Method for Estimating the State of Health of Lithium-ion Batteries Based on Differential Thermal Voltammetry and Sparrow Search Algorithm-Elman Neural Network

Precisely estimating the state of health(SOH)of lithium-ion batteries is essential for battery management systems(BMS),as it plays a key role in ensuring the safe and reliable operation of battery systems.However,current SOH estimation methods often overlook the valuable temperature information that can effectively characterize battery aging during capacity degradation.Additionally,the Elman neural network,which is commonly employed for SOH estimation,exhibits several drawbacks,including slow training speed,a tendency to become trapped in local minima,and the initialization of weights and thresholds using pseudo-random numbers,leading to unstable model performance.To address these issues,this study addresses the challenge of precise and effective SOH detection by proposing a method for estimating the SOH of lithium-ion batteries based on differential thermal voltammetry(DTV)and an SSA-Elman neural network.Firstly,two health features(HFs)considering temperature factors and battery voltage are extracted fromthe differential thermal voltammetry curves and incremental capacity curves.Next,the Sparrow Search Algorithm(SSA)is employed to optimize the initial weights and thresholds of the Elman neural network,forming the SSA-Elman neural network model.To validate the performance,various neural networks,including the proposed SSA-Elman network,are tested using the Oxford battery aging dataset.The experimental results demonstrate that the method developed in this study achieves superior accuracy and robustness,with a mean absolute error(MAE)of less than 0.9%and a rootmean square error(RMSE)below 1.4%.

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.

A review of data-driven whole-life state of health prediction for lithium-ion batteries:Data preprocessing,aging characteristics,algorithms,and future challenges

Lithium-ion batteries are the preferred green energy storage method and are equipped with intelligent battery management systems(BMSs)that efficiently manage the batteries.This not only ensures the safety performance of the batteries but also significantly improves their efficiency and reduces their damage rate.Throughout their whole life cycle,lithium-ion batteries undergo aging and performance degradation due to diverse external environments and irregular degradation of internal materials.This degradation is reflected in the state of health(SOH)assessment.Therefore,this review offers the first comprehensive analysis of battery SOH estimation strategies across the entire lifecycle over the past five years,highlighting common research focuses rooted in data-driven methods.It delves into various dimensions such as dataset integration and preprocessing,health feature parameter extraction,and the construction of SOH estimation models.These approaches unearth hidden insights within data,addressing the inherent tension between computational complexity and estimation accuracy.To enha nce support for in-vehicle implementation,cloud computing,and the echelon technologies of battery recycling,remanufacturing,and reuse,as well as to offer insights into these technologies,a segmented management approach will be introduced in the future.This will encompass source domain data processing,multi-feature factor reconfiguration,hybrid drive modeling,parameter correction mechanisms,and fulltime health management.Based on the best SOH estimation outcomes,health strategies tailored to different stages can be devised in the future,leading to the establishment of a comprehensive SOH assessment framework.This will mitigate cross-domain distribution disparities and facilitate adaptation to a broader array of dynamic operation protocols.This article reviews the current research landscape from four perspectives and discusses the challenges that lie ahead.Researchers and practitioners can gain a comprehensive understanding of battery SOH estimation methods,offering valuable insights for the development of advanced battery management systems and embedded application research.

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.

Electronic structure and spin state regulation of vanadium nitride via a sulfur doping strategy toward flexible zinc-air batteries

