State of health(SOH)estimation of e-mobilities operated in real and dynamic conditions is essential and challenging.Most of existing estimations are based on a fixed constant current charging and discharging aging pro...State of health(SOH)estimation of e-mobilities operated in real and dynamic conditions is essential and challenging.Most of existing estimations are based on a fixed constant current charging and discharging aging profiles,which overlooked the fact that the charging and discharging profiles are random and not complete in real application.This work investigates the influence of feature engineering on the accuracy of different machine learning(ML)-based SOH estimations acting on different recharging sub-profiles where a realistic battery mission profile is considered.Fifteen features were extracted from the battery partial recharging profiles,considering different factors such as starting voltage values,charge amount,and charging sliding windows.Then,features were selected based on a feature selection pipeline consisting of filtering and supervised ML-based subset selection.Multiple linear regression(MLR),Gaussian process regression(GPR),and support vector regression(SVR)were applied to estimate SOH,and root mean square error(RMSE)was used to evaluate and compare the estimation performance.The results showed that the feature selection pipeline can improve SOH estimation accuracy by 55.05%,2.57%,and 2.82%for MLR,GPR and SVR respectively.It was demonstrated that the estimation based on partial charging profiles with lower starting voltage,large charge,and large sliding window size is more likely to achieve higher accuracy.This work hopes to give some insights into the supervised ML-based feature engineering acting on random partial recharges on SOH estimation performance and tries to fill the gap of effective SOH estimation between theoretical study and real dynamic application.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
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
Battery systems are increasingly being used for powering ocean going ships,and the number of fully electric or hybrid ships relying on battery power for propulsion is growing.To ensure the safety of such ships,it is i...Battery systems are increasingly being used for powering ocean going ships,and the number of fully electric or hybrid ships relying on battery power for propulsion is growing.To ensure the safety of such ships,it is important to monitor the available energy that can be stored in the batteries,and classification societies typically require the state of health(SOH)to be verified by independent tests.This paper addresses statistical modeling of SOH for maritime lithium-ion batteries based on operational sensor data.Various methods for sensor-based,data-driven degradation monitoring will be presented,and advantages and challenges with the different approaches will be discussed.The different approaches include cumulative degradation models and snapshot models,models that need to be trained and models that need no prior training,and pure data-driven models and physics-informed models.Some of the methods only rely on measured data,such as current,voltage,and temperature,whereas others rely on derived quantities such as state of charge.Models include simple statistical models and more complicated machine learning techniques.Insight from this exploration will be important in establishing a framework for data-driven diagnostics and prognostics of maritime battery systems within the scope of classification societies.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
Prognostics and health management(PHM)has gotten considerable attention in the background of Industry 4.0.Battery PHM contributes to the reliable and safe operation of electric devices.Nevertheless,relevant reviews ar...Prognostics and health management(PHM)has gotten considerable attention in the background of Industry 4.0.Battery PHM contributes to the reliable and safe operation of electric devices.Nevertheless,relevant reviews are still continuously updated over time.In this paper,we browsed extensive literature related to battery PHM from 2018to 2023 and summarized advances in battery PHM field,including battery testing and public datasets,fault diagnosis and prediction methods,health status estimation and health management methods.The last topic includes state of health estimation methods,remaining useful life prediction methods and predictive maintenance methods.Each of these categories is introduced and discussed in details.Based on this survey,we accordingly discuss challenges left to battery PHM,and provide future research opportunities.This research systematically reviews recent research about battery PHM from the perspective of key PHM steps and provide some valuable prospects for researchers and practitioners.展开更多
To improve the estimation accuracy of state of charge(SOC)and state of health(SOH)for lithium-ion batteries,in this paper,a joint estimation method of SOC and SOH at charging cut-off voltage based on genetic algorithm...To improve the estimation accuracy of state of charge(SOC)and state of health(SOH)for lithium-ion batteries,in this paper,a joint estimation method of SOC and SOH at charging cut-off voltage based on genetic algorithm(GA)combined with back propagation(BP)neural network is proposed,the research addresses the issue of data manipulation resulting fromcyber-attacks.Firstly,anomalous data stemming fromcyber-attacks are identified and eliminated using the isolated forest algorithm,followed by data restoration.Secondly,the incremental capacity(IC)curve is derived fromthe restored data using theKalman filtering algorithm,with the peak of the ICcurve(ICP)and its corresponding voltage serving as the health factor(HF).Thirdly,the GA-BP neural network is applied to map the relationship between HF,constant current charging time,and SOH,facilitating the estimation of SOH based on HF.Finally,SOC estimation at the charging cut-off voltage is calculated by inputting the SOH estimation value into the trained model to determine the constant current charging time,and by updating the maximum available capacity.Experiments show that the root mean squared error of the joint estimation results does not exceed 1%,which proves that the proposed method can estimate the SOC and SOH accurately and stably even in the presence of false data injection attacks.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
