Lithium-Ion Battery Pack Based on Fuzzy Logic Control Research on Multi-Layer Equilibrium Circuits

In order to solve the problem of inconsistent energy in the charging and discharging cycles of lithium-ion battery packs,a new multilayer equilibrium topology is designed in this paper.The structure adopts a hierarchical structure design,which includes intra-group equilibrium,primary inter-group equilibrium and secondary inter-group equilibrium.This structure greatly increases the number of equilibrium paths for lithium-ion batteries,thus shortening the time required for equilibrium,and improving the overall efficiency.In terms of control strategy,fuzzy logic control(FLC)is chosen to control the size of the equilibrium current during the equilibrium process.We performed rigorous modeling and simulation of the proposed system by MATLAB and Simulink software.Experiments show that the multilayer equilibrium circuit structure greatly exceeds the traditional single-layer equilibrium circuit in terms of efficacy,specifically,the Li-ion battery equilibrium speed is improved by 12.71%in static equilibrium,14.48%in charge equilibrium,and 11.19%in discharge equilibrium.In addition,compared with the maximum value algorithm,the use of the FLC algorithm reduces the equalization time by about 3.27%and improves the energy transfer efficiency by about 66.49%under the stationary condition,which verifies the feasibility of the equalization scheme.

Deep Learning Hybrid Model for Lithium-Ion Battery Aging Estimation and Prediction

The degradation process of lithium-ion batteries is intricately linked to their entire lifecycle as power sources and energy storage devices,encompassing aspects such as performance delivery and cycling utilization.Consequently,the accurate and expedient estimation or prediction of the aging state of lithium-ion batteries has garnered extensive attention.Nonetheless,prevailing research predominantly concentrates on either aging estimation or prediction,neglecting the dynamic fusion of both facets.This paper proposes a hybrid model for capacity aging estimation and prediction based on deep learning,wherein salient features highly pertinent to aging are extracted from charge and discharge relaxation processes.By amalgamating historical capacity decay data,the model dynamically furnishes estimations of the present capacity and forecasts of future capacity for lithium-ion batteries.Our approach is validated against a novel dataset involving charge and discharge cycles at varying rates.Specifically,under a charging condition of 0.25 C,a mean absolute percentage error(MAPE)of 0.29%is achieved.This outcome underscores the model's adeptness in harnessing relaxation processes commonly encountered in the real world and synergizing with historical capacity records within battery management systems(BMS),thereby affording estimations and prognostications of capacity decline with heightened precision.

Thermal safety boundary of lithium-ion battery at different state of charge

Thermal runaway(TR)is a critical issue hindering the large-scale application of lithium-ion batteries(LIBs).Understanding the thermal safety behavior of LIBs at the cell and module level under different state of charges(SOCs)has significant implications for reinforcing the thermal safety design of the lithium-ion battery module.This study first investigates the thermal safety boundary(TSB)correspondence at the cells and modules level under the guidance of a newly proposed concept,safe electric quantity boundary(SEQB).A reasonable thermal runaway propagation(TRP)judgment indicator,peak heat transfer power(PHTP),is proposed to predict whether TRP occurs.Moreover,a validated 3D model is used to quantitatively clarify the TSB at different SOCs from the perspective of PHTP,TR trigger temperature,SOC,and the full cycle life.Besides,three different TRP transfer modes are discovered.The interconversion relationship of three different TRP modes is investigated from the perspective of PHTP.This paper explores the TSB of LIBs under different SOCs at both cell and module levels for the first time,which has great significance in guiding the thermal safety design of battery systems.

Mechanisms for the evolution of cell-to-cell variations and their impacts on fast-charging performance within a lithium-ion battery pack

Cell-to-cell variations(CtCV) compromise the electrochemical performance of battery packs, yet the evolutional mechanism and quantitative impacts of CtCV on the pack's fast-charging performance remain unexplored. This knowledge gap is vital for the proliferation of electric vehicles. This study underlies the relationship between CtCV and charging performance by assessing the pack's charge speed, final electric quantity, and temperature consistency. Cell variations and pack status are depicted using 2D parameter diagrams, and an m PnS configured pack model is built upon a decomposed electrode cell model.Variations in three single electric parameters, i.e., capacity(Q), electric quantity(E), and internal resistance(R), and their dual interactions, i.e., E-Q and R-Q, are analyzed carefully. The results indicate that Q variations predominantly affect the final electric quantity of the pack, while R variations impact the charge speed most. With incremental variances in cell parameters, the pack's fast-charging capability first declines linearly and then deteriorates sharply as variations intensify. This research elucidates the correlations between pack charging capabilities and cell variations, providing essential insights for optimizing cell sorting and assembly, battery management design, and charging protocol development for battery packs.