Owing to the distinctive structural characteristics,vanadium nitride(VN)is highly regarded as a catalyst for oxygen reduction reaction(ORR)in zinc-air batteries(ZABs).However,VN exhibits limited intrinsic ORR activity due to the weak adsorption ability to O-containing species.Here,the S-doped VN anchored on N,S-doped multi-dimensional carbon(S-VN/Co/NS-MC)was constructed using the solvothermal and in-situ doping methods.Incorporating sulfur atoms into VN species alters the electron spin state of vanadium in the S-VN/Co/NS-MC for regulating the adsorption energy of vanadium sites to oxygen molecules.The introduced sulfur atoms polarize the V 3d_(z)^(2) electrons,shifting spin-down electrons closer to the Fermi level in the S-VN/Co/NS-MC.Consequently,the introduction of sulfur atoms into VN species enhances the adsorption energy of vanadium sites for oxygen molecules.The*OOH dissociation transitions from being unspontaneous on the VN surface to a spontaneous state on the S-doped VN surface.Then,the ORR barrier on the S-VN/Co/NS-MC surface is reduced.The S-VN/Co/NS-MC demonstrates a higher half-wave potential and limiting current density compared to the VN/Co/N-MC.The S-VN/Co/NS-MC-based liquid ZABs display a power density of 195.7 m W cm^(-2),a specific capacity of 815.7 m A h g^(-1),and a cycling stability exceeding 250 h.The S-VN/Co/NS-MC-based flexible ZABs are successfully employed to charge both a smart watch and a mobile phone.This approach holds promise for advancing the commercial utilization of VN-based catalysts in ZABs.

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.

State-of-health estimation for fast-charging lithium-ion batteries based on a short charge curve using graph convolutional and long short-term memory networks

A fast-charging policy is widely employed to alleviate the inconvenience caused by the extended charging time of electric vehicles. However, fast charging exacerbates battery degradation and shortens battery lifespan. In addition, there is still a lack of tailored health estimations for fast-charging batteries;most existing methods are applicable at lower charging rates. This paper proposes a novel method for estimating the health of lithium-ion batteries, which is tailored for multi-stage constant current-constant voltage fast-charging policies. Initially, short charging segments are extracted by monitoring current switches,followed by deriving voltage sequences using interpolation techniques. Subsequently, a graph generation layer is used to transform the voltage sequence into graphical data. Furthermore, the integration of a graph convolution network with a long short-term memory network enables the extraction of information related to inter-node message transmission, capturing the key local and temporal features during the battery degradation process. Finally, this method is confirmed by utilizing aging data from 185 cells and 81 distinct fast-charging policies. The 4-minute charging duration achieves a balance between high accuracy in estimating battery state of health and low data requirements, with mean absolute errors and root mean square errors of 0.34% and 0.66%, respectively.

Key Issues for Modelling, Operation, Management and Diagnosis of Lithium Batteries: Current States and Prospects

1 Introduction.With the continuous growth of the global population,the energy demand continues to increase.However,due to the dominance of fossil fuels in global energy and fossil fuels are non-renewable,it has led to the global energy crisis[1].Besides,the use of fossil fuels will generate a mass of air pollutants(e.g.,carbon dioxide,sulfur dioxide,etc.),which will cause serious environmental pollution,climate change[2],etc.To resolve the aforementioned issues,countries around the world have implemented a variety of measures hoping to fundamentally adjust the global energy structure and achieve sustainable development.Thereinto,“Paris Agreement”reached in 2015 under the framework of“United Nations Framework Convention on Climate Change”aims to control the increase in the average temperature of the globe to within 2°C below preindustrial levels,and thereafter to peak global greenhouse gas emissions as soon as possible,continuously decreasing thereafter[3].United Kingdom plans to reduce the average exhaust emissions of“new cars”to approximately 50–70 g/km by 20230,which is roughly half of what it is now[4].In addition,China proposed a plan at“United Nations General Assembly”in 2020 to peak carbon dioxide emissions by 2030 and strive to achieve carbon neutrality by 2060.It is a fact that the whole world is committed to changing the current energy structure,protecting the Earth’s ecology,and achieving global sustainable development[5].

