In industrial production and engineering operations,the health state of complex systems is critical,and predicting it can ensure normal operation.Complex systems have many monitoring indicators,complex coupling struct...In industrial production and engineering operations,the health state of complex systems is critical,and predicting it can ensure normal operation.Complex systems have many monitoring indicators,complex coupling structures,non-linear and time-varying characteristics,so it is a challenge to establish a reliable prediction model.The belief rule base(BRB)can fuse observed data and expert knowledge to establish a nonlinear relationship between input and output and has well modeling capabilities.Since each indicator of the complex system can reflect the health state to some extent,the BRB is built based on the causal relationship between system indicators and the health state to achieve the prediction.A health state prediction model based on BRB and long short term memory for complex systems is proposed in this paper.Firstly,the LSTMis introduced to predict the trend of the indicators in the system.Secondly,the Density Peak Clustering(DPC)algorithmis used todetermine referential values of indicators for BRB,which effectively offset the lack of expert knowledge.Then,the predicted values and expert knowledge are fused to construct BRB to predict the health state of the systems by inference.Finally,the effectiveness of the model is verified by a case study of a certain vehicle hydraulic pump.展开更多
Accurate estimation of the State of Charge(SOC),State of Health(SOH),and Terminal Resistance(TR)is crucial for the effective operation of Battery Management Systems(BMS)in lithium-ion batteries.This study conducts a c...Accurate estimation of the State of Charge(SOC),State of Health(SOH),and Terminal Resistance(TR)is crucial for the effective operation of Battery Management Systems(BMS)in lithium-ion batteries.This study conducts a comprehensive comparative analysis of four Kalman filter variants Extended Kalman Filter(EKF),Extended Kalman-Bucy Filter(EKBF),Unscented Kalman Filter(UKF),and Unscented Kalman-Bucy Filter(UKBF)under varying battery parameter conditions.These include temperature fluctuation,self-discharge,current direction,cell capacity,process noise,and measurement noise.Our findings reveal significant variations in the performance of SOC and SOH predictions across filters,emphasizing that UKF demonstrates superior robustness to noise,while EKF performs better under accurate system dynamics.The study underscores the need for adaptive filtering strategies that can dynamically adjust to evolving battery parameters,thereby enhancing BMS reliability and extending battery lifespan.展开更多
Predicting the future health state of a transformer can offer early warning of latent defects and faults within the transformer,thereby facilitating the formulation of power outage maintenance plans and power dispatch...Predicting the future health state of a transformer can offer early warning of latent defects and faults within the transformer,thereby facilitating the formulation of power outage maintenance plans and power dispatch strategies.However,existing prediction methods based on the structure of‘splicing prediction and diagnosis method’suffer from limitations such as inability to achieve global optimality,error accumulation,and low prediction accuracy.To fill this gap,a novel direct prediction method of a trans-former state based on knowledge and data fusion-driven model(K&DFDM)is pro-posed in this paper.Firstly,a state quantity data space is constructed to comprehensively reflect the changes in the health state of the transformer over time,encompassing online monitoring,offline testing,evaluation results,and actual operation data.After that,correlation knowledge between state quantities,fault diagnosis mechanism knowledge,current diagnosis experience knowledge,and uncertain fuzzy knowledge are extracted separately.The actual fault mechanism,existing expert experience,and other knowledge in the diagnosis process are quantified.Then,the attention model is sub-sequently optimised,leveraging quantitative knowledge to effectively constrain and guide the data prediction process.Incorporating fault diagnosis mechanism knowledge into the data prediction model enables the achievement of global optimisation in both diagnosis and prediction.The integration of traditional expert experience knowledge and the correlation knowledge between state quantities serves as constraints during the process of attaining the global optimum.The verification results,comprising 327 cases,demonstrate that K&DFDM effectively addresses the issue of error superposition encountered by existing state prediction methods,leading to a direct state prediction accuracy of 96.33%.展开更多
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,curr...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%.展开更多
Lithium-ion batteries are essential for renewable energy storage,necessitating efficient battery management systems(BMS)for optimal performance and longevity.Accurate estimation of the state of health(SOH)is crucial f...Lithium-ion batteries are essential for renewable energy storage,necessitating efficient battery management systems(BMS)for optimal performance and longevity.Accurate estimation of the state of health(SOH)is crucial for BMS safety,yet current machine learning-based SOH estimation relying on global aging features often overlooks localized degradation patterns.In this study,we introduce a novel SOH estimation pipeline that integrates voltage-range-specific segmentation with a multi-stage,crossvalidation-driven localized feature-selection framework and a feature-augmented dual-stream fusion network.Our methodology partitions full-range voltage into localized intervals to construct a degradation-sensitive feature library,from which 4 optimal features are identified from a set of 336 candidates.These selected features are combined with raw voltage signals via a dual-stream architecture that employs a dynamic gating mechanism to recalibrate feature contributions during training.Crossvalidation-based evaluation on datasets encompassing different chemistries and charge/discharge protocols demonstrate that our approach can achieve lower average root-mean-square-error(Oxford dataset:0.7201%,Massachusetts Institute of Technology(MIT)dataset:0.7184%)compared to baseline models.An in-depth analysis of the physical significance of the screened features improves the interpretability of the features.This work underscores the significant potential of leveraging localized feature enhancement in SOH estimation by systematically integrating degradation-sensitive features,thereby offering precise estimation.展开更多
