Dear Editor,Health management is essential to ensure battery performance and safety, while data-driven learning system is a promising solution to enable efficient state of health(SoH) estimation of lithium-ion(Liion) ...Dear Editor,Health management is essential to ensure battery performance and safety, while data-driven learning system is a promising solution to enable efficient state of health(SoH) estimation of lithium-ion(Liion) batteries. However, the time-consuming signal data acquisition and the lack of interpretability of model still hinder its efficient deployment. Motivated by this, this letter proposes a novel and interpretable data-driven learning strategy through combining the benefits of explainable AI and non-destructive ultrasonic detection for battery SoH estimation. Specifically, after equipping battery with advanced ultrasonic sensor to promise fast real-time ultrasonic signal measurement, an interpretable data-driven learning strategy named generalized additive neural decision ensemble(GANDE) is designed to rapidly estimate battery SoH and explain the effects of the involved ultrasonic features of interest.展开更多
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....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.展开更多
For large-scale in-service electric vehicles(EVs)that undergo potential maintenance,second-hand transactions,and retirement,it is crucial to rapidly evaluate the health status of their battery packs.However,existing m...For large-scale in-service electric vehicles(EVs)that undergo potential maintenance,second-hand transactions,and retirement,it is crucial to rapidly evaluate the health status of their battery packs.However,existing methods often rely on lengthy battery charging/discharging data or extensive training samples,which hinders their implementation in practical scenarios.To address this issue,a rapid health estimation method based on short-time charging data and limited labels for in-service battery packs is proposed in this paper.First,a digital twin of battery pack is established to emulate its dynamic behavior across various aging levels and inconsistency degrees.Then,increment capacity sequences(△Q)within a short voltage span are extracted from charging process to indicate battery health.Furthermore,data-driven models based on deep convolutional neural network(DCNN)are constructed to estimate battery state of health(SOH),where the synthetic data is employed to pre-train the models,and transfer learning strategies by using fine-tuning and domain adaptation are utilized to enhance the model adaptability.Finally,field data of 10 EVs exhibiting different SOHs are used to verify the proposed methods.By using the△Q with 100 m V voltage change,the SOH of battery packs can be accurately estimated with an error around 3.2%.展开更多
Monitoring and evaluating the health parameters of marine gas turbine engine help in developing predictive control techniques and maintenance schedules.Because the health parameters are unmeasurable,researchers estima...Monitoring and evaluating the health parameters of marine gas turbine engine help in developing predictive control techniques and maintenance schedules.Because the health parameters are unmeasurable,researchers estimate them only based on the available measurement parameters.Kalman filter-based approaches are the most commonly used estimation approaches;how-ever,the conventional Kalman filter-based approaches have a poor robustness to the model uncertainty,and their ability to track the mutation condition is influenced by historical data.Therefore,in this paper,an improved Kalman filter-based algorithm called the strong tracking extended Kalman filter(STEKF)approach is proposed to estimate the gas turbine health parameters.The analytical expressions of Jacobian matrixes are deduced by non-equilibrium point analytical linearization to address the problem of the conventional approaches.The proposed approach was used to estimate the health parameters of a two-shaft marine gas turbine engine in the simulation environment and was compared with the extended Kalman filter(EKF)and the unscented Kalman filter(UKF).The results show that the STEKF approach not only has a computation cost similar to that of the EKF approach but also outperforms the EKF approach when the health parameters change abruptly and the noise mean value is not zero.展开更多
Lithium-ion batteries are the most widely accepted type of battery in the electric vehicle industry because of some of their positive inherent characteristics. However, the safety problems associated with inaccurate e...Lithium-ion batteries are the most widely accepted type of battery in the electric vehicle industry because of some of their positive inherent characteristics. However, the safety problems associated with inaccurate estimation and prediction of the state of health of these batteries have attracted wide attention due to the adverse negative effect on vehicle safety. In this paper, both machine and deep learning models were used to estimate the state of health of lithium-ion batteries. The paper introduces the definition of battery health status and its importance in the electric vehicle industry. Based on the data preprocessing and visualization analysis, three features related to actual battery capacity degradation are extracted from the data. Two learning models, SVR and LSTM were employed for the state of health estimation and their respective results are compared in this paper. The mean square error and coefficient of determination were the two metrics for the performance evaluation of the models. The experimental results indicate that both models have high estimation results. However, the metrics indicated that the SVR was the overall best model.展开更多