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
This paper presents a more accurate battery state of charge(SOC)and state of health(SOH)estimation method.A lithium battery is represented by a nonlinear two-order resistance-capacitance equivalent circuit model.The m...This paper presents a more accurate battery state of charge(SOC)and state of health(SOH)estimation method.A lithium battery is represented by a nonlinear two-order resistance-capacitance equivalent circuit model.The model parameters are estimated by searching least square error optimization algorithm.Precisely defined by this method,the model parameters allow to accurately determine the capacity of the battery,which in turn allows to specify the SOC prediction value used as a basis for the SOH value.Application of the extended Kalman filter(EKF)removes the need of prior known initial SOC,and applying the fuzzy logic helps to eliminate the measurement and process noise.Simulation results obtained during the urban dynamometer driving schedule(UDDS)test show that the maximum error in estimation of the battery SOC is 0.66%.Battery capacity is estimate by offline updated Kalman filter,and then SOH will be predicted.The maximum error in estimation of the battery capacity is 1.55%.展开更多
100 pieces of 26650-type Lithium iron phosphate（LiFePO4） batteries cycled with a fixed charge and discharge rate are tested, and the influence of the battery internal resistance and the instantaneous voltage drop at...100 pieces of 26650-type Lithium iron phosphate（LiFePO4） batteries cycled with a fixed charge and discharge rate are tested, and the influence of the battery internal resistance and the instantaneous voltage drop at the start of discharge on the state of health（SOH） is discussed. A back propagation（BP） neural network model using additional momentum is built up to estimate the state of health of Li-ion batteries. The additional 10 pieces are used to verify the feasibility of the proposed method. The results show that the neural network prediction model have a higher accuracy and can be embedded into battery management system（BMS） to estimate SOH of LiFePO4 Li-ion batteries.展开更多
When considering the mechanism of the batteries,the capacity reduction at storage(when not in use)and cycling(during use)and increase of internal resistance is because of degradation in the chemical composition inside...When considering the mechanism of the batteries,the capacity reduction at storage(when not in use)and cycling(during use)and increase of internal resistance is because of degradation in the chemical composition inside the batteries.To optimize battery usage,a battery management system(BMS)is used to estimate possible aging effects while different load profiles are requested from the grid.This is specifically seen in a case when the vehicle is connected to the net(online through BMS).During this process,the BMS chooses the optimized load profiles based on the least aging effects on the battery pack.The major focus of this paper is to design an algorithm/model for lithium iron phosphate(LiFePO4)batteries.The model of the batteries is based on the accelerated aging test data(data from the beginning of life till the end of life).The objective is to develop an algorithm based on the actual battery trend during the whole life of the battery.By the analysis of the test data,the complete trend of the battery aging and the factors on which the aging is depending on is identified,the aging model can then be recalibrated to avoid any differences in the production process during cell manufacturing.The validation of the model was carried out at the end by utilizing different driving profiles at different C-rates and different ambient temperatures.A Linear and non-linear model-based approach is used based on statistical data.The parameterization was carried out by dividing the data into small chunks and estimating the parameters for the individual chunks.Self-adaptive characteristic map using a lookup table was also used.The nonlinear model was chosen as the best candidate among all other approaches for longer validation of 8-month data with real driving data set.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
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
As the intersection of disciplines deepens,the field of battery modeling is increasingly employing various artificial intelligence(AI)approaches to improve the efficiency of battery management and enhance the stabilit...As the intersection of disciplines deepens,the field of battery modeling is increasingly employing various artificial intelligence(AI)approaches to improve the efficiency of battery management and enhance the stability and reliability of battery operation.This paper reviews the value of AI methods in lithium-ion battery health management and in particular analyses the application of machine learning(ML),one of the many branches of AI,to lithium-ion battery state of health(SOH),focusing on the advantages and strengths of neural network(NN)methods in ML for lithium-ion battery SOH simulation and prediction.NN is one of the important branches of ML,in which the application of NNs such as backpropagation NN,convolutional NN,and long short-term memory NN in SOH estimation of lithium-ion batteries has received wide attention.Reports so far have shown that the utilization of NN to model the SOH of lithium-ion batteries has the advantages of high efficiency,low energy consumption,high robustness,and scalable models.In the future,NN can make a greater contribution to lithium-ion battery management by,first,utilizing more field data to play a more practical role in health feature screening and model building,and second,by enhancing the intelligent screening and combination of battery parameters to characterize the actual lithium-ion battery SOH to a greater extent.The in-depth application of NN in lithium-ion battery SOH will certainly further enhance the science,reliability,stability,and robustness of lithium-ion battery management.展开更多