The state of health SoH of lithium ion batteries plays a predominant role in ensuring the safe and reliable operation of electric vehicles.In this,a novel SoH estimation approach using support vector regression with a...The state of health SoH of lithium ion batteries plays a predominant role in ensuring the safe and reliable operation of electric vehicles.In this,a novel SoH estimation approach using support vector regression with a Gaussian kernel optimized using the Bayesian optimization technique(BO-SVR with a Gaussian kernel)was proposed.Unlike,traditional approaches that use the internal resistance,and battery capacity as input parameters,this study utilized the equivalent discharging voltage difference interval and equivalent charging voltage difference interval,as they capture the dynamic voltage characteristics associated with the battery degradation.The model was simulated using MATLAB 2023a.The mean absolute error,R^(2),root mean squared error,and mean squared error were considered as performance indicators.The simulation results indicated that the proposed BO-SVR with a Gaussian kernel model had superior performance to other kernel SVR and Gaussian Process Regression models,with a reduced RMSE of 0.0082,thus demonstrating its potential to predict the SoH more accurately.展开更多
The increasing integration of lithium-ion batteries in electric vehicles has spurred extensive research aimed at enhancing the safety and efficiency of battery management systems.A fundamental component of battery man...The increasing integration of lithium-ion batteries in electric vehicles has spurred extensive research aimed at enhancing the safety and efficiency of battery management systems.A fundamental component of battery management system functionality is the accurate estimation of the state of health(SOH),which is critical for ensuring the dependable and safe operation of electric vehicles.To address this challenge,we introduce FCM-CNN-WNN-WBILSTM-AM,a time-series prediction framework specifically designed to improve SOH estimation accuracy for Li-ion batteries.The proposed framework starts with a preprocessing phase using fuzzy c-means(FCM)clustering to group batteries with similar characteristics,enabling more precise and customized predictions.It then employs a convolutional neural network(CNN)for initial feature extraction,followed by a wavelet neural network(WNN)layer to handle the non-stationary nature of battery degradation.A wavelet bidirectional long short-term memory(WBILSTM)layer further enhances time-series analysis by capturing both past and future dependencies.To refine feature selection and improve predictive accuracy,an attention mechanism(AM)is integrated,ensuring the model focuses on the most relevant information.To improve computational efficiency and ensure global optimization,the FCM-CNN-WNN-WBILSTM-AM framework employs the RMSprop optimizer,replacing the commonly used Adagrad optimizer.The experimental validation,utilizing multi-battery datasets from the National Aeronautics and Space Administration and the Center for Advanced Life Cycle Engineering repositories,demonstrates the model’s effectiveness in accurately capturing SOH trends and degradation patterns.The results indicate a significant enhancement in SOH estimation accuracy,achieving a root-mean-squared error of 0.0013 and a mean absolute percentage error of 0.0015.These outcomes highlight the model’s superior predictive performance and reduced error rates compared to existing methodologies.Ultimately,the proposed advancements contribute to improving the reliability and longevity of electric vehicle batteries,promoting the widespread adoption and sustainability of electric mobility solutions.展开更多
The accurate state of health(SOH)estimation of lithium-ion batteries is crucial for efficient,healthy,and safe operation of battery systems.Extracting meaningful aging information from highly stochastic and noisy data...The accurate state of health(SOH)estimation of lithium-ion batteries is crucial for efficient,healthy,and safe operation of battery systems.Extracting meaningful aging information from highly stochastic and noisy data segments while designing SOH estimation algorithms that efficiently handle the large-scale computational demands of cloud-based battery management systems presents a substantial challenge.In this work,we propose a quantum convolutional neural network(QCNN)model designed for accurate,robust,and generalizable SOH estimation with minimal data and parameter requirements and is compatible with quantum computing cloud platforms in the Noisy Intermediate-Scale Quantum.First,we utilize data from 4 datasets comprising 272 cells,covering 5 chemical compositions,4 rated parameters,and 73operating conditions.We design 5 voltage windows as small as 0.3 V for each cell from incremental capacity peaks for stochastic SOH estimation scenarios generation.We extract 3 effective health indicators(HIs)sequences and develop an automated feature fusion method using quantum rotation gate encoding,achieving an R2of 96%.Subsequently,we design a QCNN whose convolutional layer,constructed with variational quantum circuits,comprises merely 39 parameters.Additionally,we explore the impact of training set size,using strategies,and battery materials on the model’s accuracy.Finally,the QCNN with quantum convolutional layers reduces root mean squared error by 28% and achieves an R^(2)exceeding 96% compared to other three commonly used algorithms.This work demonstrates the effectiveness of quantum encoding for automated feature fusion of HIs extracted from limited discharge data.It highlights the potential of QCNN in improving the accuracy,robustness,and generalization of SOH estimation while dealing with stochastic and noisy data with few parameters and simple structure.It also suggests a new paradigm for leveraging quantum computational power in SOH estimation.展开更多