An accurate assessment of the state of health(SOH)is the cornerstone for guaranteeing the long-term stable operation of electrical equipment.However,the noise the data carries during cyclic aging poses a severe challe...An accurate assessment of the state of health(SOH)is the cornerstone for guaranteeing the long-term stable operation of electrical equipment.However,the noise the data carries during cyclic aging poses a severe challenge to the accuracy of SOH estimation and the generalization ability of the model.To this end,this paper proposed a novel SOH estimation model for lithium-ion batteries that incorporates advanced signal-processing techniques and optimized machine-learning strategies.The model employs a whale optimization algorithm(WOA)to seek the optimal parameter combination(K,α)for the variational modal decomposition(VMD)method to ensure that the signals are accurately decomposed into different modes representing the SOH of batteries.Then,the excellent local feature extraction capability of the convolutional neural network(CNN)was utilized to obtain the critical features of each modal of SOH.Finally,the support vector machine(SVM)was selected as the final SOH estimation regressor based on its generalization ability and efficient performance on small sample datasets.The method proposed was validated on a two-class publicly available aging dataset of lithium-ion batteries containing different temperatures,discharge rates,and discharge depths.The results show that the WOA-VMD-based data processing technique effectively solves the interference problem of cyclic aging data noise on SOH estimation.The CNN-SVM optimized machine learning method significantly improves the accuracy of SOH estimation.Compared with traditional techniques,the fused algorithm achieves significant results in solving the interference of data noise,improving the accuracy of SOH estimation,and enhancing the generalization ability.展开更多
Lithium-ion battery State of Health(SOH)estimation is an essential issue in battery management systems.In order to better estimate battery SOH,Extreme Learning Machine(ELM)is used to establish a model to estimate lith...Lithium-ion battery State of Health(SOH)estimation is an essential issue in battery management systems.In order to better estimate battery SOH,Extreme Learning Machine(ELM)is used to establish a model to estimate lithium-ion battery SOH.The Swarm Optimization algorithm(PSO)is used to automatically adjust and optimize the parameters of ELM to improve estimation accuracy.Firstly,collect cyclic aging data of the battery and extract five characteristic quantities related to battery capacity from the battery charging curve and increment capacity curve.Use Grey Relation Analysis(GRA)method to analyze the correlation between battery capacity and five characteristic quantities.Then,an ELM is used to build the capacity estimation model of the lithium-ion battery based on five characteristics,and a PSO is introduced to optimize the parameters of the capacity estimation model.The proposed method is validated by the degradation experiment of the lithium-ion battery under different conditions.The results show that the battery capacity estimation model based on ELM and PSO has better accuracy and stability in capacity estimation,and the average absolute percentage error is less than 1%.展开更多
Lithium-ion batteries have an essential characteristic in consumer electronics applications and electric mobility.However,predicting their lifetime performance is a difficult task due to the impact of operating and en...Lithium-ion batteries have an essential characteristic in consumer electronics applications and electric mobility.However,predicting their lifetime performance is a difficult task due to the impact of operating and environmental conditions.Additionally,state-of-health(SOH)and remaining-useful-life(RUL)predictions have developed into crucial components of the energy management system for lifetime prediction to guarantee the best possible performance.Due to the non-linear behaviour of the health prediction of electric vehicle batteries,the assessment of SOH and RUL has therefore become a core research challenge for both business and academics.This paper introduces a comprehensive analysis of the application of machine learning in the domain of electric vehicle battery management,emphasizing state prediction and ageing prognostics.The objective is to provide comprehensive information about the evaluation,categorization and multiple machine-learning algorithms for predicting the SOH and RUL.Additionally,lithium-ion bat-tery behaviour,the SOH estimation approach,key findings,advantages,challenges and potential of the battery management system for different state estimations are discussed.The study identifies the common challenges encountered in traditional battery manage-ment and provides a summary of how machine learning can be employed to address these challenges.展开更多
Accurate simulation of characteristics performance and state of health(SOH)estimation for lithium-ion batteries are critical for battery management systems(BMS)in electric vehicles.Battery simplified electrochemical m...Accurate simulation of characteristics performance and state of health(SOH)estimation for lithium-ion batteries are critical for battery management systems(BMS)in electric vehicles.Battery simplified electrochemical model(SEM)can achieve accurate estimation of battery terminal voltage with less computing resources.To ensure the applica-bility of life-cycle usage,degradation physics need to be involved in SEM models.This work conducts deep analysis on battery degradation physics and develops an aging-effect coupling model based on an existing improved single particle(ISP)model.Firstly,three mechanisms of solid electrolyte interface(SEI)film growth throughout life cycle are analyzed,and an SEI film growth model of lithium-ion battery is built coupled with the ISP model.Then,a series of identification conditions for individual cells are designed to non-destructively determine model parameters.Finally,battery aging experiment is designed to validate the battery performance simulation method and SOH estimation method.The validation results under different aging rates indicate that this method can accurately es-timate characteristics performance and SOH for lithium-ion batteries during the whole life cycle.展开更多
By social expenditure on health service(SEHS)we refer to the sum total of money paid by thewhole society during a certain period of year for the sake of preventing and treating diseases andof protecting and improving ...By social expenditure on health service(SEHS)we refer to the sum total of money paid by thewhole society during a certain period of year for the sake of preventing and treating diseases andof protecting and improving the people’s health.It reflects objectively the total level of SEHSduring a certain period;the levels of health service expenditures on the parts of the whole society,enterprises,and individuals;the ratio between SEHS and total social expenditure;and the ratiosof SEHS to gross national product and to national income.The article discussed and展开更多
