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 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.

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.

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.

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.

Solvent extraction and separation of cobalt from leachate of spent lithium-ion battery cathodes with N263 in nitrite media

To effectively separate and recover Co(Ⅱ) from the leachate of spent lithium-ion battery cathodes,we investigated solvent extraction with quaternary ammonium salt N263 in the sodium nitrite system.NO_(2)^(-)combines with Co(Ⅱ) to form an anion [Co(NO_(2))_(3)]^(-),and it is then extracted by N263.The extraction of Co(Ⅱ) is related to the concentration of NO_(2)^(-).The extraction efficiency of Co(Ⅱ) reaches the maximum of99.16%,while the extraction efficiencies of Ni(Ⅱ),Mn(Ⅱ),and Li(Ⅰ) are 9.27%-9.80% under the following conditions:30vol% of N263 and15vol% of iso-propyl alcohol in sulfonated kerosene,the volume ratio of the aqueous-to-organic phase is 2:1,the extraction time is 30 min,and1 M sodium nitrite in 0.1 MHNO_(3).The theoretical stages require for the Co(Ⅱ) extraction are performed in the McCabe–Thiele diagram,and the extraction efficiency of Co(Ⅱ) reaches more than 99.00% after three-stage counter-current extraction with Co(Ⅱ) concentration of 2544mg/L.When the HCl concentration is 1.5 M,the volume ratio of the aqueous-to-organic phase is 1:1,the back-extraction efficiency of Co(Ⅱ)achieves 91.41%.After five extraction and back-extraction cycles,the Co(Ⅱ) extraction efficiency can still reach 93.89%.The Co(Ⅱ) extraction efficiency in the actual leaching solution reaches 100%.

Prelithiation strategies for silicon-based anode in high energy density lithium-ion battery

Green energy storage devices play vital roles in reducing fossil fuel emissions and achieving carbon neutrality by 2050.Growing markets for portable electronics and electric vehicles create tremendous demand for advanced lithium-ion batteries(LIBs)with high power and energy density,and novel electrode material with high capacity and energy density is one of the keys to next-generation LIBs.Silicon-based materials,with high specific capacity,abundant natural resources,high-level safety and environmental friendliness,are quite promising alternative anode materials.However,significant volume expansion and redundant side reactions with electrolytes lead to active lithium loss and decreased coulombic efficiency(CE)of silicon-based material,which hinders the commercial application of silicon-based anode.Prelithiation,preembedding extra lithium ions in the electrodes,is a promising approach to replenish the lithium loss during cycling.Recent progress on prelithiation strategies for silicon-based anode,including electrochemical method,chemical method,direct contact method,and active material method,and their practical potentials are reviewed and prospected here.The development of advanced Si-based material and prelithiation technologies is expected to provide promising approaches for the large-scale application of silicon-based materials.

Robust state of charge estimation of lithium-ion battery via mixture kernel mean p-power error loss LSTM with heap-based-optimizer

The state of charge(SOC)estimation of lithium-ion battery is an important function in the battery management system(BMS)of electric vehicles.The long short term memory(LSTM)model can be employed for SOC estimation,which is capable of estimating the future changing states of a nonlinear system.Since the BMS usually works under complicated operating conditions,i.e the real measurement data used for model training may be corrupted by non-Gaussian noise,and thus the performance of the original LSTM with the mean square error(MSE)loss may deteriorate.Therefore,a novel LSTM with mixture kernel mean p-power error(MKMPE)loss,called MKMPE-LSTM,is developed by using the MKMPE loss to replace the MSE as the learning criterion in LSTM framework,which can achieve robust SOC estimation under the measurement data contaminated with non-Gaussian noises(or outliers)because of the MKMPE containing the p-order moments of the error distribution.In addition,a meta-heuristic algorithm,called heap-based-optimizer(HBO),is employed to optimize the hyper-parameters(mainly including learning rate,number of hidden layer neuron and value of p in MKMPE)of the proposed MKMPE-LSTM model to further improve its flexibility and generalization performance,and a novel hybrid model(HBO-MKMPE-LSTM)is established for SOC estimation under non-Gaussian noise cases.Finally,several tests are performed under various cases through a benchmark to evaluate the performance of the proposed HBO-MKMPE-LSTM model,and the results demonstrate that the proposed hybrid method can provide a good robustness and accuracy under different non-Gaussian measurement noises,and the SOC estimation results in terms of mean square error(MSE),root MSE(RMSE),mean absolute relative error(MARE),and determination coefficient R2are less than 0.05%,3%,3%,and above 99.8%at 25℃,respectively.