Accurate state of health(SOH)estimation is a cornerstone for ensuring the safety,performance and longevity of lithium-ion batteries,especially in electric vehicle(EV)applications.While numerous studies have demonstrat...Accurate state of health(SOH)estimation is a cornerstone for ensuring the safety,performance and longevity of lithium-ion batteries,especially in electric vehicle(EV)applications.While numerous studies have demonstrated the significant advantages of data-driven methods in SOH estimation,most rely on laboratory-standardized test data.This raises concerns about the generalization and robustness of the models under real-world operating conditions,where batteries undergo irregular driving patterns,incomplete charging cycles,and unpredictable environments.Notably,real-world EV data reflects the coupling between battery aging characteristics and actual operating conditions,providing an unprecedented perspective for developing SOH estimation models.This review provides a comprehensive and systematic overview of data-driven SOH estimation using real-world data,a topic that has received increasing attention but lacks a consolidated research framework.The paper begins by reviewing the established SOH estimation methodologies and points out the specific challenges arising from the transition to real-world data.It then probes practical issues across the pipeline:data pre-processing for anomalies,solutions for the lack of labels,feature extraction from complex operating data,machine learning model construction,and performance evaluation across various system deployments.Key insights are presented on how to handle noisy,unlabeled,and heterogeneous data using robust modeling strategies.Moreover,a valuable extension focusing on applying the advancements to battery reuse and recycling is discussed,with the goal of developing a whole lifecycle health diagnosis framework.The paper concludes with promising prospects,encompassing open-source standardized dataset establishment,weakly supervised learning,physics-reinforced modeling,real-world deployment,and advanced sensing technology,emphasizing that real-world data makes the transition of data-driven methods from theoretical validation to industrial deployment promising.This paper aims to assist researchers and practitioners in navigating the complexities of real-world SOH estimation,accelerating the collaborative innovation and industrial adoption in battery health management.展开更多
To ensure a long-term safety and reliability of electric vehicle and energy storage system,an accurate estimation of the state of health(SOH)for lithium-ion battery is important.In this study,a method for estimating t...To ensure a long-term safety and reliability of electric vehicle and energy storage system,an accurate estimation of the state of health(SOH)for lithium-ion battery is important.In this study,a method for estimating the lithium-ion battery SOH was proposed based on an improved extreme learning machine(ELM).Input weights and hidden layer biases were generated randomly in traditional ELM.To improve the estimation accuracy of ELM,the differential evolution algorithm was used to optimize these parameters in feasible solution spaces.First,incremental capacity curves were obtained by incremental capacity analysis and smoothed by Gaussian filter to extract health interests.Then,the ELM based on differential evolution algorithm(DE-ELM model)was used for a lithium-ion battery SOH estimation.At last,four battery historical aging data sets and one random walk data set were employed to validate the prediction performance of DE-ELM model.Results show that the DE-ELM has a better performance than other studied algorithms in terms of generalization ability.展开更多
This study proposes a multimodal deep learning framework for joint prediction of the state of health(SOH)and remaining useful life(RUL)of lithium-ion batteries.Twelve representative impedance features-covering charge-...This study proposes a multimodal deep learning framework for joint prediction of the state of health(SOH)and remaining useful life(RUL)of lithium-ion batteries.Twelve representative impedance features-covering charge-transfer resistance,solid electrolyte interface(SEI)layer impedance,and ion diffusion-are extracted from electrochemical impedance spectroscopy(EIS)and combined with short voltage/current segments to form a compact,interpretable feature set.A residual multi-layer perceptron(ResMLP)is employed for SOH regression,and a temporal convolutional network with attention(TCNAttention)is used for RUL estimation.Lifetime experiments on two battery types with different chemistries and form factors,evaluated through three rounds of paired cross-validation,validate the approach.Results show that the proposed features significantly reduce dimensionality and computational cost while substantially lowering SOH error,achieving an average normalized root mean square error of 2.3%.The RUL prediction reaches an average error of 14.8%.Overall,the framework balances interpretability,robustness,and feasibility,providing a practical solution for battery management systems(BMS)monitoring and life prediction.展开更多
Efficient and accurate health state estimation is crucial for lithium-ion battery(LIB)performance monitoring and economic evaluation.Effectively estimating the health state of LIBs online is the key but is also the mo...Efficient and accurate health state estimation is crucial for lithium-ion battery(LIB)performance monitoring and economic evaluation.Effectively estimating the health state of LIBs online is the key but is also the most difficult task for energy storage systems.With high adaptability and applicability advantages,battery health state estimation based on data-driven techniques has attracted extensive attention from researchers around the world.Artificial neural network(ANN)-based methods are often used for state estimations of LIBs.As one of the ANN methods,the Elman neural network(ENN)model has been improved to estimate the battery state more efficiently and accurately.In this paper,an improved ENN estimation method based on electrochemical impedance spectroscopy(EIS)and cuckoo search(CS)is established as the EIS-CS-ENN model to estimate the health state of LIBs.Also,the paper conducts a critical review of various ANN models against the EIS-CS-ENN model.This demonstrates that the EIS-CS-ENN model outperforms other models.The review also proves that,under the same conditions,selecting appropriate health indicators(HIs)according to the mathematical modeling ability and state requirements are the keys in estimating the health state efficiently.In the calculation process,several evaluation indicators are adopted to analyze and compare the modeling accuracy with other existing methods.Through the analysis of the evaluation results and the selection of HIs,conclusions and suggestions are put forward.Also,the robustness of the EIS-CS-ENN model for the health state estimation of LIBs is verified.展开更多