Constructing Al@C-Sn pellet anode without passivation layer for lithium-ion battery

Al is considered as a promising lithium-ion battery(LIBs)anode materials owing to its high theoretical capacity and appropri-ate lithation/de-lithation potential.Unfortunately,its inevitable volume expansion causes the electrode structure instability,leading to poor cyclic stability.What’s worse,the natural Al2O3 layer on commercial Al pellets is always existed as a robust insulating barrier for elec-trons,which brings the voltage dip and results in low reversible capacity.Herein,this work synthesized core-shell Al@C-Sn pellets for LIBs by a plus-minus strategy.In this proposal,the natural Al2O3 passivation layer is eliminated when annealing the pre-introduced SnCl2,meanwhile,polydopamine-derived carbon is introduced as dual functional shell to liberate the fresh Al core from re-oxidization and alle-viate the volume swellings.Benefiting from the addition of C-Sn shell and the elimination of the Al2O3 passivation layer,the as-prepared Al@C-Sn pellet electrode exhibits little voltage dip and delivers a reversible capacity of 1018.7 mAh·g^(-1) at 0.1 A·g^(-1) and 295.0 mAh·g^(-1) at 2.0 A·g^(-1)(after 1000 cycles),respectively.Moreover,its diffusion-controlled capacity is muchly improved compared to those of its counterparts,confirming the well-designed nanostructure contributes to the rapid Li-ion diffusion and further enhances the lithium storage activity.

Thermal runaway evolution of a 280 Ah lithium-ion battery with LiFePO_(4) as the cathode for different heat transfer modes constructed by mechanical abuse

Lithium iron phosphate batteries have been increasingly utilized in recent years because their higher safety performance can improve the increasing trend of recurring thermal runaway accidents.However,the safety performance and mechanism of high-capacity lithium iron phosphate batteries under internal short-circuit challenges remain to be explored.This work analyzes the thermal runaway evolution of high-capacity LiFePO_(4) batteries under different internal heat transfer modes,which are controlled by different penetration modes.Two penetration cases involving complete penetration and incomplete penetration were detected during the test,and two modes were performed incorporating nails that either remained or were removed after penetration to comprehensively reveal the thermal runaway mechanism.A theoretical model of microcircuits and internal heat conduction is also established.The results indicated three thermal runaway evolution processes for high-capacity batteries,which corresponded to the experimental results of thermal equilibrium,single thermal runaway,and two thermal runaway events.The difference in heat distribution in the three phenomena is determined based on the microstructure and material structure near the pinhole.By controlling the heat dissipation conditions,the time interval between two thermal runaway events can be delayed from 558 to 1417 s,accompanied by a decrease in the concentration of in-situ gas production during the second thermal runaway event.

Abnormal State Detection in Lithium-ion Battery Using Dynamic Frequency Memory and Correlation Attention LSTM Autoencoder

This paper addresses the challenge of identifying abnormal states in Lithium-ion Battery(LiB)time series data.As the energy sector increasingly focuses on integrating distributed energy resources,Virtual Power Plants(VPP)have become a vital new framework for energy management.LiBs are key in this context,owing to their high-efficiency energy storage capabilities essential for VPP operations.However,LiBs are prone to various abnormal states like overcharging,over-discharging,and internal short circuits,which impede power transmission efficiency.Traditional methods for detecting such abnormalities in LiB are too broad and lack precision for the dynamic and irregular nature of LiB data.In response,we introduce an innovative method:a Long Short-Term Memory(LSTM)autoencoder based on Dynamic Frequency Memory and Correlation Attention(DFMCA-LSTM-AE).This unsupervised,end-to-end approach is specifically designed for dynamically monitoring abnormal states in LiB data.The method starts with a Dynamic Frequency Fourier Transform module,which dynamically captures the frequency characteristics of time series data across three scales,incorporating a memory mechanism to reduce overgeneralization of abnormal frequencies.This is followed by integrating LSTM into both the encoder and decoder,enabling the model to effectively encode and decode the temporal relationships in the time series.Empirical tests on a real-world LiB dataset demonstrate that DFMCA-LSTM-AE outperforms existing models,achieving an average Area Under the Curve(AUC)of 90.73%and an F1 score of 83.83%.These results mark significant improvements over existing models,ranging from 2.4%–45.3%for AUC and 1.6%–28.9%for F1 score,showcasing the model’s enhanced accuracy and reliability in detecting abnormal states in LiB data.