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 t...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.展开更多
Data-driven models for battery state estimation require extensive experimental training data,which may not be available or suitable for specific tasks like open-circuit voltage(OCV)reconstruction and subsequent state ...Data-driven models for battery state estimation require extensive experimental training data,which may not be available or suitable for specific tasks like open-circuit voltage(OCV)reconstruction and subsequent state of health(SOH)estimation.This study addresses this issue by developing a transfer-learning-based OCV reconstruction model using a temporal convolutional long short-term memory(TCN-LSTM)network trained on synthetic data from an automotive nickel cobalt aluminium oxide(NCA)cell generated through a mechanistic model approach.The data consists of voltage curves at constant temperature,C-rates between C/30 to 1C,and a SOH-range from 70%to 100%.The model is refined via Bayesian optimization and then applied to four use cases with reduced experimental nickel manganese cobalt oxide(NMC)cell training data for higher use cases.The TL models’performances are compared with models trained solely on experimental data,focusing on different C-rates and voltage windows.The results demonstrate that the OCV reconstruction mean absolute error(MAE)within the average battery electric vehicle(BEV)home charging window(30%to 85%state of charge(SOC))is less than 22 mV for the first three use cases across all C-rates.The SOH estimated from the reconstructed OCV exhibits an mean absolute percentage error(MAPE)below 2.2%for these cases.The study further investigates the impact of the source domain on TL by incorporating two additional synthetic datasets,a lithium iron phosphate(LFP)cell and an entirely artificial,non-existing,cell,showing that solely the shifting and scaling of gradient changes in the charging curve suffice to transfer knowledge,even between different cell chemistries.A key limitation with respect to extrapolation capability is identified and evidenced in our fourth use case,where the absence of such comprehensive data hindered the TL process.展开更多
Open-circuit voltage(OCV)updates are the key to accurate state of charge(SOC)estimates over lifetime.Degradation modes(DM)are directly coupled to OCV estimation.They offer a more detailed analysis of the battery’s st...Open-circuit voltage(OCV)updates are the key to accurate state of charge(SOC)estimates over lifetime.Degradation modes(DM)are directly coupled to OCV estimation.They offer a more detailed analysis of the battery’s state of health(SOH)and yield optimized usage strategy,and with that,a prolonged lifetime.In this study two data-driven models are coupled with physics-based models and compared in regards of their OCV and DM estimation accuracy:Two temporal convolutional—long short-term memory neural networks(TCN-LSTM)are trained from synthetic NCA-graphite battery data for OCV curve estimation(model 1)and alignment parameter estimation(model 2).Both models are fine-tuned with varying amounts of experimental NMC-graphite battery data during the transfer learning(TL)step.In the subsequent physics-constraining part the DMs are derived via optimization(model 1),i.e.,fitting the OCV with half cell open-circuit potentials,or directly via mathematical equations(model 2).Both models prove that fine-tuning data from one aging path suffices,if it includes the maximum appearing DMs of the target domain.For these use cases both models maintain OCV mean absolute errors(MAEs),DM MAEs and SOH mean absolute percentage errors(MAPEs)under 10 mV,3.10%and 1.98%,respectively.The model 2 has less computational complexity and reaches slightly better results but requires labeled target data including alignment parameters for its application.This study shows that synthetic data is eligible for TL,even for varying cell chemistries,and that the mechanistic model helps to physically constrain the output.展开更多
The identification of latent risk factors that can induce to health risks or an abnormal status is an important task in healthcare data analyses.In recent years,health analyses based on neural network models have been...The identification of latent risk factors that can induce to health risks or an abnormal status is an important task in healthcare data analyses.In recent years,health analyses based on neural network models have been applied widely.However,such analysis processes are blackbox and the results lack explainability.Some approaches by constructing a domain model may tackle these issues.However,domain knowledge from an expert is required.In this study,we propose an exploratory and interpretable approach to estimating latent health risk factors without relying on domain knowledge,in which feature selection and causal discovery are used to construct a domain model for uncovering complex relationships in health and medical data.An evaluation experiment conducted on two datasets by comparing the proposed approach with four baselines demonstrated that the proposed approach outperformed the baselines in terms of model fitness.Furthermore,the number of model parameters in our method was smaller than that in the baselines,which reduced model complexity.Moreover,the analysis process of the proposed approach was visible and explainable,which improved the interpretability of the analysis processes.展开更多
At present,a life-cycle assessment of energy storage systems(ESSs)is not widely available in the literature.Such an assessment is increasingly vital nowadays as ESS is recognized as one of the important equipment in p...At present,a life-cycle assessment of energy storage systems(ESSs)is not widely available in the literature.Such an assessment is increasingly vital nowadays as ESS is recognized as one of the important equipment in power systems to reduce peak demands for deferring or avoiding augmentation in the network and power generation.As the battery cost is still very high at present,a comprehensive assessment is necessary to determine the optimum ESS capacity so that the maximum financial gain is achievable at the end of the batteries’lifespan.Therefore,an effective life-cycle assessment is proposed in this paper to show how the optimum ESS capacity can be determined such that the maximum net financial gain is achievable at the end of the batteries’lifespan when ESS is used to perform peak demand reductions for the customer or utility companies.The findings reveal the positive financial viability of ESS on the power grid,otherwise the projection of the financial viability is often seemingly poor due to the high battery cost with a short battery lifespan.An improved battery degradation model is used in this assessment,which can simulate the battery degradation accurately in a situation whereby the charging current,discharging current,and temperature of the batteries are intermittent on a site during peak demand reductions.This assessment is crucial to determine the maximum financial benefits brought by ESS.展开更多