This article discussed the feasibility of assessing health state by detecting redox state of human body. Firstly, the balance of redox state is the basis of homeostasis, and the balance ability of redox can reflect he...This article discussed the feasibility of assessing health state by detecting redox state of human body. Firstly, the balance of redox state is the basis of homeostasis, and the balance ability of redox can reflect health state of human body. Secondly, the redox state of human body is a sensitive index of multiple risk factors of health such as age, external environment and psychological factors. It participates in the occurrence and development of multiple diseases involving metabolic diseases and nervous system diseases, and can serve as a cut-in point for treatment of these diseases. Detecting the redox state of high risk people is significantly important for early detection and treatment of disease. The blood plasma and urine could be selected to detect, which is convenient. It is pointed that the indexes not only involve oxidation product and antioxidant enzyme but also redox couple. Chinese medicine constitution reflects the state of body itself and the ability of adapting to external environment, which is consistent with the connotation of health. It is found that there are nine basic types of constitution in Chinese population, which provides a theoretical basis of health preservation, preventive treatment of disease and personalized treatment. With the combination of redox state detection and the Chinese medicine constitution theory, the heath state can be systemically assessed by conducting large-scale epidemiological survey with classified detection on redox state of human body.展开更多
In the field of energy storage,it is very important to predict the state of charge and the state of health of lithium-ion batteries.In this paper,we review the current widely used equivalent circuit and electrochemica...In the field of energy storage,it is very important to predict the state of charge and the state of health of lithium-ion batteries.In this paper,we review the current widely used equivalent circuit and electrochemical models for battery state predictions.The review demonstrates that machine learning and deep learning approaches can be used to construct fast and accurate data-driven models for the prediction of battery performance.The details,advantages,and limitations of these approaches are presented,compared,and summarized.Finally,future key challenges and opportunities are discussed.展开更多
With the rapid development of new energy technologies, lithium batteries are widely used in the field of energy storage systems and electric vehicles. The accurate prediction for the state of health(SOH) has an import...With the rapid development of new energy technologies, lithium batteries are widely used in the field of energy storage systems and electric vehicles. The accurate prediction for the state of health(SOH) has an important role in maintaining a safe and stable operation of lithium-ion batteries. To address the problems of uncertain battery discharge conditions and low SOH estimation accuracy in practical applications, this paper proposes a SOH estimation method based on constant-current battery charging section characteristics with a back-propagation neural network with an improved atom search optimization algorithm. A temperature characteristic, equal-time temperature variation(Dt_DT), is proposed by analyzing the temperature data of the battery charging section with the incremental capacity(IC) characteristics obtained from an IC analysis as an input to the data-driven prediction model. Testing and analysis of the proposed prediction model are carried out using publicly available datasets. Experimental results show that the maximum error of SOH estimation results for the proposed method in this paper is below 1.5%.展开更多
Though the lithium-ion battery is universally applied,the reliability of lithium-ion batteries remains a challenge due to various physicochemical reactions,electrode material degradation,and even thermal runaway.Accur...Though the lithium-ion battery is universally applied,the reliability of lithium-ion batteries remains a challenge due to various physicochemical reactions,electrode material degradation,and even thermal runaway.Accurate estimation and prediction of battery health conditions are crucial for battery safety management.In this paper,an end-cloud collaboration method is proposed to approach the track of battery degradation process,integrating end-side empirical model with cloud-side data-driven model.Based on ensemble learning methods,the data-driven model is constructed by three base models to obtain cloud-side highly accurate results.The double exponential decay model is utilized as an empirical model to output highly real-time prediction results.With Kalman filter,the prediction results of end-side empirical model can be periodically updated by highly accurate results of cloud-side data-driven model to obtain highly accurate and real-time results.Subsequently,the whole framework can give an accurate prediction and tracking of battery degradation,with the mean absolute error maintained below 2%.And the execution time on the end side can reach 261μs.The proposed end-cloud collaboration method has the potential to approach highly accurate and highly real-time estimation for battery health conditions during battery full life cycle in architecture of cyber hierarchy and interactional network.展开更多
Using electric vehicles(EVs)for transportation is considered as a necessary component for managing sustainable development and environmental issues.The present concerns regarding the environment,such as rapid fossil f...Using electric vehicles(EVs)for transportation is considered as a necessary component for managing sustainable development and environmental issues.The present concerns regarding the environment,such as rapid fossil fuel depletion,increases in air pollution,accelerating energy demands,global warming,and climate change,have paved the way for the electrification of the transport sector.EVs can address all of the aforementioned issues.Portable power supplies have become the lifeline of the EV world,especially lithium-ion(Li-ion)batteries.Li-ion batteries have attracted considerable attention in the EV industry,owing to their high energy density,power density,lifespan,nominal voltage,and cost.One major issue with such batteries concerns providing a quick and accurate estimation of a battery’s state and health;therefore,accurate determinations of the battery’S performance and health,as well as an accurate prediction of its life,are necessary to ensure reliability and efficiency.This study conducts a review of the technological briefs of EVs and their types,as well as the corresponding battery characteristics.Various aspects of recent research and developments in Li-ion battery prognostics and health monitoring are summarized,along with the techniques,algorithms,and models used for current/voltage estimations,state-of-charge(SoC)estimations,capacity estimations,and remaining-useful-life predictions.展开更多
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 ac...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.展开更多
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 ...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.展开更多
基金supported by the Natural Science Foundation of China underGrant 61833016 and 61873293the Shaanxi OutstandingYouth Science Foundation underGrant 2020JC-34the Shaanxi Science and Technology Innovation Team under Grant 2022TD-24.