Aging Characteristics of Lithium-Ion Battery Under Fast Charging Based on Electrochemical-thermalmechanical Coupling Model

The aging characteristics of lithium-ion battery(LIB)under fast charging is investigated based on an electrochemical-thermal-mechanical(ETM)coupling model.Firstly,the ETM coupling model is established by COMSOL Multiphysics.Subsequently,a long cycle test was conducted to explore the aging characteristics of LIB.Specifically,the effects of charging(C)rate and cycle number on battery aging are analyzed in terms of nonuniform distribution of solid electrolyte interface(SEI),SEI formation,thermal stability and stress characteristics.The results indicate that the increases in C rate and cycling led to an increase in the degree of nonuniform distribution of SEI,and thus a consequent increase in the capacity loss due to the SEI formation.Meanwhile,the increases in C rate and cycle number also led to an increase in the heat generation and a decrease in the heat dissipation rate of the battery,respectively,which result in a decrease in the thermal stability of the electrode materials.In addition,the von Mises stress of the positive electrode material is higher than that of the negative electrode material as the cycling proceeds,with the positive electrode material exhibiting tensile deformation and the negative electrode material exhibiting compressive deformation.The available lithium ion concentration of the positive electrode is lower than that of the negative electrode,proving that the tensile-type fracture occurring in the positive material under long cycling dominated the capacity loss process.The aforementioned studies are helpful for researchers to further explore the aging behavior of LIB under fast charging and take corresponding preventive measures.

Solvent-Free Manufacturing of Lithium-Ion Battery Electrodes via Cold Plasma

Slurry casting has been used to fabricate lithium-ion battery electrodes for decades,which involves toxic and expensive organic solvents followed by high-cost vacuum drying and electrode calendering.This work presents a new manufacturing method using a nonthermal plasma to create inter-particle binding without using any polymeric binding materials,enabling solvent-free manufacturing electrodes with any electrochemistry of choice.The cold-plasma-coating technique enables fabricating electrodes with thickness(>200 pm),high mass loading(>30 mg cm^(-2)),high peel strength,and the ability to print lithium-ion batteries in an arbitrary geometry.This crosscutting,chemistry agnostic,platform technology would increase energy density,eliminate the use of solvents,vacuum drying,and calendering processes during production,and reduce manufacturing cost for current and future cell designs.Here,lithium iron phosphate and lithium cobalt oxide were used as examples to demonstrate the efficacy of the cold-plasma-coating technique.It is found that the mechanical peel strength of cold-plasma-coating-manufactured lithium iron phosphate is over an order of magnitude higher than that of slurry-casted lithium iron phosphate electrodes.Full cells assembled with a graphite anode and the cold-plasma-coating-lithium iron phosphate cathode offer highly reversible cycling performance with a capacity retention of 81.6%over 500 cycles.For the highly conductive cathode material lithium cobalt oxide,an areal capacity of 4.2 mAh cm^(-2)at 0.2 C is attained.We anticipate that this new,highly scalable manufacturing technique will redefine global lithium-ion battery manufacturing providing significantly reduced plant footprints and material costs.

Review on Lithium-ion Battery PHM from the Perspective of Key PHM Steps

Prognostics and health management(PHM)has gotten considerable attention in the background of Industry 4.0.Battery PHM contributes to the reliable and safe operation of electric devices.Nevertheless,relevant reviews are still continuously updated over time.In this paper,we browsed extensive literature related to battery PHM from 2018to 2023 and summarized advances in battery PHM field,including battery testing and public datasets,fault diagnosis and prediction methods,health status estimation and health management methods.The last topic includes state of health estimation methods,remaining useful life prediction methods and predictive maintenance methods.Each of these categories is introduced and discussed in details.Based on this survey,we accordingly discuss challenges left to battery PHM,and provide future research opportunities.This research systematically reviews recent research about battery PHM from the perspective of key PHM steps and provide some valuable prospects for researchers and practitioners.