A review of Al-based material dopants for high-performance solid state lithium metal batteries

As the world transitions to green energy, there is a growing focus among many researchers on the requirement for high-efficient and safe batteries. Solid-state lithium metal batteries(SSLMBs) have emerged as a promising alternative to traditional liquid lithium-ion batteries(LIBs), offering higher energy density, enhanced safety, and longer lifespan. The rise of SSLMBs has brought about a transformation in energy storage, with aluminum(Al)-based material dopants playing a crucial role in advancing the next generation of batteries. The review highlights the significance of Al-based material dopants in SSLMBs applications, particularly its contributions to solid-state electrolytes(SSEs), cathodes, anodes,and other components of SSLMBs. Some studies have also shown that Al-based material dopants effectively enhance SSE ion conductivity, stabilize electrode and SSE interfaces, and suppress lithium dendrite growth, thereby enhancing the electrochemical performance of SSLMBs. Despite the above mentioned progresses, there are still problems and challenges need to be addressed. The review offers a comprehensive insight into the important role of Al in SSLMBs and addresses some of the issues related to its applications, endowing valuable support for the practical implementation of SSLMBs.

All-solid-state lithium batteries with inorganic solid electrolytes:Review of fundamental science

The scientific basis of all-solid-state lithium batteries with inorganic solid electrolytes is reviewed briefly, touching upon solid electrolytes, electrode materials, electrolyte/electrode interface phenomena, fabrication, and evaluation. The challenges and prospects are outlined as well.

Composite solid electrolyte of Na3PS4-PEO for all-solid-state SnS2/Na batteries with excellent interfacial compatibility between electrolyte and Na metal

High ionic conductivity and superior interfacial stability of solid electrolytes at the electrodes are crucial factors for high-performance all-solid-state sodium batteries. Herein, a composite solid electrolyte Na3PS4-polyethylene oxide is synthesized by the solution-phase reaction method with an improved ionic conductivity up to 9.4 × 10-5 S/cm at room temperature. Moreover, polyethylene oxide polymer layer is wrapped homogeneously on the surface of Na3PS4 particles, which could effectively avoid the direct contact between Na3PS4 electrolyte and sodium metal, thus alleviate their side reactions. We demonstrate that all-solid-state battery SnS2/Na with the composite solid electrolyte Na3PS4-polyethylene oxide delivers an enhanced electrochemical performance with 230 m Ah/g after 40 cycles.

LiNbO3-coated LiNi0.8Co0.1Mn0.1O2 cathode with high discharge capacity and rate performance for all-solid-state lithium battery

In order to obtain high power density,energy density and safe energy storage lithium ion batteries(LIB)to meet growing demand for electronic products,oxide cathodes have been widely explored in all-solidstate lithium batteries(ASSLB)using sulfide solid electrolyte.However,the electrochemical performances are still not satisfactory,due to the high interfacial resistance caused by severe interfacial instability between sulfide solid electrolyte and oxide cathode,especially Ni-rich oxide cathodes,in charge-discharge process.Ni-rich LiNi0.8Co0.1Mn0.1O2(NCM811)material at present is one of the most key cathode candidates to achieve the high energy density up to 300 Wh kg^-1 in liquid LIB,but rarely investigated in ASSLB using sulfide electrolyte.To design the stable interface between NCM811 and sulfide electrolyte should be extremely necessary.In this work,in view of our previous work,LiNbO3 coating with about 1 wt% content is adopted to improve the interfacial stability and the electrochemical performances of NCM811 cathode in ASSLB using Li10GeP2S12 solid electrolyte.Consequently,LiNbO3-coated NCM811 cathode displays the higher discharge capacity and rate performance than the reported oxide electrodes in ASSLB using sulfide solid electrolyte to our knowledge.

Statistical Models for Condition Monitoring and State of Health Estimation of Lithium-Ion Batteries for Ships

Battery systems are increasingly being used for powering ocean going ships,and the number of fully electric or hybrid ships relying on battery power for propulsion is growing.To ensure the safety of such ships,it is important to monitor the available energy that can be stored in the batteries,and classification societies typically require the state of health(SOH)to be verified by independent tests.This paper addresses statistical modeling of SOH for maritime lithium-ion batteries based on operational sensor data.Various methods for sensor-based,data-driven degradation monitoring will be presented,and advantages and challenges with the different approaches will be discussed.The different approaches include cumulative degradation models and snapshot models,models that need to be trained and models that need no prior training,and pure data-driven models and physics-informed models.Some of the methods only rely on measured data,such as current,voltage,and temperature,whereas others rely on derived quantities such as state of charge.Models include simple statistical models and more complicated machine learning techniques.Insight from this exploration will be important in establishing a framework for data-driven diagnostics and prognostics of maritime battery systems within the scope of classification societies.