With a growing consumer market of battery electric vehicles, customers＇ demand for technology and features is on the rise. The range and, to a certain extent, the range estimation will play a key factor in customers...With a growing consumer market of battery electric vehicles, customers＇ demand for technology and features is on the rise. The range and, to a certain extent, the range estimation will play a key factor in customers＇ purchase decisions. In order to guarantee a precise range estimation over the usage life of battery electric vehicles, a method is presented that combines adaptive filter algorithms with statistical approaches. The statistical approach uses recurring driving cycles over the lifetime in order to derive the aging status of the traction battery. It is implied that the variance of the energy usage of these driving cycles is within certain bounds. This fact should be proven by an experimental case study. The dataset used in this paper is open to the public.展开更多
Fault diagnosis is key to enhancing the performance and safety of battery storage systems.However,it is challenging to realize efficient fault diagnosis for lithium-ion batteries because the accuracy diagnostic algori...Fault diagnosis is key to enhancing the performance and safety of battery storage systems.However,it is challenging to realize efficient fault diagnosis for lithium-ion batteries because the accuracy diagnostic algorithm is limited and the features of the different faults are similar.The model-based method has been widely used for degradation mechanism analysis,state estimation,and life prediction of lithium-ion battery systems due to the fast speed and high development efficiency.This paper reviews the mainstream modeling approaches used for battery diagnosis.First,a review of the battery’s degradation mechanisms and the external factors affecting the aging rate is presented.Second,the different modeling approaches are summarized,from microscopic to macroscopic scales,including density functional theory,molecular dynamics,X-ray computed tomography technology,electrochemical model,equivalent circuit model,distributed model and neural network algorithm.Subsequently,the advantages and disadvantages of these model approaches are discussed for fault detection and diagnosis of batteries in different application scenarios.Finally,the remaining challenges of model-based battery diagnosis and the future perspective of using cloud control and battery intelligent networking to enhance diagnostic performance are discussed.展开更多
The penetration of electrical vehicles(EVs)is exponentially rising to decarbonize the transport sector resulting in the research problem regarding the future of their retired batteries.Landfill disposal poses an envir...The penetration of electrical vehicles(EVs)is exponentially rising to decarbonize the transport sector resulting in the research problem regarding the future of their retired batteries.Landfill disposal poses an environmental hazard,therefore,recycling or reusing them as second-life batteries(SLBs)are the inevitable options.Reusing the EV batter-ies with significant remaining useful life in stationary storage applications maximizes the economic benefits while extending the useful lifetime before recycling.Following a critical review of the research in SLBs,the key areas were identified as accurate State of Health(SOH)estimation,optimization of health indicators,battery life cycle assessment including repurposing,End-Of-Life(EOL)extension techniques and significance of first-life degradation data on age-ing in second-life applications.The inconsistencies found in the reviewed literature showed that the absence of deg-radation data from first as well as second life,has a serious impact on accurate remaining useful life(RUL)prediction and SOH estimation.This review,for the first time,critically surveyed the recent studies in the field of identification,selection and control of application-based health indicators in relation to the accurate SOH estimation,offering future research directions in this emerging research area.In addition to the technical challenges,this paper also analyzed the economic perspective of SLBs,highlighting the impact of accuracy in second-life SOH estimation and RUL extension on their projected revenue in stationary storage applications.Lack of standard business model based on future mar-ket trends of energy and battery pricing and governing policies for SLBs are identified as urgent research gaps.展开更多
基金supported by the National Natural Science Foundation of China(62373224,62333013,U23A20327)the Natural Science Foundation of Shandong Province(ZR2024JQ021)
文摘Dear Editor,Health management is essential to ensure battery performance and safety, while data-driven learning system is a promising solution to enable efficient state of health(SoH) estimation of lithium-ion(Liion) batteries. However, the time-consuming signal data acquisition and the lack of interpretability of model still hinder its efficient deployment. Motivated by this, this letter proposes a novel and interpretable data-driven learning strategy through combining the benefits of explainable AI and non-destructive ultrasonic detection for battery SoH estimation. Specifically, after equipping battery with advanced ultrasonic sensor to promise fast real-time ultrasonic signal measurement, an interpretable data-driven learning strategy named generalized additive neural decision ensemble(GANDE) is designed to rapidly estimate battery SoH and explain the effects of the involved ultrasonic features of interest.