文摘In industrial production and engineering operations,the health state of complex systems is critical,and predicting it can ensure normal operation.Complex systems have many monitoring indicators,complex coupling structures,non-linear and time-varying characteristics,so it is a challenge to establish a reliable prediction model.The belief rule base(BRB)can fuse observed data and expert knowledge to establish a nonlinear relationship between input and output and has well modeling capabilities.Since each indicator of the complex system can reflect the health state to some extent,the BRB is built based on the causal relationship between system indicators and the health state to achieve the prediction.A health state prediction model based on BRB and long short term memory for complex systems is proposed in this paper.Firstly,the LSTMis introduced to predict the trend of the indicators in the system.Secondly,the Density Peak Clustering(DPC)algorithmis used todetermine referential values of indicators for BRB,which effectively offset the lack of expert knowledge.Then,the predicted values and expert knowledge are fused to construct BRB to predict the health state of the systems by inference.Finally,the effectiveness of the model is verified by a case study of a certain vehicle hydraulic pump.
基金supported by National Natural Science Foundation of China(61074093,61473048,61233008)the Open Research Project from SKLMCCS(20150101)Youth Talent Support Plan of Changsha University of Science and Technology
基金supported by the Royal Academy of Engineering,UK,in the scheme of Distinguished International Associate(DIA-2424-5-134).
文摘Accurate estimation of the State of Charge(SOC),State of Health(SOH),and Terminal Resistance(TR)is crucial for the effective operation of Battery Management Systems(BMS)in lithium-ion batteries.This study conducts a comprehensive comparative analysis of four Kalman filter variants Extended Kalman Filter(EKF),Extended Kalman-Bucy Filter(EKBF),Unscented Kalman Filter(UKF),and Unscented Kalman-Bucy Filter(UKBF)under varying battery parameter conditions.These include temperature fluctuation,self-discharge,current direction,cell capacity,process noise,and measurement noise.Our findings reveal significant variations in the performance of SOC and SOH predictions across filters,emphasizing that UKF demonstrates superior robustness to noise,while EKF performs better under accurate system dynamics.The study underscores the need for adaptive filtering strategies that can dynamically adjust to evolving battery parameters,thereby enhancing BMS reliability and extending battery lifespan.
基金Research on Robust Decision and Full Stack Optimisation Techniques for Cloud Edge Intelligent Systems for Substation Inspection,Grant/Award Number:52550022001J。
文摘Predicting the future health state of a transformer can offer early warning of latent defects and faults within the transformer,thereby facilitating the formulation of power outage maintenance plans and power dispatch strategies.However,existing prediction methods based on the structure of‘splicing prediction and diagnosis method’suffer from limitations such as inability to achieve global optimality,error accumulation,and low prediction accuracy.To fill this gap,a novel direct prediction method of a trans-former state based on knowledge and data fusion-driven model(K&DFDM)is pro-posed in this paper.Firstly,a state quantity data space is constructed to comprehensively reflect the changes in the health state of the transformer over time,encompassing online monitoring,offline testing,evaluation results,and actual operation data.After that,correlation knowledge between state quantities,fault diagnosis mechanism knowledge,current diagnosis experience knowledge,and uncertain fuzzy knowledge are extracted separately.The actual fault mechanism,existing expert experience,and other knowledge in the diagnosis process are quantified.Then,the attention model is sub-sequently optimised,leveraging quantitative knowledge to effectively constrain and guide the data prediction process.Incorporating fault diagnosis mechanism knowledge into the data prediction model enables the achievement of global optimisation in both diagnosis and prediction.The integration of traditional expert experience knowledge and the correlation knowledge between state quantities serves as constraints during the process of attaining the global optimum.The verification results,comprising 327 cases,demonstrate that K&DFDM effectively addresses the issue of error superposition encountered by existing state prediction methods,leading to a direct state prediction accuracy of 96.33%.
基金supported by the National Natural Science Foundation of China(NSFC)under Grant(No.51677058).
文摘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%.
基金financially supported by the National Natural Science Foundation of China(22273096)the International Postdoctoral Exchange Fellowship Program between Helmholtz and OCPC(ZD2023019)+1 种基金the Young Scientists Fund of the National Natural Science Foundation of China(Grant No.22409139)the Sichuan Provincial Natural Science Foundation for Young Scientists(24NSFSC607)。
文摘Lithium-ion batteries are essential for renewable energy storage,necessitating efficient battery management systems(BMS)for optimal performance and longevity.Accurate estimation of the state of health(SOH)is crucial for BMS safety,yet current machine learning-based SOH estimation relying on global aging features often overlooks localized degradation patterns.In this study,we introduce a novel SOH estimation pipeline that integrates voltage-range-specific segmentation with a multi-stage,crossvalidation-driven localized feature-selection framework and a feature-augmented dual-stream fusion network.Our methodology partitions full-range voltage into localized intervals to construct a degradation-sensitive feature library,from which 4 optimal features are identified from a set of 336 candidates.These selected features are combined with raw voltage signals via a dual-stream architecture that employs a dynamic gating mechanism to recalibrate feature contributions during training.Crossvalidation-based evaluation on datasets encompassing different chemistries and charge/discharge protocols demonstrate that our approach can achieve lower average root-mean-square-error(Oxford dataset:0.7201%,Massachusetts Institute of Technology(MIT)dataset:0.7184%)compared to baseline models.An in-depth analysis of the physical significance of the screened features improves the interpretability of the features.This work underscores the significant potential of leveraging localized feature enhancement in SOH estimation by systematically integrating degradation-sensitive features,thereby offering precise estimation.