CNTs@S composite as cathode for all-solid-state lithium-sulfur batteries with ultralong cycle life

The main challenges in development of traditional liquid lithium-sulfur batteries are the shuttle effect at the cathode caused by the polysulfide and the safety concern at the Li metal anode arose from the dendrite formation.All-solid-state lithium-sulfur batteries have been proposed to solve the shuttle effect and prevent short circuits.However,solid-solid contacts between the electrodes and the electrolyte increase the interface resistance and stress/strain,which could result in the limited electrochemical performances.In this work,the cathode of all-solid-state lithium-sulfur batteries is prepared by depositing sulfur on the surface of the carbon nanotubes(CNTs@S)and further mixing with Li10GeP2S12 electrolyte and acetylene black agents.At 60℃,CNTs@S electrode exhibits superior electrochemical performance,delivering the reversible discharge capacities of 1193.3,959.5,813.1,569.6 and 395.5 mAhg^-1 at the rate of 0.1,0.5,1,2 and 5 C,respectively.Moreover,the CNTs@S is able to demonstrate superior high-rate capability of 660.3 mAhg^-1 and cycling stability of 400 cycles at a high rate of 1.0 C.Such uniform distribution of the CNTs,S and Li10GeP2S12 electrolyte increase the electronic and ionic conductivity between the cathode and the electrolyte hence improves the rate performance and capacity retention.

A new multi-dimensional state of health evaluation method for lithium-ion batteries

Electric vehicles and battery energy storage are effective technical paths to achieve carbon neutrality,and lithium-ion batteries(LiBs)are very critical energy storage devices,which is of great significance to the goal.However,the battery’s characteristics of instant degradation seriously affect its long life and high safety applications.The aging mechanisms of LiBs are complex and multi-faceted,strongly influenced by numerous interacting factors.Currently,the degree of capacity fading is commonly used to describe the aging of the battery,and the ratio of the maximum available capacity to the rated capacity of the battery is defined as the state of health(SOH).However,the aging or health of the battery should be multifaceted.To realize the multi-dimensional comprehensive evaluation of battery health status,a novel SOH estimation method driven by multidimensional aging characteristics is proposed through the improved single-particle model.The parameter identification and sensitivity analysis of the model were carried out during the whole cycle of life in a wide temperature environment.Nine aging characteristic parameters were obtained to describe the SOH.Combined with aging mechanisms,the current health status was evaluated from four aspects:capacity level,lithium-ion dif-fusion,electrochemical reaction,and power capacity.The proposed method can more comprehensively evaluate the aging charac-teristics of batteries,and the SOH estimation error is within 2%.

An improved state-of-charge estimation method for sodium-ion battery based on combined correction of voltage and internal resistance

ABSTRACT The accurate state-of-charge(SOC)estimation of sodium-ion batteries is the basis for their efficient application.In this paper,a new SOC estimation method suitable for sodium-ion batteries and their application conditions is proposed,which considers the combination of open circuit voltage(OCV)and internal resistance correction.First,the optimal order of equivalent circuit model is analyzed and selected,and the monotonic and stable mapping relationships between OCV and SOC,as well as between ohmic internal resistance and SOC are determined.Then,a joint estimation algorithm for battery model parameters and SOC is estab-lished,and a joint SOC correction strategy based on OCV and ohmic internal resistance is established.The test results show that OCV correction is reliable when polarization is small,that the ohmic internal resistance correction is reliable when the current fluctuation is large,and that the maximum absolute error of SOC estimation of the proposed method is not more than 2.6%.