Background: Worldwide elderly population and their life expectancy are increasing gradually. Longevity in most cases brings down poorer health as well as functional status. Thus, it is necessary to understand the prob...Background: Worldwide elderly population and their life expectancy are increasing gradually. Longevity in most cases brings down poorer health as well as functional status. Thus, it is necessary to understand the problems as well as social, psychological, and medical needs of elderly people in order to plan their optimal care. Objectives: To assess the mental health status (depression and memory state) of elderly people attending Geriatric clinic in medical city, and to determine the influence of some sociodemographic factors on elderly mental health status. Subjects and Method: A cross-sectional study was conducted among elderly people aged 60 years and more who attended geriatric clinic of medical city in Baghdad, from 1st of April to the end of June 2015. Special questionnaire form had been used for data collection via direct interview. The evaluation of the mental state was performed by using modified version of Wechsler Memory Scale and geriatric depression scale. Results: A total of 400 elderly persons were enrolled in the study, 109 (27.3%) of them had impaired memory. The analysis of data revealed that the age and marital status had statistical significant association with memory state. Nearly three quarter (72.8%) of study group had depression according to geriatric depression scale. The majority of studied women had depression (90%), and the same percentage was observed among widowed elders joining in the study.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
The estimation of State of Health(SOH)for battery packs used in Electric Vehicles(EVs)is a complex task with significant importance,accompanied by several challenges.This study introduces a data-fusion model approach ...The estimation of State of Health(SOH)for battery packs used in Electric Vehicles(EVs)is a complex task with significant importance,accompanied by several challenges.This study introduces a data-fusion model approach to estimate the SOH of battery packs.The approach utilizes dual Gaussian Process Regressions(GPRs)to construct a data-driven and non-parametric aging model based on charging-based Aging Features(AFs).To enhance the accuracy of the aging model,a noise model is established to replace the random noise.Subsequently,the statespace representation of the aging model is incorporated.Additionally,the Particle Filter(PF)is introduced to track the unknown state in the aging model,thereby developing the data-fusion-model for SOH estimation.The performance of the proposed method is validated through aging experiments conducted on battery packs.The simulation results demonstrate that the data-fusion model approach achieves accurate SOH estimation,with maximum errors less than 1.5%.Compared to conventional techniques such as GPR and Support Vector Regression(SVR),the proposed method exhibits higher estimation accuracy and robustness.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
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
基金funded by China Scholarship Council.The fund number is 202108320111 and 202208320055。
文摘State of health(SOH)estimation of e-mobilities operated in real and dynamic conditions is essential and challenging.Most of existing estimations are based on a fixed constant current charging and discharging aging profiles,which overlooked the fact that the charging and discharging profiles are random and not complete in real application.This work investigates the influence of feature engineering on the accuracy of different machine learning(ML)-based SOH estimations acting on different recharging sub-profiles where a realistic battery mission profile is considered.Fifteen features were extracted from the battery partial recharging profiles,considering different factors such as starting voltage values,charge amount,and charging sliding windows.Then,features were selected based on a feature selection pipeline consisting of filtering and supervised ML-based subset selection.Multiple linear regression(MLR),Gaussian process regression(GPR),and support vector regression(SVR)were applied to estimate SOH,and root mean square error(RMSE)was used to evaluate and compare the estimation performance.The results showed that the feature selection pipeline can improve SOH estimation accuracy by 55.05%,2.57%,and 2.82%for MLR,GPR and SVR respectively.It was demonstrated that the estimation based on partial charging profiles with lower starting voltage,large charge,and large sliding window size is more likely to achieve higher accuracy.This work hopes to give some insights into the supervised ML-based feature engineering acting on random partial recharges on SOH estimation performance and tries to fill the gap of effective SOH estimation between theoretical study and real dynamic application.