Electrolyte induced synergistic construction of cathode electrolyte interphase and capture of reactive free radicals for safer high energy density lithium-ion battery

As the energy density of battery increases rapidly,lithium-ion batteries(LIBs)are facing serious safety issue with thermal runaway,which largely limits the large-scale applications of high-energy-density LIBs.It is generally agreed that the chemical crosstalk between the cathode and anode leads to thermal runaway of LIBs.Herein,a multifunctional high safety electrolyte is designed with synergistic construction of cathode electrolyte interphase and capture of reactive free radicals to limit the intrinsic pathway of thermal runaway.The cathode electrolyte interphase not only resists the gas attack from the anode but suppresses the parasitic side reactions induced by electrolyte.And the function of free radical capture has the ability of reducing heat release from thermal runaway of battery.The dual strategy improves the intrinsic safety of battery prominently that the triggering temperature of thermal runaway is increased by 24.4℃and the maximum temperature is reduced by 177.7℃.Simultaneously,the thermal runaway propagation in module can be self-quenched.Moreover,the electrolyte design balances the trade-off of electrochemical and safety performance of high-energy batteries.The capacity retention of LiNi_(0.8)Co_(0.1)Mn_(0.1)O_(2)|graphite pouch cell has been significantly increased from 53.85%to 97.05%with higher coulombic efficiency of 99.94%at operating voltage extended up to 4.5 V for 200 cycles.Therefore,this work suggests a feasible strategy to mitigate the safety risk of high-energy-density LIBs without sacrificing electrochemical performances.

Lithium-ion battery degradation trajectory early prediction with synthetic dataset and deep learning

Knowing the long-term degradation trajectory of Lithium-ion(Li-ion) battery in its early usage stage is critical for the maintenance of the battery energy storage system(BESS) in reality. Previous battery health diagnosis methods focus on capacity and state of health(SOH) estimation which can receive only the short-term health status of the cell. This paper proposes a novel degradation trajectory prediction method with synthetic dataset and deep learning, which enables to grasp the characterization of the cell's health at a very early stage of Li-ion battery usage. A transferred convolutional neural network(CNN) is chosen to finalize the early prediction target, and the polynomial function based synthetic dataset generation strategy is designed to reduce the costly data collection procedure in real application. In this thread, the proposed method needs one full lifespan data to predict the overall degradation trajectories of other cells. With only the full lifespan cycling data from 4 cells and 100 cycling data from each cell in experimental validation, the proposed method shows a good prediction accuracy on a dataset with more than 100 commercial Li-ion batteries.

Two-dimensional CrP_(2) with high specific capacity and fast charge rate for lithium-ion battery

The electrode material is regarded as one of the key factors that determine the performance of lithium-ion batteries(LIBs).However,it is still a challenge to search for an anode material with large capacity,low diffusion barrier,and good stability.In the present work,two new CrP_(2) monolayers(Pmmn-CrP_(2) and Pmma-CrP_(2)) are predicted by means of first principles swarm structure search.Our study shows that both the two CrP_(2) monolayers have high dynamical and thermal stability,as well as excellent electron conductivity.Additionally,Pmmn-CrP_(2) exhibits a remarkably high storage capacity of 705 mA·h·g^(-1) for Li,meanwhile the diffusion energy barrier of Li on the surface of this monolayer is 0.21 eV,ensuring it as a high-performance anode material for LIBs.We hope that our study will inspire researchers to search for new-type two-dimensional(2D) transition metal phosphides for the electrode materials of LIB s.

Physics-informed neural network approach for heat generation rate estimation of lithium-ion battery under various driving conditions

Accurate insight into the heat generation rate(HGR) of lithium-ion batteries(LIBs) is one of key issues for battery management systems to formulate thermal safety warning strategies in advance.For this reason,this paper proposes a novel physics-informed neural network(PINN) approach for HGR estimation of LIBs under various driving conditions.Specifically,a single particle model with thermodynamics(SPMT) is first constructed for extracting the critical physical knowledge related with battery HGR.Subsequently,the surface concentrations of positive and negative electrodes in battery SPMT model are integrated into the bidirectional long short-term memory(BiLSTM) networks as physical information.And combined with other feature variables,a novel PINN approach to achieve HGR estimation of LIBs with higher accuracy is constituted.Additionally,some critical hyperparameters of BiLSTM used in PINN approach are determined through Bayesian optimization algorithm(BOA) and the results of BOA-based BiLSTM are compared with other traditional BiLSTM/LSTM networks.Eventually,combined with the HGR data generated from the validated virtual battery,it is proved that the proposed approach can well predict the battery HGR under the dynamic stress test(DST) and worldwide light vehicles test procedure(WLTP),the mean absolute error under DST is 0.542 kW/m^(3),and the root mean square error under WLTP is1.428 kW/m^(3)at 25℃.Lastly,the investigation results of this paper also show a new perspective in the application of the PINN approach in battery HGR estimation.

Deep Transfer Ensemble Learning-Based Diagnostic of Lithium-Ion Battery

Dear Editor,State of health(SOH)estimation is critical for the management of lithium-ion batteries(LIBs).Data-driven estimation methods are appealing with the availability of real-world battery data.However,time-and data-costly training for batteries with different chemistries and models barriers their efficient deployment.