State of Health Estimation of LiFePO_(4) Batteries for Battery Management Systems

When considering the mechanism of the batteries,the capacity reduction at storage(when not in use)and cycling(during use)and increase of internal resistance is because of degradation in the chemical composition inside the batteries.To optimize battery usage,a battery management system(BMS)is used to estimate possible aging effects while different load profiles are requested from the grid.This is specifically seen in a case when the vehicle is connected to the net(online through BMS).During this process,the BMS chooses the optimized load profiles based on the least aging effects on the battery pack.The major focus of this paper is to design an algorithm/model for lithium iron phosphate(LiFePO4)batteries.The model of the batteries is based on the accelerated aging test data(data from the beginning of life till the end of life).The objective is to develop an algorithm based on the actual battery trend during the whole life of the battery.By the analysis of the test data,the complete trend of the battery aging and the factors on which the aging is depending on is identified,the aging model can then be recalibrated to avoid any differences in the production process during cell manufacturing.The validation of the model was carried out at the end by utilizing different driving profiles at different C-rates and different ambient temperatures.A Linear and non-linear model-based approach is used based on statistical data.The parameterization was carried out by dividing the data into small chunks and estimating the parameters for the individual chunks.Self-adaptive characteristic map using a lookup table was also used.The nonlinear model was chosen as the best candidate among all other approaches for longer validation of 8-month data with real driving data set.

A Lithium-free Anode Toward Stable All-solid-state Lithium Metal Batteries

Lithium(Li)metal has the lowest standard electrochemical redox potential and very high theoretical specific capacity,making it the most potential anode material for rechargeable batteries[1].The use of Li metal in organic liquid electrolyte faces many issues in terms of battery performance and safety[2].Solid-electrolyte in-terphase(SEI)formation during the uneven Li deposition will continuously consume Li and dry up the electrolyte.Solid-state electrolytes are superior to liquid counterparts in terms of battery safety due to their nonflammable nature[3].

Supercritical-hydrothermal accelerated solid state reaction route for synthesis of LiMn_2O_4 cathode material for high-power Li-ion batteries

Synthesis of the spinel structure lithium manganese oxide （LiMn2O4） by supercritical hydrothermal （SH） accelerated solid state reaction （SSR） route was studied. The impacts of the reaction pressure, reaction temperature and reaction time of SH route, and the calcination temperature of SSR route on the purity, particle morphology and electrochemical properties of the prepared LiMn2O4 materials were studied. The experimental results show that after 15 min reaction in SH route at 400 ℃ and 30 MPa, the reaction time of SSR could be significantly decreased, e.g. down to 3 h with the formation temperature of 800 ℃, compared with the conventional solid state reaction method. The prepared LiMn2O4 material exhibits good crystallinity, uniform size distribution and good electrochemical performance, and has an initial specific capacity of 120 mA.h/g at a rate of 0.1C （1C=148 mA/g） and a good rate capability at high rates, even up to 50C.

State of charge estimation of Li-ion batteries in an electric vehicle based on a radial-basis-function neural network

The on-line estimation of the state of charge （SOC） of the batteries is important for the reliable running of the pure electric vehicle in practice. Because a nonlinear feature exists in the batteries and the radial-basis-function neural network （RBF NN） has good characteristics to solve the nonlinear problem, a practical method for the SOC estimation of batteries based on the RBF NN with a small number of input variables and a simplified structure is proposed. Firstly, in this paper, the model of on-line SOC estimation with the RBF NN is set. Secondly, four important factors for estimating the SOC are confirmed based on the contribution analysis method, which simplifies the input variables of the RBF NN and enhttnces the real-time performance of estimation. FiItally, the pure electric buses with LiFePO4 Li-ion batteries running during the period of the 2010 Shanghai World Expo are considered as the experimental object. The performance of the SOC estimation is validated and evaluated by the battery data from the electric vehicle.