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.

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.

Synergistic interactions between the charge‐transport and mechanical properties of the ionic‐liquid‐based solid polymer electrolytes for solid‐state lithium batteries

The performance sensitivity of the solid‐state lithium cells to the synergistic interactions of the charge‐transport and mechanical properties of the electrolyte is well acknowledged in the literature,but the quantitative insights therein are very limited.Here,the charge‐transport and mechanical properties of a polymerized ionic‐liquid‐based solid electrolyte are reported.The transference number and diffusion coefficient of lithium in the concentrated solid electrolyte are measured as a function of concentration and stack pressure.The elastoplastic behavior of the electrolyte is quantified under compression,within a home‐made setup,to substantiate the impact of stack pressure on the stability of the Li/electrolyte interface in the symmetric lithium cells.The results spotlight the interaction between the concentration and thickness of the solid electrolyte and the stack pressure in determining the polarization and stability of the solid‐state lithium batteries during extended cycling.

Boosting battery state of health estimation based on self-supervised learning

State of health(SoH) estimation plays a key role in smart battery health prognostic and management.However,poor generalization,lack of labeled data,and unused measurements during aging are still major challenges to accurate SoH estimation.Toward this end,this paper proposes a self-supervised learning framework to boost the performance of battery SoH estimation.Different from traditional data-driven methods which rely on a considerable training dataset obtained from numerous battery cells,the proposed method achieves accurate and robust estimations using limited labeled data.A filter-based data preprocessing technique,which enables the extraction of partial capacity-voltage curves under dynamic charging profiles,is applied at first.Unsupervised learning is then used to learn the aging characteristics from the unlabeled data through an auto-encoder-decoder.The learned network parameters are transferred to the downstream SoH estimation task and are fine-tuned with very few sparsely labeled data,which boosts the performance of the estimation framework.The proposed method has been validated under different battery chemistries,formats,operating conditions,and ambient.The estimation accuracy can be guaranteed by using only three labeled data from the initial 20% life cycles,with overall errors less than 1.14% and error distribution of all testing scenarios maintaining less than 4%,and robustness increases with aging.Comparisons with other pure supervised machine learning methods demonstrate the superiority of the proposed method.This simple and data-efficient estimation framework is promising in real-world applications under a variety of scenarios.

Deep learning-based battery state of charge estimation:Enhancing estimation performance with unlabelled training samples

The estimation of state of charge(SOC)using deep neural networks(DNN)generally requires a considerable number of labelled samples for training,which refer to the current and voltage pieces with knowing their corresponding SOCs.However,the collection of labelled samples is costly and time-consuming.In contrast,the unlabelled training samples,which consist of the current and voltage data with unknown SOCs,are easy to obtain.In view of this,this paper proposes an improved DNN for SOC estimation by effectively using both a pool of unlabelled samples and a limited number of labelled samples.Besides the traditional supervised network,the proposed method uses an input reconstruction network to reformulate the time dependency features of the voltage and current.In this way,the developed network can extract useful information from the unlabelled samples.The proposed method is validated under different drive cycles and temperature conditions.The results reveal that the SOC estimation accuracy of the DNN trained with both labelled and unlabelled samples outperforms that of only using a limited number of labelled samples.In addition,when the dataset with reduced number of labelled samples to some extent is used to test the developed network,it is found that the proposed method performs well and is robust in producing the model outputs with the required accuracy when the unlabelled samples are involved in the model training.Furthermore,the proposed method is evaluated with different recurrent neural networks(RNNs)applied to the input reconstruction module.The results indicate that the proposed method is feasible for various RNN algorithms,and it could be flexibly applied to other conditions as required.

Estimating the State of Health for Lithium-ion Batteries:A Particle Swarm Optimization-Assisted Deep Domain Adaptation Approach

The state of health(SOH)is a critical factor in evaluating the performance of the lithium-ion batteries(LIBs).Due to various end-user behaviors,the LIBs exhibit different degradation modes,which makes it challenging to estimate the SOHs in a personalized way.In this article,we present a novel particle swarm optimization-assisted deep domain adaptation(PSO-DDA)method to estimate the SOH of LIBs in a personalized manner,where a new domain adaptation strategy is put forward to reduce cross-domain distribution discrepancy.The standard PSO algorithm is exploited to automatically adjust the chosen hyperparameters of developed DDA-based method.The proposed PSODDA method is validated by extensive experiments on two LIB datasets with different battery chemistry materials,ambient temperatures and charge-discharge configurations.Experimental results indicate that the proposed PSO-DDA method surpasses the convolutional neural network-based method and the standard DDA-based method.The Py Torch implementation of the proposed PSO-DDA method is available at https://github.com/mxt0607/PSO-DDA.