An Early Minor-Fault Diagnosis Method for Lithium-Ion Battery Packs Based on Unsupervised Learning

Dear Editor,Any fault of a battery system that is not handled timely can cause catastrophic consequences.Therefore,it is significant to diagnose battery faults early and accurately.Due to the complex nonlinear features and inconsistency of lithium batteries,traditional fault diagnosis methods usually fail to detect battery minor faults in the early stages.

Deep learning enhanced lithium-ion battery nonlinear fading prognosis

With the assistance of artificial intelligence,advanced health prognosis technique plays a critical role in the lithium-ion(Li-ion) batteries management system.However,conventional data-driven early aging prediction exhibited dramatic drawbacks,i.e.,volatile capacity nonlinear fading trajectories create obstacles to the accurate multistep ahead prediction due to the complex working conditions of batteries.Herein,a novel deep learning model is proposed to achieve a universal and accurate Li-ion battery aging prognosis.Two battery datasets with various electrode types and cycling conditions are developed to validate the proposed approaches.Knee-point probability(KPP),extracted from the capacity loss curve,is first proposed to detect knee points and improve state-of-health(SOH) predictive accuracy,especially during periods of rapid capacity decline.Using one-cycle data of partial raw voltage as the model input,the SOH and KPP can be simultaneously predicted at multistep ahead,whereas the conventional method showed worse accuracy.Furthermore,to explore the underlying characteristics among various degradation tendencies,an online model update strategy is developed by leveraging the adversarial adaptationinduced transfer learning technique.This work gains new sights into the comprehensive Li-ion battery management and prognosis framework through decomposing capacity degradation trajectories and adversarial learning on the unlabeled samples.

An upgraded polymeric composite with interparticle chemical bonding microstructure toward lithium-ion battery separators with enhanced safety and electrochemical performances

A composite separator of SiC/PVDF-HFP was synthesized for lithium-ion batteries with high thermal and mechanical stabilities.Benefiting from the nanoscale,high hardness,and melting point of SiC,SiC/PVDFHFP with highly uniform microstructure was obtained.This polarization caused by barrier penetration was significantly restrained.Due to the Si-F bond between SiC and PVDF-HFP,the structural stability has been obviously enhanced,which could suppress the growth of lithium(Li) dendrite.Furthermore,some 3D reticulated Si nanowires are found on the surface of Li anode,which also greatly inhibit Li dendrites and result in irregular flakes of Li metal.Especially,the shrinkage of 6% SiC/PVDF-HFP at 150℃ is only 5%,which is notably lower than those of PVDF-HFP and Celgard2500.The commercial LiFePO_(4) cell assembled with 6% SiC/PVDF-HFP possesses a specific capacity of 157.8 mA h g^(-1) and coulomb efficiency of 98% at 80℃.In addition,the tensile strength and modulus of 6% SiC/PVDF-HFP could reach 14.6 and 562 MPa,respectively.And a small deformation(1000 nm) and strong deformation recovery are obtained under a high additional load(2.3 mN).Compared with PVDF-HFP and Celgard2500,the symmetric Li cell assembled with 6% SiC/PVDF-HFP has not polarized after 900 cycles due to its excellent mechanical stabilities.This strategy provides a feasible solution for the composite separator of high-safety batteries with a high temperature and impact resistance.

Online Identification of Lithium-ion Battery Model Parameters with Initial Value Uncertainty and Measurement Noise

Online parameter identification is essential for the accuracy of the battery equivalent circuit model(ECM).The traditional recursive least squares(RLS)method is easily biased with the noise disturbances from sensors,which degrades the modeling accuracy in practice.Meanwhile,the recursive total least squares(RTLS)method can deal with the noise interferences,but the parameter slowly converges to the reference with initial value uncertainty.To alleviate the above issues,this paper proposes a co-estimation framework utilizing the advantages of RLS and RTLS for a higher parameter identification performance of the battery ECM.RLS converges quickly by updating the parameters along the gradient of the cost function.RTLS is applied to attenuate the noise effect once the parameters have converged.Both simulation and experimental results prove that the proposed method has good accuracy,a fast convergence rate,and also robustness against noise corruption.

Multi-scale computation methods:Their applications in lithium-ion battery research and development

Based upon advances in theoretical algorithms, modeling and simulations, and computer technologies, the rational design of materials, cells, devices, and packs in the field of lithium-ion batteries is being realized incrementally and will at some point trigger a paradigm revolution by combining calculations and experiments linked by a big shared database, enabling accelerated development of the whole industrial chain. Theory and multi-scale modeling and simulation, as supplements to experimental efforts, can help greatly to close some of the current experimental and technological gaps, as well as predict path-independent properties and help to fundamentally understand path-independent performance in multiple spatial and temporal scales.