基金National Key Research and Development Program of China (Grant No. 2022YFE0102700)National Natural Science Foundation of China (Grant No. 52102420)+2 种基金research project “Safe Da Batt” (03EMF0409A) funded by the German Federal Ministry of Digital and Transport (BMDV)China Postdoctoral Science Foundation (Grant No. 2023T160085)Sichuan Science and Technology Program (Grant No. 2024NSFSC0938)。
文摘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.
基金supported in part by the National Natural Science Foundation of China,China(Grant No.52102420)the National Key Research and Development Program of China,China(Grant No.2022YFE0102700)the China Postdoctoral Science Foundation,China(Grant No.2023T160085)。
文摘For large-scale in-service electric vehicles(EVs)that undergo potential maintenance,second-hand transactions,and retirement,it is crucial to rapidly evaluate the health status of their battery packs.However,existing methods often rely on lengthy battery charging/discharging data or extensive training samples,which hinders their implementation in practical scenarios.To address this issue,a rapid health estimation method based on short-time charging data and limited labels for in-service battery packs is proposed in this paper.First,a digital twin of battery pack is established to emulate its dynamic behavior across various aging levels and inconsistency degrees.Then,increment capacity sequences(△Q)within a short voltage span are extracted from charging process to indicate battery health.Furthermore,data-driven models based on deep convolutional neural network(DCNN)are constructed to estimate battery state of health(SOH),where the synthetic data is employed to pre-train the models,and transfer learning strategies by using fine-tuning and domain adaptation are utilized to enhance the model adaptability.Finally,field data of 10 EVs exhibiting different SOHs are used to verify the proposed methods.By using the△Q with 100 m V voltage change,the SOH of battery packs can be accurately estimated with an error around 3.2%.
文摘Monitoring and evaluating the health parameters of marine gas turbine engine help in developing predictive control techniques and maintenance schedules.Because the health parameters are unmeasurable,researchers estimate them only based on the available measurement parameters.Kalman filter-based approaches are the most commonly used estimation approaches;how-ever,the conventional Kalman filter-based approaches have a poor robustness to the model uncertainty,and their ability to track the mutation condition is influenced by historical data.Therefore,in this paper,an improved Kalman filter-based algorithm called the strong tracking extended Kalman filter(STEKF)approach is proposed to estimate the gas turbine health parameters.The analytical expressions of Jacobian matrixes are deduced by non-equilibrium point analytical linearization to address the problem of the conventional approaches.The proposed approach was used to estimate the health parameters of a two-shaft marine gas turbine engine in the simulation environment and was compared with the extended Kalman filter(EKF)and the unscented Kalman filter(UKF).The results show that the STEKF approach not only has a computation cost similar to that of the EKF approach but also outperforms the EKF approach when the health parameters change abruptly and the noise mean value is not zero.
文摘Lithium-ion batteries are the most widely accepted type of battery in the electric vehicle industry because of some of their positive inherent characteristics. However, the safety problems associated with inaccurate estimation and prediction of the state of health of these batteries have attracted wide attention due to the adverse negative effect on vehicle safety. In this paper, both machine and deep learning models were used to estimate the state of health of lithium-ion batteries. The paper introduces the definition of battery health status and its importance in the electric vehicle industry. Based on the data preprocessing and visualization analysis, three features related to actual battery capacity degradation are extracted from the data. Two learning models, SVR and LSTM were employed for the state of health estimation and their respective results are compared in this paper. The mean square error and coefficient of determination were the two metrics for the performance evaluation of the models. The experimental results indicate that both models have high estimation results. However, the metrics indicated that the SVR was the overall best model.