基金supported by the Royal Academy of Engineering,UK,under the scheme of Distinguished International Associates(DIA-2424-5-134).
文摘The state of health SoH of lithium ion batteries plays a predominant role in ensuring the safe and reliable operation of electric vehicles.In this,a novel SoH estimation approach using support vector regression with a Gaussian kernel optimized using the Bayesian optimization technique(BO-SVR with a Gaussian kernel)was proposed.Unlike,traditional approaches that use the internal resistance,and battery capacity as input parameters,this study utilized the equivalent discharging voltage difference interval and equivalent charging voltage difference interval,as they capture the dynamic voltage characteristics associated with the battery degradation.The model was simulated using MATLAB 2023a.The mean absolute error,R^(2),root mean squared error,and mean squared error were considered as performance indicators.The simulation results indicated that the proposed BO-SVR with a Gaussian kernel model had superior performance to other kernel SVR and Gaussian Process Regression models,with a reduced RMSE of 0.0082,thus demonstrating its potential to predict the SoH more accurately.
文摘The increasing integration of lithium-ion batteries in electric vehicles has spurred extensive research aimed at enhancing the safety and efficiency of battery management systems.A fundamental component of battery management system functionality is the accurate estimation of the state of health(SOH),which is critical for ensuring the dependable and safe operation of electric vehicles.To address this challenge,we introduce FCM-CNN-WNN-WBILSTM-AM,a time-series prediction framework specifically designed to improve SOH estimation accuracy for Li-ion batteries.The proposed framework starts with a preprocessing phase using fuzzy c-means(FCM)clustering to group batteries with similar characteristics,enabling more precise and customized predictions.It then employs a convolutional neural network(CNN)for initial feature extraction,followed by a wavelet neural network(WNN)layer to handle the non-stationary nature of battery degradation.A wavelet bidirectional long short-term memory(WBILSTM)layer further enhances time-series analysis by capturing both past and future dependencies.To refine feature selection and improve predictive accuracy,an attention mechanism(AM)is integrated,ensuring the model focuses on the most relevant information.To improve computational efficiency and ensure global optimization,the FCM-CNN-WNN-WBILSTM-AM framework employs the RMSprop optimizer,replacing the commonly used Adagrad optimizer.The experimental validation,utilizing multi-battery datasets from the National Aeronautics and Space Administration and the Center for Advanced Life Cycle Engineering repositories,demonstrates the model’s effectiveness in accurately capturing SOH trends and degradation patterns.The results indicate a significant enhancement in SOH estimation accuracy,achieving a root-mean-squared error of 0.0013 and a mean absolute percentage error of 0.0015.These outcomes highlight the model’s superior predictive performance and reduced error rates compared to existing methodologies.Ultimately,the proposed advancements contribute to improving the reliability and longevity of electric vehicle batteries,promoting the widespread adoption and sustainability of electric mobility solutions.
基金funded by the Research on SOC/SOH Joint Estimation Technology of Electric Vehicle Battery System State Based on Online Parameter Identification Project(2019)the National Natural Science Foundation of China(Grant No.51877120)。
文摘The accurate state of health(SOH)estimation of lithium-ion batteries is crucial for efficient,healthy,and safe operation of battery systems.Extracting meaningful aging information from highly stochastic and noisy data segments while designing SOH estimation algorithms that efficiently handle the large-scale computational demands of cloud-based battery management systems presents a substantial challenge.In this work,we propose a quantum convolutional neural network(QCNN)model designed for accurate,robust,and generalizable SOH estimation with minimal data and parameter requirements and is compatible with quantum computing cloud platforms in the Noisy Intermediate-Scale Quantum.First,we utilize data from 4 datasets comprising 272 cells,covering 5 chemical compositions,4 rated parameters,and 73operating conditions.We design 5 voltage windows as small as 0.3 V for each cell from incremental capacity peaks for stochastic SOH estimation scenarios generation.We extract 3 effective health indicators(HIs)sequences and develop an automated feature fusion method using quantum rotation gate encoding,achieving an R2of 96%.Subsequently,we design a QCNN whose convolutional layer,constructed with variational quantum circuits,comprises merely 39 parameters.Additionally,we explore the impact of training set size,using strategies,and battery materials on the model’s accuracy.Finally,the QCNN with quantum convolutional layers reduces root mean squared error by 28% and achieves an R^(2)exceeding 96% compared to other three commonly used algorithms.This work demonstrates the effectiveness of quantum encoding for automated feature fusion of HIs extracted from limited discharge data.It highlights the potential of QCNN in improving the accuracy,robustness,and generalization of SOH estimation while dealing with stochastic and noisy data with few parameters and simple structure.It also suggests a new paradigm for leveraging quantum computational power in SOH estimation.