Preparation of lithium-ion battery anode materials from graphitized spent carbon cathode derived from aluminum electrolysis

Graphitized spent carbon cathode(SCC)is a hazardous solid waste generated in the aluminum electrolysis process.In this study,a flotation-acid leaching process is proposed for the purification of graphitized SCC,and the use of the purified SCC as an anode material for lithium-ion batteries is explored.The flotation and acid leaching processes were separately optimized through one-way experiments.The maximum SCC carbon content(93wt%)was achieved at a 90%proportion of−200-mesh flotation particle size,a slurry concentration of 10wt%,a rotation speed of 1600 r/min,and an inflatable capacity of 0.2 m^(3)/h(referred to as FSCC).In the subsequent acid leaching process,the SCC carbon content reached 99.58wt%at a leaching concentration of 5 mol/L,a leaching time of 100 min,a leaching temperature of 85°C,and an HCl/FSCC volume ratio of 5:1.The purified graphitized SCC(referred to as FSCC-CL)was utilized as an anode material,and it exhibited an initial capacity of 348.2 mAh/g at 0.1 C and a reversible capacity of 347.8 mAh/g after 100 cycles.Moreover,compared with commercial graphite,FSCC-CL exhibited better reversibility and cycle stability.Thus,purified SCC is an important candidate for anode material,and the flotation-acid leaching purification method is suitable for the resourceful recycling of SCC.

Accuracy comparison and improvement for state of health estimation of lithium-ion battery based on random partial recharges and feature engineering

State of health(SOH)estimation of e-mobilities operated in real and dynamic conditions is essential and challenging.Most of existing estimations are based on a fixed constant current charging and discharging aging profiles,which overlooked the fact that the charging and discharging profiles are random and not complete in real application.This work investigates the influence of feature engineering on the accuracy of different machine learning(ML)-based SOH estimations acting on different recharging sub-profiles where a realistic battery mission profile is considered.Fifteen features were extracted from the battery partial recharging profiles,considering different factors such as starting voltage values,charge amount,and charging sliding windows.Then,features were selected based on a feature selection pipeline consisting of filtering and supervised ML-based subset selection.Multiple linear regression(MLR),Gaussian process regression(GPR),and support vector regression(SVR)were applied to estimate SOH,and root mean square error(RMSE)was used to evaluate and compare the estimation performance.The results showed that the feature selection pipeline can improve SOH estimation accuracy by 55.05%,2.57%,and 2.82%for MLR,GPR and SVR respectively.It was demonstrated that the estimation based on partial charging profiles with lower starting voltage,large charge,and large sliding window size is more likely to achieve higher accuracy.This work hopes to give some insights into the supervised ML-based feature engineering acting on random partial recharges on SOH estimation performance and tries to fill the gap of effective SOH estimation between theoretical study and real dynamic application.

A Joint Estimation Method of SOC and SOH for Lithium-ion Battery Considering Cyber-Attacks Based on GA-BP

To improve the estimation accuracy of state of charge(SOC)and state of health(SOH)for lithium-ion batteries,in this paper,a joint estimation method of SOC and SOH at charging cut-off voltage based on genetic algorithm(GA)combined with back propagation(BP)neural network is proposed,the research addresses the issue of data manipulation resulting fromcyber-attacks.Firstly,anomalous data stemming fromcyber-attacks are identified and eliminated using the isolated forest algorithm,followed by data restoration.Secondly,the incremental capacity(IC)curve is derived fromthe restored data using theKalman filtering algorithm,with the peak of the ICcurve(ICP)and its corresponding voltage serving as the health factor(HF).Thirdly,the GA-BP neural network is applied to map the relationship between HF,constant current charging time,and SOH,facilitating the estimation of SOH based on HF.Finally,SOC estimation at the charging cut-off voltage is calculated by inputting the SOH estimation value into the trained model to determine the constant current charging time,and by updating the maximum available capacity.Experiments show that the root mean squared error of the joint estimation results does not exceed 1%,which proves that the proposed method can estimate the SOC and SOH accurately and stably even in the presence of false data injection attacks.