Physics-based battery SOC estimation methods:Recent advances and future perspectives

The reliable prediction of state of charge(SOC)is one of the vital functions of advanced battery management system(BMS),which has great significance towards safe operation of electric vehicles.By far,the empirical model-based and data-driven-based SOC estimation methods of lithium-ion batteries have been comprehensively discussed and reviewed in various literatures.However,few reviews involving SOC estimation focused on electrochemical mechanism,which gives physical explanations to SOC and becomes most attractive candidate for advanced BMS.For this reason,this paper comprehensively surveys on physics-based SOC algorithms applied in advanced BMS.First,the research progresses of physical SOC estimation methods for lithium-ion batteries are thoroughly discussed and corresponding evaluation criteria are carefully elaborated.Second,future perspectives of the current researches on physics-based battery SOC estimation are presented.The insights stated in this paper are expected to catalyze the development and application of the physics-based advanced BMS algorithms.

Current collectors’ effects on the electrochemical performance of LiNi_(0.6)Co_(0.2)Mn_(0.2)O_(2) suspension electrodes for lithium slurry battery

Take after the advantages of lithium-ion battery (LIB) and redox flow battery (RFB), semi-solid flow battery (SSFB) is a promising electrochemical energy storage device in renewable energy utilization. The flowable slurry electrode realizes decouple of energy and power density, while it also brings about new challenge to SSFBs, electron transport between active material and the out circuit. In this consideration, three types of current collectors (CCs) are applied to study the resistance and electrochemical performances of slurry cathodes within pouch cells for the first time. It proves that the electronic resistance (Re) between slurry electrode and the CC plays a decisive role in SSFB operation, and it is so large when Al foil is adopted that the cell cannot even work. Contact angle between Ketjen black (KB) slurry without active material (AM) and the CC is a preliminarily sign for the Re, the smaller the angle, the lower the resistance, and the better electrochemical performance of the cell.

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.

Functional Optical Fiber Sensors Detecting Imperceptible Physical/Chemical Changes for Smart Batteries

The battery technology progress has been a contradictory process in which performance improvement and hidden risks coexist.Now the battery is still a“black box”,thus requiring a deep understanding of its internal state.The battery should“sense its internal physical/chemical conditions”,which puts strict requirements on embedded sensing parts.This paper summarizes the application of advanced optical fiber sensors in lithium-ion batteries and energy storage technologies that may be mass deployed,focuses on the insights of advanced optical fiber sensors into the processes of one-dimensional nano-micro-level battery material structural phase transition,electrolyte degradation,electrode-electrolyte interface dynamics to three-dimensional macro-safety evolution.The paper contributes to understanding how to use optical fiber sensors to achieve“real”and“embedded”monitoring.Through the inherent advantages of the advanced optical fiber sensor,it helps clarify the battery internal state and reaction mechanism,aiding in the establishment of more detailed models.These advancements can promote the development of smart batteries,with significant importance lying in essentially promoting the improvement of system consistency.Furthermore,with the help of smart batteries in the future,the importance of consistency can be weakened or even eliminated.The application of advanced optical fiber sensors helps comprehensively improve the battery quality,reliability,and life.

Atomistic understanding of capacity loss in LiNiO_(2)for high-nickel Li-ion batteries:First-principles study

Combining the first-principles calculations and structural enumeration with recognition,the delithiation process of LiNiO_(2)is investigated,where various supercell shapes are considered in order to obtain the formation energy of Li_(x)NiO_(2).Meanwhile,the voltage profile is simulated and the ordered phases of lithium vacancies corresponding to concentrations of 1/4,2/5,3/7,1/2,2/3,3/4,5/6,and 6/7 are predicted.To understand the capacity decay in the experiment during the charge/discharge cycles,deoxygenation and Li/Ni antisite defects are calculated,revealing that the chains of oxygen vacancies will be energetically preferrable.It can be inferred that in the absence of oxygen atom in high delithiate state,the diffusion of Ni atoms is facilitated and the formation of Li/Ni antisite is induced.

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.

Empowering the Future: Exploring the Construction and Characteristics of Lithium-Ion Batteries

Lithium element has attracted remarkable attraction for energy storage devices, over the past 30 years. Lithium is a light element and exhibits the low atomic number 3, just after hydrogen and helium in the periodic table. The lithium atom has a strong tendency to release one electron and constitute a positive charge, as Li . Initially, lithium metal was employed as a negative electrode, which released electrons. However, it was observed that its structure changed after the repetition of charge-discharge cycles. To remedy this, the cathode mainly consisted of layer metal oxide and olive, e.g., cobalt oxide, LiFePO4, etc., along with some contents of lithium, while the anode was assembled by graphite and silicon, etc. Moreover, the electrolyte was prepared using the lithium salt in a suitable solvent to attain a greater concentration of lithium ions. Owing to the lithium ions’ role, the battery’s name was mentioned as a lithium-ion battery. Herein, the presented work describes the working and operational mechanism of the lithium-ion battery. Further, the lithium-ion batteries’ general view and future prospects have also been elaborated.