentials of vehicle components while guaranteeing driving safety and stability.It can be generally divided into the cyber intelligent driving system on the cyber-end and the intelligent vehicle system on the vehicle-end.On the cyber-end,the state information of the surrounding vehicles is transmitted via the Vehicle-to-Everything structure and further processed in the cloud platform to generate future driving behaviors based on a car-following theory.On the vehicle-end,an optimized control sequence for vehicle components at micro-levels is derived by incorporating a physics-informed neural network model for battery health prediction.The results show that global optimization needs high coupling between the macro-and micro-physical processes.By introducing the genetic algorithm for time smoothing,the improved driving strategy is capable of macro-and micro-coupling,and thus improves the controllable performance in time series.Moreover,this method spans the complexity of space,time,and chemistry,enhances the interpretation performance of machine learning,and slows down the battery aging in the process of multiscale optimization.

差基的下界

若集合A■Z满足A-A■{1,2,...,n},则A称为关于n的差基.差基应用于图的优美标记、集合的对称相交族和信号处理,与密码和编码理论有密切而深刻的联系.本文利用Fourier分析的方法,讨论一些参数的更高阶的Fourier系数的性质,进一步改进了差基的下界,从而改进了R′edei和Re′nyi(1949)、Leech(1956)以及Bernshteyn和Tait(2019)的结果.

Non-invasive Characteristic Curve Analysis of Lithium-ion Batteries Enabling Degradation Analysis and Data-Driven Model Construction:A Review

Power battery technology is essential to ensuring the overall performance and safety of electric vehicles.Non-invasive char-acteristic curve analysis(CCA)for lithium-ion batteries is of particular importance.CCA can provide characteristic data for further applications such as state estimation and thermal runaway warning without disassembling the batteries.This paper summarizes the characteristic curves consisting of incremental curve analysis,differential voltage analysis,and differential thermal voltammetry from the perspectives of exploring the aging mechanism of batteries and constructing the data-driven model.The process of quantitative analysis of battery aging mechanism is presented and the steps of constructing data-driven models are induced.Moreover,the recent progress and application of the main features and methodologies are discussed.Finally,the applicability of battery CCA is discussed by converting non-quantifiable battery information into transportable data covering macrostate and micro-reaction information.Combined with the cloud-based battery management platform,the above-mentioned battery characteristic curves could be used as a valuable dataset to upgrade the next-generation battery management system design.