基金supported by the Action Programme for Cultivation of Young and Middle-aged Teachers in Universities in Anhui Province(YQYB2023030),Chinathe Supporting Programme for Outstanding Young Talents in Colleges and Universities of Anhui Provincial Department of Education(gxyq2022068),China+1 种基金the Huainan Normal University Scientific Research Project(2023XJZD016),Chinathe Key Projects of Huainan Normal University(2024XJZD012),China.
文摘An accurate assessment of the state of health(SOH)is the cornerstone for guaranteeing the long-term stable operation of electrical equipment.However,the noise the data carries during cyclic aging poses a severe challenge to the accuracy of SOH estimation and the generalization ability of the model.To this end,this paper proposed a novel SOH estimation model for lithium-ion batteries that incorporates advanced signal-processing techniques and optimized machine-learning strategies.The model employs a whale optimization algorithm(WOA)to seek the optimal parameter combination(K,α)for the variational modal decomposition(VMD)method to ensure that the signals are accurately decomposed into different modes representing the SOH of batteries.Then,the excellent local feature extraction capability of the convolutional neural network(CNN)was utilized to obtain the critical features of each modal of SOH.Finally,the support vector machine(SVM)was selected as the final SOH estimation regressor based on its generalization ability and efficient performance on small sample datasets.The method proposed was validated on a two-class publicly available aging dataset of lithium-ion batteries containing different temperatures,discharge rates,and discharge depths.The results show that the WOA-VMD-based data processing technique effectively solves the interference problem of cyclic aging data noise on SOH estimation.The CNN-SVM optimized machine learning method significantly improves the accuracy of SOH estimation.Compared with traditional techniques,the fused algorithm achieves significant results in solving the interference of data noise,improving the accuracy of SOH estimation,and enhancing the generalization ability.
基金This work was supported by the State Grid Corporation Headquarters Management Technology Project(SGTYHT/19-JS-215)Southwest Jiaotong University new interdisciplinary cultivation project by(YH1500112432273).
文摘Lithium-ion battery State of Health(SOH)estimation is an essential issue in battery management systems.In order to better estimate battery SOH,Extreme Learning Machine(ELM)is used to establish a model to estimate lithium-ion battery SOH.The Swarm Optimization algorithm(PSO)is used to automatically adjust and optimize the parameters of ELM to improve estimation accuracy.Firstly,collect cyclic aging data of the battery and extract five characteristic quantities related to battery capacity from the battery charging curve and increment capacity curve.Use Grey Relation Analysis(GRA)method to analyze the correlation between battery capacity and five characteristic quantities.Then,an ELM is used to build the capacity estimation model of the lithium-ion battery based on five characteristics,and a PSO is introduced to optimize the parameters of the capacity estimation model.The proposed method is validated by the degradation experiment of the lithium-ion battery under different conditions.The results show that the battery capacity estimation model based on ELM and PSO has better accuracy and stability in capacity estimation,and the average absolute percentage error is less than 1%.
文摘Lithium-ion batteries have an essential characteristic in consumer electronics applications and electric mobility.However,predicting their lifetime performance is a difficult task due to the impact of operating and environmental conditions.Additionally,state-of-health(SOH)and remaining-useful-life(RUL)predictions have developed into crucial components of the energy management system for lifetime prediction to guarantee the best possible performance.Due to the non-linear behaviour of the health prediction of electric vehicle batteries,the assessment of SOH and RUL has therefore become a core research challenge for both business and academics.This paper introduces a comprehensive analysis of the application of machine learning in the domain of electric vehicle battery management,emphasizing state prediction and ageing prognostics.The objective is to provide comprehensive information about the evaluation,categorization and multiple machine-learning algorithms for predicting the SOH and RUL.Additionally,lithium-ion bat-tery behaviour,the SOH estimation approach,key findings,advantages,challenges and potential of the battery management system for different state estimations are discussed.The study identifies the common challenges encountered in traditional battery manage-ment and provides a summary of how machine learning can be employed to address these challenges.
基金supported by China Postdoctoral Science Foundation(2021M690740)supported by project of the study on the gradient utilization and industrialization demonstration of lithium-ion power battery(ZH01110405180053PWC).
文摘Accurate simulation of characteristics performance and state of health(SOH)estimation for lithium-ion batteries are critical for battery management systems(BMS)in electric vehicles.Battery simplified electrochemical model(SEM)can achieve accurate estimation of battery terminal voltage with less computing resources.To ensure the applica-bility of life-cycle usage,degradation physics need to be involved in SEM models.This work conducts deep analysis on battery degradation physics and develops an aging-effect coupling model based on an existing improved single particle(ISP)model.Firstly,three mechanisms of solid electrolyte interface(SEI)film growth throughout life cycle are analyzed,and an SEI film growth model of lithium-ion battery is built coupled with the ISP model.Then,a series of identification conditions for individual cells are designed to non-destructively determine model parameters.Finally,battery aging experiment is designed to validate the battery performance simulation method and SOH estimation method.The validation results under different aging rates indicate that this method can accurately es-timate characteristics performance and SOH for lithium-ion batteries during the whole life cycle.
