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.

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.

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.

Lifetime and Aging Degradation Prognostics for Lithium-ion Battery Packs Based on a Cell to Pack Method

Aging diagnosis of batteries is essential to ensure that the energy storage systems operate within a safe region.This paper proposes a novel cell to pack health and lifetime prognostics method based on the combination of transferred deep learning and Gaussian process regression.General health indicators are extracted from the partial discharge process.The sequential degradation model of the health indicator is developed based on a deep learning framework and is migrated for the battery pack degradation prediction.The future degraded capacities of both battery pack and each battery cell are probabilistically predicted to provide a comprehensive lifetime prognostic.Besides,only a few separate battery cells in the source domain and early data of battery packs in the target domain are needed for model construction.Experimental results show that the lifetime prediction errors are less than 25 cycles for the battery pack,even with only 50 cycles for model fine-tuning,which can save about 90%time for the aging experiment.Thus,it largely reduces the time and labor for battery pack investigation.The predicted capacity trends of the battery cells connected in the battery pack accurately reflect the actual degradation of each battery cell,which can reveal the weakest cell for maintenance in advance.

Thermal Management of Air-Cooling Lithium-Ion Battery Pack

Lithium-ion battery packs are made by many batteries, and the difficulty in heat transfer can cause many safety issues. It is important to evaluate thermal performance of a battery pack in designing process. Here, a multiscale method combining a pseudo-two-dimensional model of individual battery and three-dimensional computational fluid dynamics is employed to describe heat generation and transfer in a battery pack. The effect of battery arrangement on the thermal performance of battery packs is investigated. We discuss the air-cooling effect of the pack with four battery arrangements which include one square arrangement, one stagger arrangement and two trapezoid arrangements. In addition, the air-cooling strategy is studied by observing temperature distribution of the battery pack. It is found that the square arrangement is the structure with the best air-cooling effect, and the cooling effect is best when the cold air inlet is at the top of the battery pack. We hope that this work can provide theoretical guidance for thermal management of lithium-ion battery packs.

Design of Voltage Equalization Circuit and Control Method for Lithium-ion Battery Packs

The active equalization of lithium-ion batteries involves transferring energy from high-voltage cells to low-voltage cells,ensuring consistent voltage levels across the battery pack and maintaining safety.This paper presents a voltage balancing circuit and control method.First,a single capacitor method is used to design the circuit topology for energy transfer.Next,real-time voltage detection and control are employed to balance energy between cells.Finally,simulation and experimental results demonstrate the effectiveness of the proposed method,achieving balanced voltages of 3.97 V from initial voltages of 4.10,3.97,and 3.90 V.The proposed circuit is simple,reliable,and effectively prevents overcharge and overdischarge.

Electrospraying Si/SiO_(x)/C and Sn/C nanosphere arrays on carbon cloth for high-performance flexible lithium-ion batteries

Exploring electrode materials with larger capacity,higher power density and longer cycle life was critical for developing advanced flexible lithium-ion batteries(LIBs).Herein,we used a controlled two-step method including electrospraying followed with calcination treatment by CVD furnace to design novel electrodes of Si/Si_(x)/C and Sn/C microrods array consisting of nanospheres on flexible carbon cloth substrate(denoted as Si/Si_(x)/C@CC,Sn/C@CC).Microrods composed of cumulated nanospheres(the diameter was approximately 120 nm)had a mean diameter of approximately 1.5μm and a length of around 4.0μm,distributing uniformly along the entire woven carbon fibers.Both of Si/Si/Si_(x)/C@CC and Sn/C@CC products were synthesized as binder-free anodes for Li-ion battery with the features of high reversible capacity and excellent cycling.Especially Si/Six/C electrode exhibited high specific capacity of about 1750 mA∙h∙g^(−1)at 0.5 A∙g^(−1)and excellent cycling ability even after 1050 cycles with a capacity of 1388 mA∙h∙g^(−1).Highly flexible Si/Si_(x)/C@CC//LiCoO_(2)batteries based on liquid and solid electrolytes were also fabricated,exhibiting high flexibility,excellent electrical stability and potential applications in flexible wearable electronics.

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

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

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

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

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

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

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.