Analysis of Differences in Electrochemical Performance Between Coin and Pouch Cells for Lithium-Ion Battery Applications

Small coin cell batteries are predominantly used for testing lithium-ion batteries(LIBs)in academia because they require small amounts of material and are easy to assemble.However,insufficient attention is given to difference in cell performance that arises from the differences in format between coin cells used by academic researchers and pouch or cylindrical cells which are used in industry.In this article,we compare coin cells and pouch cells of different size with exactly the same electrode materials,electrolyte,and electrochemical conditions.We show the battery impedance changes substantially depending on the cell format using techniques including Electrochemical Impedance Spectroscopy(EIS)and Galvanostatic Intermittent Titration Technique(GITT).Using full cell NCA-graphite LIBs,we demonstrate that this difference in impedance has important knock-on effects on the battery rate performance due to ohmic polarization and the battery life time due to Li metal plating on the anode.We hope this work will help researchers getting a better idea of how small coin cell formats impact the cell performance and help predicting improvements that can be achieved by implementing larger cell formats.

Preparation and electrochemical performance of tantalum-doped lithium titanate as anode material for lithium-ion battery

The electrochemical performance of Ta-doped Li4Ti5O12 in the form of Li4Ti4.95Ta0.05O12 was characterized.X-ray diffraction（XRD） and scanning electron microscopy（SEM） were employed to characterize the structure and morphology of Li4Ti4.95Ta0.05O12.Ta-doping does not change the phase composition and particle morphology,while improves remarkably its cycling stability at high charge/discharge rate.Li4Ti4.95Ta0.05O12 exhibits an excellent rate capability with a reversible capacity of 116.1 mA·h/g at 10C and even 91.0 mA·h/g at 30C.The substitution of Ta for Ti site can enhance the electronic conductivity of Li4Ti5O12 via the generation of mixing Ti4＋/Ti3＋,which indicates that Li4Ti4.95Ta0.05O12 is a promising candidate material for anodes in lithium-ion battery application.

Explainable Neural Network for Sensitivity Analysis of Lithium-ion Battery Smart Production

Battery production is crucial for determining the quality of electrode,which in turn affects the manufactured battery performance.As battery production is complicated with strongly coupled intermediate and control parameters,an efficient solution that can perform a reliable sensitivity analysis of the production terms of interest and forecast key battery properties in the early production phase is urgently required.This paper performs detailed sensitivity analysis of key production terms on determining the properties of manufactured battery electrode via advanced data-driven modelling.To be specific,an explainable neural network named generalized additive model with structured interaction(GAM-SI)is designed to predict two key battery properties,including electrode mass loading and porosity,while the effects of four early production terms on manufactured batteries are explained and analysed.The experimental results reveal that the proposed method is able to accurately predict battery electrode properties in the mixing and coating stages.In addition,the importance ratio ranking,global interpretation and local interpretation of both the main effects and pairwise interactions can be effectively visualized by the designed neural network.Due to the merits of interpretability,the proposed GAM-SI can help engineers gain important insights for understanding complicated production behavior,further benefitting smart battery production.

Pioneering the direct large-scale laser printing of flexible“graphenic silicon”self-standing thin films as ultrahigh-performance lithium-ion battery anodes

Recent technological advancements,such as portable electronics and electric vehicles,have created a pressing need for more efficient energy storage solutions.Lithium-ion batteries(LIBs)have been the preferred choice for these applications,with graphite being the standard anode material due to its stability.However,graphite falls short of meeting the growing demand for higher energy density,possessing a theoretical capacity that lags behind.To address this,researchers are actively seeking alternative materials to replace graphite in commercial batteries.One promising avenue involves lithiumalloying materials like silicon and phosphorus,which offer high theoretical capacities.Carbon-silicon composites have emerged as a viable option,showing improved capacity and performance over traditional graphite or pure silicon anodes.Yet,the existing methods for synthesizing these composites remain complex,energy-intensive,and costly,preventing widespread adoption.A groundbreaking approach is presented here:the use of a laser writing strategy to rapidly transform common organic carbon precursors and silicon blends into efficient“graphenic silicon”composite thin films.These films exhibit exceptional structural and energy storage properties.The resulting three-dimensional porous composite anodes showcase impressive attributes,including ultrahigh silicon content,remarkable cyclic stability(over 4500 cycles with∼40%retention),rapid charging rates(up to 10 A g^(-1)),substantial areal capacity(>5.1 mAh cm^(-2)),and excellent gravimetric capacity(>2400 mAh g^(-1) at 0.2 A g^(-1)).This strategy marks a significant step toward the scalable production of high-performance LIB materials.Leveraging widely available,cost-effective precursors,the laser-printed“graphenic silicon”composites demonstrate unparalleled performance,potentially streamlining anode production while maintaining exceptional capabilities.This innovation not only paves the way for advanced LIBs but also sets a precedent for transforming various materials into high-performing electrodes,promising reduced complexity and cost in battery production.