文摘By social expenditure on health service(SEHS)we refer to the sum total of money paid by thewhole society during a certain period of year for the sake of preventing and treating diseases andof protecting and improving the people’s health.It reflects objectively the total level of SEHSduring a certain period;the levels of health service expenditures on the parts of the whole society,enterprises,and individuals;the ratio between SEHS and total social expenditure;and the ratiosof SEHS to gross national product and to national income.The article discussed and
基金supported in part by the National Natural Science Foundation of China(92167201,62273264,61933007)。
文摘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.
文摘Data-driven models for battery state estimation require extensive experimental training data,which may not be available or suitable for specific tasks like open-circuit voltage(OCV)reconstruction and subsequent state of health(SOH)estimation.This study addresses this issue by developing a transfer-learning-based OCV reconstruction model using a temporal convolutional long short-term memory(TCN-LSTM)network trained on synthetic data from an automotive nickel cobalt aluminium oxide(NCA)cell generated through a mechanistic model approach.The data consists of voltage curves at constant temperature,C-rates between C/30 to 1C,and a SOH-range from 70%to 100%.The model is refined via Bayesian optimization and then applied to four use cases with reduced experimental nickel manganese cobalt oxide(NMC)cell training data for higher use cases.The TL models’performances are compared with models trained solely on experimental data,focusing on different C-rates and voltage windows.The results demonstrate that the OCV reconstruction mean absolute error(MAE)within the average battery electric vehicle(BEV)home charging window(30%to 85%state of charge(SOC))is less than 22 mV for the first three use cases across all C-rates.The SOH estimated from the reconstructed OCV exhibits an mean absolute percentage error(MAPE)below 2.2%for these cases.The study further investigates the impact of the source domain on TL by incorporating two additional synthetic datasets,a lithium iron phosphate(LFP)cell and an entirely artificial,non-existing,cell,showing that solely the shifting and scaling of gradient changes in the charging curve suffice to transfer knowledge,even between different cell chemistries.A key limitation with respect to extrapolation capability is identified and evidenced in our fourth use case,where the absence of such comprehensive data hindered the TL process.
文摘Open-circuit voltage(OCV)updates are the key to accurate state of charge(SOC)estimates over lifetime.Degradation modes(DM)are directly coupled to OCV estimation.They offer a more detailed analysis of the battery’s state of health(SOH)and yield optimized usage strategy,and with that,a prolonged lifetime.In this study two data-driven models are coupled with physics-based models and compared in regards of their OCV and DM estimation accuracy:Two temporal convolutional—long short-term memory neural networks(TCN-LSTM)are trained from synthetic NCA-graphite battery data for OCV curve estimation(model 1)and alignment parameter estimation(model 2).Both models are fine-tuned with varying amounts of experimental NMC-graphite battery data during the transfer learning(TL)step.In the subsequent physics-constraining part the DMs are derived via optimization(model 1),i.e.,fitting the OCV with half cell open-circuit potentials,or directly via mathematical equations(model 2).Both models prove that fine-tuning data from one aging path suffices,if it includes the maximum appearing DMs of the target domain.For these use cases both models maintain OCV mean absolute errors(MAEs),DM MAEs and SOH mean absolute percentage errors(MAPEs)under 10 mV,3.10%and 1.98%,respectively.The model 2 has less computational complexity and reaches slightly better results but requires labeled target data including alignment parameters for its application.This study shows that synthetic data is eligible for TL,even for varying cell chemistries,and that the mechanistic model helps to physically constrain the output.
基金supported in part by 2022–2024 Masaru Ibuka Foundation Research Project on Oriental Medicine,2020–2025 JSPS A3 Foresight Program(No.JPJSA3F20200001)2022–2024 Japan National Initiative Promotion for Digital Rural City,2022–2024 JST SPRING(No.JPMJSP2128)+1 种基金2023 and 2024 Waseda University Grants for Special Research Projects(Nos.2023C-216 and 2024C-223)2023–2024 Waseda University Advanced Research Center Project for Regional Cooperation Support,and 2023–2024 Waseda University Advanced Research Center for Human Sciences Project(No.BA080Z000300).