Managing the surge:A comprehensive review of the entire disposal framework for retired lithium-ion batteries from electric vehicles

Anticipating the imminent surge of retired lithium-ion batteries(R-LIBs)from electric vehicles,the need for safe,cost-effective and environmentally friendly disposal technologies has escalated.This paper seeks to offer a comprehensive overview of the entire disposal framework for R-LIBs,encompassing a broad spectrum of activities,including screening,repurposing and recycling.Firstly,we delve deeply into a thorough examination of current screening technologies,shifting the focus from a mere enumeration of screening methods to the exploration of the strategies for enhancing screening efficiency.Secondly,we outline battery repurposing with associated key factors,summarizing stationary applications and sizing methods for R-LIBs in their second life.A particular light is shed on available reconditioning solutions,demonstrating their great potential in facilitating battery safety and lifetime in repurposing scenarios and identifying their techno-economic issues.In the realm of battery recycling,we present an extensive survey of pre-treatment options and subsequent material recovery technologies.Particularly,we introduce several global leading recyclers to illustrate their industrial processes and technical intricacies.Furthermore,relevant challenges and evolving trends are investigated in pursuit of a sustainable end-of-life management and disposal framework.We hope that this study can serve as a valuable resource for researchers,industry professionals and policymakers in this field,ultimately facilitating the adoption of proper disposal practices.

Exploring impedance spectrum for lithium-ion batteries diagnosis and prognosis:A comprehensive review

Lithium-ion batteries have extensive usage in various energy storage needs,owing to their notable benefits of high energy density and long lifespan.The monitoring of battery states and failure identification are indispensable for guaranteeing the secure and optimal functionality of the batteries.The impedance spectrum has garnered growing interest due to its ability to provide a valuable understanding of material characteristics and electrochemical processes.To inspire further progress in the investigation and application of the battery impedance spectrum,this paper provides a comprehensive review of the determination and utilization of the impedance spectrum.The sources of impedance inaccuracies are systematically analyzed in terms of frequency response characteristics.The applicability of utilizing diverse impedance features for the diagnosis and prognosis of batteries is further elaborated.Finally,challenges and prospects for future research are discussed.

Recovery of Li, Ni, Co and Mn from spent lithium-ion batteries assisted by organic acids: Process optimization and leaching mechanism

The proper recycling of spent lithium-ion batteries(LIBs)can promote the recovery and utilization of valuable resources,while also negative environmental effects resulting from the presence of toxic and hazardous substances.In this study,a new environmentally friendly hydro-metallurgical process was proposed for leaching lithium(Li),nickel(Ni),cobalt(Co),and manganese(Mn)from spent LIBs using sulfuric acid with citric acid as a reductant.The effects of the concentration of sulfuric acid,the leaching temperature,the leaching time,the solid-liquid ratio,and the reducing agent dosage on the leaching behavior of the above elements were investigated.Key parameters were optimized using response surface methodology(RSM)to maximize the recovery of metals from spent LIBs.The maxim-um recovery efficiencies of Li,Ni,Co,and Mn can reach 99.08%,98.76%,98.33%,and 97.63%.under the optimized conditions(the sulfuric acid concentration was 1.16 mol/L,the citric acid dosage was 15wt%,the solid-liquid ratio was 40 g/L,and the temperature was 83℃ for 120 min),respectively.It was found that in the collaborative leaching process of sulfuric acid and citric acid,the citric acid initially provided strong reducing CO_(2)^(-),and the transition metal ions in the high state underwent a reduction reaction to produce transition metal ions in the low state.Additionally,citric acid can also act as a proton donor and chelate with lower-priced transition metal ions,thus speeding up the dissolution process.

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.

Pyrometallurgical recycling of end-of-life lithium-ion batteries

The global importance of lithium-ion batteries(LIBs)has been increasingly underscored with the advancement of high-performance energy storage technologies.However,the end-of-life of these batteries poses significant challenges from environmental,economic,and resource management perspectives.This review paper focuses on the pyrometallurgy-based recycling process of lithium-ion batteries,exploring the fundamental understanding of this process and the importance of its optimization.Centering on the high energy consumption and emission gas issues of the pyrometallurgical recycling process,we systematically analyzed the capital-intensive nature of this process and the resulting technological characteristics.Furthermore,we conducted an in-depth discussion on the future research directions to overcome the existing technological barriers and limitations.This review will provide valuable insights for researchers and industry stakeholders in the battery recycling field.