Electric-controlled pressure relief valve for enhanced safety in liquid-cooled lithium-ion battery packs

The liquid-cooled battery energy sto rage system(LCBESS) has gained significant attention due to its superior thermal management capacity.However,liquid-cooled battery pack(LCBP) usually has a high sealing level above IP65,which can trap flammable and explosive gases from battery thermal runaway and cause explosions.This poses serious safety risks and challenges for LCBESS.In this study,we tested overcharged battery inside a commercial LCBP and found that the conventionally mechanical pressure relief valve(PRV) on the LCBP had a delayed response and low-pressure relief efficiency.A realistic 20-foot model of an energy storage cabin was constructed using the Flacs finite element simulation software.Comparative studies were conducted to evaluate the pressure relief efficiency and the influence on neighboring battery packs in case of internal explosions,considering different sizes and installation positions of the PRV.Here,a newly developed electric-controlled PRV integrated with battery fault detection is introduced,capable of starting within 50 ms of the battery safety valve opening.Furthermore,the PRV was integrated with the battery management system and changed the battery charging and discharging strategy after the PRV was opened.Experimental tests confirmed the efficacy of this method in preventing explosions.This paper addresses the safety concerns associated with LCBPs and proposes an effective solution for explosion relief.

A New Equalization Method for Lithium-Ion Battery Packs Based on CUK

Aiming at the traditional CUK equalizer can only perform energy equalization between adjacent batteries,if the two single batteries that need to be equalized are far away from each other,there will be the problem of longer energy transmission path and lower equalization efficiency,this paper optimizes the CUK equalizer and optimizes its peripheral selection circuit,which can support the equalization of single batteries at any two positions.The control strategy adopts the open-circuit voltage(OVC)of the battery and the state of charge(SOC)of the battery as the equalization variables,and selects the corresponding equalization variables according to the energy conditions of the two batteries that need to be equalized,and generates the adaptive equalization current with an adaptive PID controller in order to improve the equalization efficiency.Simulation modeling is performed in Matlab/Simulink 2021b,and the experimental results show that the optimized CUK equalizer in this paper improves the equalization time by 25.58%compared with the traditional CUK equalizer.In addition,compared with the mean value difference(MVD)method,the adaptive PID method reduces the equalization time by about 30%in the static and charging and discharging experimental environments,which verifies the superiority of this equalization scheme.

Synthesis and characterization of ε-VOPO_4 nanosheets for secondary lithium-ion battery cathode

Vanadium （III） phosphate monoclinic VPO4·H2O was synthesized hydrothermally. The ε-VOPO4 nanosheets, formed by the oxidative de-intercalation of protons from monoclinic VPO4·H2O, can reversibly react with more than 1 mol lithium atoms in two steps. Crystal XRD analysis revealed that the structure of the ε-VOPO4 nanosheets is monoclinic with lattice parameters of α=7.2588（4） A, b=6.8633（2） A and c=7.2667（4） A. The results show that the ε-VOPO4 nanosheets have a thickness of 200 nm and uniform crystallinity. Electrochemical characterization of the ε-VOPO4 monoclinic nanosheets reveals that they have good electrochemical properties at high current density, and deliver high initial capacity of 230.3 mA· h/g at a current density of 0.09 mA/cm2. Following the first charge cycle, reversible electrochemical lithium extraction/insertion at current density of 0.6 mA/cm2 affords a capacity retention rate of 73.6% （2.0？4.3 V window） that is stable for at least 1000 cycles.