文摘The identification of latent risk factors that can induce to health risks or an abnormal status is an important task in healthcare data analyses.In recent years,health analyses based on neural network models have been applied widely.However,such analysis processes are blackbox and the results lack explainability.Some approaches by constructing a domain model may tackle these issues.However,domain knowledge from an expert is required.In this study,we propose an exploratory and interpretable approach to estimating latent health risk factors without relying on domain knowledge,in which feature selection and causal discovery are used to construct a domain model for uncovering complex relationships in health and medical data.An evaluation experiment conducted on two datasets by comparing the proposed approach with four baselines demonstrated that the proposed approach outperformed the baselines in terms of model fitness.Furthermore,the number of model parameters in our method was smaller than that in the baselines,which reduced model complexity.Moreover,the analysis process of the proposed approach was visible and explainable,which improved the interpretability of the analysis processes.
文摘At present,a life-cycle assessment of energy storage systems(ESSs)is not widely available in the literature.Such an assessment is increasingly vital nowadays as ESS is recognized as one of the important equipment in power systems to reduce peak demands for deferring or avoiding augmentation in the network and power generation.As the battery cost is still very high at present,a comprehensive assessment is necessary to determine the optimum ESS capacity so that the maximum financial gain is achievable at the end of the batteries’lifespan.Therefore,an effective life-cycle assessment is proposed in this paper to show how the optimum ESS capacity can be determined such that the maximum net financial gain is achievable at the end of the batteries’lifespan when ESS is used to perform peak demand reductions for the customer or utility companies.The findings reveal the positive financial viability of ESS on the power grid,otherwise the projection of the financial viability is often seemingly poor due to the high battery cost with a short battery lifespan.An improved battery degradation model is used in this assessment,which can simulate the battery degradation accurately in a situation whereby the charging current,discharging current,and temperature of the batteries are intermittent on a site during peak demand reductions.This assessment is crucial to determine the maximum financial benefits brought by ESS.
文摘With a growing consumer market of battery electric vehicles, customers＇ demand for technology and features is on the rise. The range and, to a certain extent, the range estimation will play a key factor in customers＇ purchase decisions. In order to guarantee a precise range estimation over the usage life of battery electric vehicles, a method is presented that combines adaptive filter algorithms with statistical approaches. The statistical approach uses recurring driving cycles over the lifetime in order to derive the aging status of the traction battery. It is implied that the variance of the energy usage of these driving cycles is within certain bounds. This fact should be proven by an experimental case study. The dataset used in this paper is open to the public.
基金National Natural Science Foundation of China(U1864213).
文摘Fault diagnosis is key to enhancing the performance and safety of battery storage systems.However,it is challenging to realize efficient fault diagnosis for lithium-ion batteries because the accuracy diagnostic algorithm is limited and the features of the different faults are similar.The model-based method has been widely used for degradation mechanism analysis,state estimation,and life prediction of lithium-ion battery systems due to the fast speed and high development efficiency.This paper reviews the mainstream modeling approaches used for battery diagnosis.First,a review of the battery’s degradation mechanisms and the external factors affecting the aging rate is presented.Second,the different modeling approaches are summarized,from microscopic to macroscopic scales,including density functional theory,molecular dynamics,X-ray computed tomography technology,electrochemical model,equivalent circuit model,distributed model and neural network algorithm.Subsequently,the advantages and disadvantages of these model approaches are discussed for fault detection and diagnosis of batteries in different application scenarios.Finally,the remaining challenges of model-based battery diagnosis and the future perspective of using cloud control and battery intelligent networking to enhance diagnostic performance are discussed.
文摘The penetration of electrical vehicles(EVs)is exponentially rising to decarbonize the transport sector resulting in the research problem regarding the future of their retired batteries.Landfill disposal poses an environmental hazard,therefore,recycling or reusing them as second-life batteries(SLBs)are the inevitable options.Reusing the EV batter-ies with significant remaining useful life in stationary storage applications maximizes the economic benefits while extending the useful lifetime before recycling.Following a critical review of the research in SLBs,the key areas were identified as accurate State of Health(SOH)estimation,optimization of health indicators,battery life cycle assessment including repurposing,End-Of-Life(EOL)extension techniques and significance of first-life degradation data on age-ing in second-life applications.The inconsistencies found in the reviewed literature showed that the absence of deg-radation data from first as well as second life,has a serious impact on accurate remaining useful life(RUL)prediction and SOH estimation.This review,for the first time,critically surveyed the recent studies in the field of identification,selection and control of application-based health indicators in relation to the accurate SOH estimation,offering future research directions in this emerging research area.In addition to the technical challenges,this paper also analyzed the economic perspective of SLBs,highlighting the impact of accuracy in second-life SOH estimation and RUL extension on their projected revenue in stationary storage applications.Lack of standard business model based on future mar-ket trends of energy and battery pricing and governing policies for SLBs are identified as urgent research gaps.