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.

Selective leaching of lithium from spent lithium-ion batteries using sulfuric acid and oxalic acid

Traditional hydrometallurgical methods for recovering spent lithium-ion batteries(LIBs)involve acid leaching to simultaneously extract all valuable metals into the leachate.These methods usually are followed by a series of separation steps such as precipitation,extraction,and stripping to separate the individual valuable metals.In this study,we present a process for selectively leaching lithium through the synergistic effect of sulfuric and oxalic acids.Under optimal leaching conditions(leaching time of 1.5 h,leaching temperature of 70°C,liquid-solid ratio of 4 mL/g,oxalic acid ratio of 1.3,and sulfuric acid ratio of 1.3),the lithium leaching efficiency reached89.6%,and the leaching efficiencies of Ni,Co,and Mn were 12.8%,6.5%,and 21.7%.X-ray diffraction(XRD)and inductively coupled plasma optical emission spectrometer(ICP-OES)analyses showed that most of the Ni,Co,and Mn in the raw material remained as solid residue oxides and oxalates.This study offers a new approach to enriching the relevant theory for selectively recovering lithium from spent LIBs.

Flexible bidirectional pulse charging regulation achieving long-life lithium-ion batteries

Typical application scenarios,such as vehicle to grid(V2G)and frequency regulation,have imposed significant long-life demands on lithium-ion batteries.Herein,we propose an advanced battery life-extension method employing bidirectional pulse charging(BPC)strategy.Unlike traditional constant current charging methods,BPC strategy not only achieves comparable charging speeds but also facilitates V2G frequency regulation simultaneously.It significantly enhances battery cycle ampere-hour throughput and demonstrates remarkable life extension capabilities.For this interesting conclusion,adopting model identification and postmortem characterization to reveal the life regulation mechanism of BPC:it mitigates battery capacity loss attributed to loss of lithium-ion inventory(LLI)in graphite anodes by intermittently regulating the overall battery voltage and anode potential using a negative charging current.Then,from the perspective of internal side reaction,the life extension mechanism is further revealed as inhibition of solid electrolyte interphase(SEI)and lithium dendrite growth by regulating voltage with a bidirectional pulse current,and a semi-empirical life degradation model combining SEI and lithium dendrite growth is developed for BPC scenarios health management,the model parameters are identified by genetic algorithm with the life simulation exhibiting an accuracy exceeding 99%.This finding indicates that under typical rate conditions,adaptable BPC strategies can extend the service life of LFP battery by approximately 123%.Consequently,the developed advanced BPC strategy offers innovative perspectives and insights for the development of long-life battery applications in the future.

Unveiling the tailorable electrochemical properties of zeolitic imidazolate framework-derived Ni-doped LiCoO_(2) for lithium-ion batteries in half/full cells

As a prevailing cathode material of lithium-ion batteries(LIBs),LiCoO_(2)(LCO)still encounters the tricky problems of structural collapse,whose morphological engineering and cation doping are crucial for surmounting the mechanical strains and alleviating phase degradation upon cycling.Hereinafter,we propose a strategy using a zeolitic imidazolate framework(ZIF)as the self-sacrificing template to directionally prepare a series of LiNi_(0.1)Co_(0.9)O_(2)(LNCO)with tailorable electrochemical properties.The rational selection of sintering temperature imparts the superiority of the resultant products in lithium storage,during which the sample prepared at 700℃(LNCO-700)outperforms its counterparts in cyclability(156.8 mA h g^(-1)at 1 C for 200 cycles in half cells,1 C=275 mA g^(-1))and rate capability due to the expedited ion/electron transport and the strengthen mechanical robustness.The feasibility of proper Ni doping is also divulged by half/full cell tests and theoretical study,during which LNCO-700(167 mA h g^(-1)at 1 C for 100 cycles in full cells)surpasses LCO-700 in battery performance due to the mitigated phase deterioration,stabilized layered structu re,ameliorated electro nic co nductivity,a nd exalted lithium sto rage activity.This work systematically unveils tailorable electrochemical behaviors of LNCO to better direct their practical application.