基金supported by the National Natural Science Foundation of China(52375144 and 52205153)the Shanghai Pujiang Programme(23PJD019)the Shanghai Gaofeng Project for University Academic Program Development。
文摘Accurate state of health(SOH)estimation is a cornerstone for ensuring the safety,performance and longevity of lithium-ion batteries,especially in electric vehicle(EV)applications.While numerous studies have demonstrated the significant advantages of data-driven methods in SOH estimation,most rely on laboratory-standardized test data.This raises concerns about the generalization and robustness of the models under real-world operating conditions,where batteries undergo irregular driving patterns,incomplete charging cycles,and unpredictable environments.Notably,real-world EV data reflects the coupling between battery aging characteristics and actual operating conditions,providing an unprecedented perspective for developing SOH estimation models.This review provides a comprehensive and systematic overview of data-driven SOH estimation using real-world data,a topic that has received increasing attention but lacks a consolidated research framework.The paper begins by reviewing the established SOH estimation methodologies and points out the specific challenges arising from the transition to real-world data.It then probes practical issues across the pipeline:data pre-processing for anomalies,solutions for the lack of labels,feature extraction from complex operating data,machine learning model construction,and performance evaluation across various system deployments.Key insights are presented on how to handle noisy,unlabeled,and heterogeneous data using robust modeling strategies.Moreover,a valuable extension focusing on applying the advancements to battery reuse and recycling is discussed,with the goal of developing a whole lifecycle health diagnosis framework.The paper concludes with promising prospects,encompassing open-source standardized dataset establishment,weakly supervised learning,physics-reinforced modeling,real-world deployment,and advanced sensing technology,emphasizing that real-world data makes the transition of data-driven methods from theoretical validation to industrial deployment promising.This paper aims to assist researchers and practitioners in navigating the complexities of real-world SOH estimation,accelerating the collaborative innovation and industrial adoption in battery health management.
文摘To ensure a long-term safety and reliability of electric vehicle and energy storage system,an accurate estimation of the state of health(SOH)for lithium-ion battery is important.In this study,a method for estimating the lithium-ion battery SOH was proposed based on an improved extreme learning machine(ELM).Input weights and hidden layer biases were generated randomly in traditional ELM.To improve the estimation accuracy of ELM,the differential evolution algorithm was used to optimize these parameters in feasible solution spaces.First,incremental capacity curves were obtained by incremental capacity analysis and smoothed by Gaussian filter to extract health interests.Then,the ELM based on differential evolution algorithm(DE-ELM model)was used for a lithium-ion battery SOH estimation.At last,four battery historical aging data sets and one random walk data set were employed to validate the prediction performance of DE-ELM model.Results show that the DE-ELM has a better performance than other studied algorithms in terms of generalization ability.
基金financially supported by the National Natural Science Foundation of China(No.U22A20439)the Shenzhen Fundamental Research Program(No.JCYJ20220818100418040)+2 种基金the Guangdong-Hong Kong-Macao Joint Innovation Fund(No.2024A0505040001)the Guangdong Basic and Applied Basic Research Foundation(2023A1515011122)the Shenzhen ShowMac Network Technology Co.,Ltd.
文摘This study proposes a multimodal deep learning framework for joint prediction of the state of health(SOH)and remaining useful life(RUL)of lithium-ion batteries.Twelve representative impedance features-covering charge-transfer resistance,solid electrolyte interface(SEI)layer impedance,and ion diffusion-are extracted from electrochemical impedance spectroscopy(EIS)and combined with short voltage/current segments to form a compact,interpretable feature set.A residual multi-layer perceptron(ResMLP)is employed for SOH regression,and a temporal convolutional network with attention(TCNAttention)is used for RUL estimation.Lifetime experiments on two battery types with different chemistries and form factors,evaluated through three rounds of paired cross-validation,validate the approach.Results show that the proposed features significantly reduce dimensionality and computational cost while substantially lowering SOH error,achieving an average normalized root mean square error of 2.3%.The RUL prediction reaches an average error of 14.8%.Overall,the framework balances interpretability,robustness,and feasibility,providing a practical solution for battery management systems(BMS)monitoring and life prediction.
基金supported by the National Natural Science Foundation of China(No.62173281 and No.61801407)the Sichuan Science and Technology Pro-gram(No.2019YFG0427 and No.2023YFG0108)+1 种基金the China Scholarship Council(No.201908515099)the Fund of Robot Technology used for the Special Environment Key Laboratory of Sichuan Province(No.18kftk03).
文摘Efficient and accurate health state estimation is crucial for lithium-ion battery(LIB)performance monitoring and economic evaluation.Effectively estimating the health state of LIBs online is the key but is also the most difficult task for energy storage systems.With high adaptability and applicability advantages,battery health state estimation based on data-driven techniques has attracted extensive attention from researchers around the world.Artificial neural network(ANN)-based methods are often used for state estimations of LIBs.As one of the ANN methods,the Elman neural network(ENN)model has been improved to estimate the battery state more efficiently and accurately.In this paper,an improved ENN estimation method based on electrochemical impedance spectroscopy(EIS)and cuckoo search(CS)is established as the EIS-CS-ENN model to estimate the health state of LIBs.Also,the paper conducts a critical review of various ANN models against the EIS-CS-ENN model.This demonstrates that the EIS-CS-ENN model outperforms other models.The review also proves that,under the same conditions,selecting appropriate health indicators(HIs)according to the mathematical modeling ability and state requirements are the keys in estimating the health state efficiently.In the calculation process,several evaluation indicators are adopted to analyze and compare the modeling accuracy with other existing methods.Through the analysis of the evaluation results and the selection of HIs,conclusions and suggestions are put forward.Also,the robustness of the EIS-CS-ENN model for the health state estimation of LIBs is verified.