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.
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This work has been carried out with in the DDD BATMAN project,supported by MarTERA and the Research Council of Norway(project no 311445).
文摘Battery systems are increasingly being used for powering ocean going ships,and the number of fully electric or hybrid ships relying on battery power for propulsion is growing.To ensure the safety of such ships,it is important to monitor the available energy that can be stored in the batteries,and classification societies typically require the state of health(SOH)to be verified by independent tests.This paper addresses statistical modeling of SOH for maritime lithium-ion batteries based on operational sensor data.Various methods for sensor-based,data-driven degradation monitoring will be presented,and advantages and challenges with the different approaches will be discussed.The different approaches include cumulative degradation models and snapshot models,models that need to be trained and models that need no prior training,and pure data-driven models and physics-informed models.Some of the methods only rely on measured data,such as current,voltage,and temperature,whereas others rely on derived quantities such as state of charge.Models include simple statistical models and more complicated machine learning techniques.Insight from this exploration will be important in establishing a framework for data-driven diagnostics and prognostics of maritime battery systems within the scope of classification societies.
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 Tianjin Municipal Education Commission of China (Grant No. 2023KJ303)National Natural Science Foundation of China (Grant Nos. 12121002, 51975355)
文摘Prognostics and health management(PHM)has gotten considerable attention in the background of Industry 4.0.Battery PHM contributes to the reliable and safe operation of electric devices.Nevertheless,relevant reviews are still continuously updated over time.In this paper,we browsed extensive literature related to battery PHM from 2018to 2023 and summarized advances in battery PHM field,including battery testing and public datasets,fault diagnosis and prediction methods,health status estimation and health management methods.The last topic includes state of health estimation methods,remaining useful life prediction methods and predictive maintenance methods.Each of these categories is introduced and discussed in details.Based on this survey,we accordingly discuss challenges left to battery PHM,and provide future research opportunities.This research systematically reviews recent research about battery PHM from the perspective of key PHM steps and provide some valuable prospects for researchers and practitioners.
基金funded by the Scientific Research Project of the Education Department of Jilin Province(No.JJKH20230121KJ).
文摘To improve the estimation accuracy of state of charge(SOC)and state of health(SOH)for lithium-ion batteries,in this paper,a joint estimation method of SOC and SOH at charging cut-off voltage based on genetic algorithm(GA)combined with back propagation(BP)neural network is proposed,the research addresses the issue of data manipulation resulting fromcyber-attacks.Firstly,anomalous data stemming fromcyber-attacks are identified and eliminated using the isolated forest algorithm,followed by data restoration.Secondly,the incremental capacity(IC)curve is derived fromthe restored data using theKalman filtering algorithm,with the peak of the ICcurve(ICP)and its corresponding voltage serving as the health factor(HF).Thirdly,the GA-BP neural network is applied to map the relationship between HF,constant current charging time,and SOH,facilitating the estimation of SOH based on HF.Finally,SOC estimation at the charging cut-off voltage is calculated by inputting the SOH estimation value into the trained model to determine the constant current charging time,and by updating the maximum available capacity.Experiments show that the root mean squared error of the joint estimation results does not exceed 1%,which proves that the proposed method can estimate the SOC and SOH accurately and stably even in the presence of false data injection attacks.
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.
