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.

Molecularly Imprinted Micro-Solid-Phase Extraction for the Selective Determination of Phenolic Compounds in Environmental Water Samples with High Performance Liquid Chromatraphy

2,4,6-trichlorophenol molecularly imprinted suspension polymer has been prepared and applied to the molecularly imprinted micro-solid-phase extraction procedure for selective preconcentration of phenolic compounds from environmental water samples. The influence of functional monomer, cross-linker, polymerization condition, porogen, and the ratio of template molecule and functional monomer to cross-linker on the size of the obtained particles were investigated. It was found that methyacrylic acid as functional monomer, divinylbenzene as cross-linker, the molar ratio of template molecule and functional monomer to cross-linker was 1:4:20, the amount of AIBN was 100 mg, ultraviolet radiation at 365 nm were the optimal conditions, and at these conditions, the polymers had the best adsorption efficiency and had the monodispersity of 2 - 3 μm microsphere particles. The characteristics of the MIMSPE method were valid by high performance liquid chromatography. This MIMSPE-HPLC method has been successfully applied to the direct preconcentration and determination of phenolic compounds (phenol, 4-chlorophenol, 2,4-dichlorophenol, 2,4,6-trichlorophenol, pentachlorophenol) in environmental water samples.

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.

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.

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.

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.

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.