基金Supported by Major Project of Chinese National Programs for Fundamental Research and Development(973 Program,No.2011CB505405)Young Scientist Project of National Natural Science Foundation of China(No.81102526)Key Program of National Natural Science Foundation of China(No.81030064)
文摘This article discussed the feasibility of assessing health state by detecting redox state of human body. Firstly, the balance of redox state is the basis of homeostasis, and the balance ability of redox can reflect health state of human body. Secondly, the redox state of human body is a sensitive index of multiple risk factors of health such as age, external environment and psychological factors. It participates in the occurrence and development of multiple diseases involving metabolic diseases and nervous system diseases, and can serve as a cut-in point for treatment of these diseases. Detecting the redox state of high risk people is significantly important for early detection and treatment of disease. The blood plasma and urine could be selected to detect, which is convenient. It is pointed that the indexes not only involve oxidation product and antioxidant enzyme but also redox couple. Chinese medicine constitution reflects the state of body itself and the ability of adapting to external environment, which is consistent with the connotation of health. It is found that there are nine basic types of constitution in Chinese population, which provides a theoretical basis of health preservation, preventive treatment of disease and personalized treatment. With the combination of redox state detection and the Chinese medicine constitution theory, the heath state can be systemically assessed by conducting large-scale epidemiological survey with classified detection on redox state of human body.
基金funding support from the Department of Science and Technology of Guangdong Province(2019A050510043)the Department of Science and Technology of Zhuhai City(ZH22017001200059PWC)+1 种基金the National Natural Science Foundation of China(2210050123)the China Postdoctoral Science Foundation(2021TQ0161 and 2021M691709)。
文摘In the field of energy storage,it is very important to predict the state of charge and the state of health of lithium-ion batteries.In this paper,we review the current widely used equivalent circuit and electrochemical models for battery state predictions.The review demonstrates that machine learning and deep learning approaches can be used to construct fast and accurate data-driven models for the prediction of battery performance.The details,advantages,and limitations of these approaches are presented,compared,and summarized.Finally,future key challenges and opportunities are discussed.
基金supported by National Natural Science Foundation of China (Grant No. 51677058)。
文摘With the rapid development of new energy technologies, lithium batteries are widely used in the field of energy storage systems and electric vehicles. The accurate prediction for the state of health(SOH) has an important role in maintaining a safe and stable operation of lithium-ion batteries. To address the problems of uncertain battery discharge conditions and low SOH estimation accuracy in practical applications, this paper proposes a SOH estimation method based on constant-current battery charging section characteristics with a back-propagation neural network with an improved atom search optimization algorithm. A temperature characteristic, equal-time temperature variation(Dt_DT), is proposed by analyzing the temperature data of the battery charging section with the incremental capacity(IC) characteristics obtained from an IC analysis as an input to the data-driven prediction model. Testing and analysis of the proposed prediction model are carried out using publicly available datasets. Experimental results show that the maximum error of SOH estimation results for the proposed method in this paper is below 1.5%.
基金financially supported by the National Natural Science Foundation of China(No.52102470)。
文摘Though the lithium-ion battery is universally applied,the reliability of lithium-ion batteries remains a challenge due to various physicochemical reactions,electrode material degradation,and even thermal runaway.Accurate estimation and prediction of battery health conditions are crucial for battery safety management.In this paper,an end-cloud collaboration method is proposed to approach the track of battery degradation process,integrating end-side empirical model with cloud-side data-driven model.Based on ensemble learning methods,the data-driven model is constructed by three base models to obtain cloud-side highly accurate results.The double exponential decay model is utilized as an empirical model to output highly real-time prediction results.With Kalman filter,the prediction results of end-side empirical model can be periodically updated by highly accurate results of cloud-side data-driven model to obtain highly accurate and real-time results.Subsequently,the whole framework can give an accurate prediction and tracking of battery degradation,with the mean absolute error maintained below 2%.And the execution time on the end side can reach 261μs.The proposed end-cloud collaboration method has the potential to approach highly accurate and highly real-time estimation for battery health conditions during battery full life cycle in architecture of cyber hierarchy and interactional network.
基金by Department of Science and Technology,New Delhi(Indo-Norway consortium)project entitled“Integrated Renewable Resources and Storage Operation and Management”program.
文摘Using electric vehicles(EVs)for transportation is considered as a necessary component for managing sustainable development and environmental issues.The present concerns regarding the environment,such as rapid fossil fuel depletion,increases in air pollution,accelerating energy demands,global warming,and climate change,have paved the way for the electrification of the transport sector.EVs can address all of the aforementioned issues.Portable power supplies have become the lifeline of the EV world,especially lithium-ion(Li-ion)batteries.Li-ion batteries have attracted considerable attention in the EV industry,owing to their high energy density,power density,lifespan,nominal voltage,and cost.One major issue with such batteries concerns providing a quick and accurate estimation of a battery’s state and health;therefore,accurate determinations of the battery’S performance and health,as well as an accurate prediction of its life,are necessary to ensure reliability and efficiency.This study conducts a review of the technological briefs of EVs and their types,as well as the corresponding battery characteristics.Various aspects of recent research and developments in Li-ion battery prognostics and health monitoring are summarized,along with the techniques,algorithms,and models used for current/voltage estimations,state-of-charge(SoC)estimations,capacity estimations,and remaining-useful-life predictions.
基金funded by the “SMART BATTERY” project, granted by Villum Foundation in 2021 (project number 222860)。
文摘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.
基金supported by the National Natural Science Foundation of China (No.62173281,52377217,U23A20651)Sichuan Science and Technology Program (No.24NSFSC0024,23ZDYF0734,23NSFSC1436)+2 种基金Dazhou City School Cooperation Project (No.DZXQHZ006)Technopole Talent Summit Project (No.KJCRCFH08)Robert Gordon University。
文摘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.