基金Open Fund Project of State Key Laboratory of Large Electric Transmission Systems and Equipment Technology(No.SKLLDJ042017005)。
文摘This paper presents a more accurate battery state of charge(SOC)and state of health(SOH)estimation method.A lithium battery is represented by a nonlinear two-order resistance-capacitance equivalent circuit model.The model parameters are estimated by searching least square error optimization algorithm.Precisely defined by this method,the model parameters allow to accurately determine the capacity of the battery,which in turn allows to specify the SOC prediction value used as a basis for the SOH value.Application of the extended Kalman filter(EKF)removes the need of prior known initial SOC,and applying the fuzzy logic helps to eliminate the measurement and process noise.Simulation results obtained during the urban dynamometer driving schedule(UDDS)test show that the maximum error in estimation of the battery SOC is 0.66%.Battery capacity is estimate by offline updated Kalman filter,and then SOH will be predicted.The maximum error in estimation of the battery capacity is 1.55%.
基金Supported by Special Topic of the Ministry of Education about Humanities and Social Sciences of China(No.12JDGC007)International Scientific and Technological Cooperation Projects of China(No.2012DFB10060)
文摘100 pieces of 26650-type Lithium iron phosphate（LiFePO4） batteries cycled with a fixed charge and discharge rate are tested, and the influence of the battery internal resistance and the instantaneous voltage drop at the start of discharge on the state of health（SOH） is discussed. A back propagation（BP） neural network model using additional momentum is built up to estimate the state of health of Li-ion batteries. The additional 10 pieces are used to verify the feasibility of the proposed method. The results show that the neural network prediction model have a higher accuracy and can be embedded into battery management system（BMS） to estimate SOH of LiFePO4 Li-ion batteries.
文摘When considering the mechanism of the batteries,the capacity reduction at storage(when not in use)and cycling(during use)and increase of internal resistance is because of degradation in the chemical composition inside the batteries.To optimize battery usage,a battery management system(BMS)is used to estimate possible aging effects while different load profiles are requested from the grid.This is specifically seen in a case when the vehicle is connected to the net(online through BMS).During this process,the BMS chooses the optimized load profiles based on the least aging effects on the battery pack.The major focus of this paper is to design an algorithm/model for lithium iron phosphate(LiFePO4)batteries.The model of the batteries is based on the accelerated aging test data(data from the beginning of life till the end of life).The objective is to develop an algorithm based on the actual battery trend during the whole life of the battery.By the analysis of the test data,the complete trend of the battery aging and the factors on which the aging is depending on is identified,the aging model can then be recalibrated to avoid any differences in the production process during cell manufacturing.The validation of the model was carried out at the end by utilizing different driving profiles at different C-rates and different ambient temperatures.A Linear and non-linear model-based approach is used based on statistical data.The parameterization was carried out by dividing the data into small chunks and estimating the parameters for the individual chunks.Self-adaptive characteristic map using a lookup table was also used.The nonlinear model was chosen as the best candidate among all other approaches for longer validation of 8-month data with real driving data set.
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supported by the National Key R&D Program of China(Grant No.2021YFB2401800)the Research Fund Program for Young Scholars(Chen Lai)of Beijing Institute of Technology,and the National Natural Science Foundation of China(Grant No.52074037).
文摘As the intersection of disciplines deepens,the field of battery modeling is increasingly employing various artificial intelligence(AI)approaches to improve the efficiency of battery management and enhance the stability and reliability of battery operation.This paper reviews the value of AI methods in lithium-ion battery health management and in particular analyses the application of machine learning(ML),one of the many branches of AI,to lithium-ion battery state of health(SOH),focusing on the advantages and strengths of neural network(NN)methods in ML for lithium-ion battery SOH simulation and prediction.NN is one of the important branches of ML,in which the application of NNs such as backpropagation NN,convolutional NN,and long short-term memory NN in SOH estimation of lithium-ion batteries has received wide attention.Reports so far have shown that the utilization of NN to model the SOH of lithium-ion batteries has the advantages of high efficiency,low energy consumption,high robustness,and scalable models.In the future,NN can make a greater contribution to lithium-ion battery management by,first,utilizing more field data to play a more practical role in health feature screening and model building,and second,by enhancing the intelligent screening and combination of battery parameters to characterize the actual lithium-ion battery SOH to a greater extent.The in-depth application of NN in lithium-ion battery SOH will certainly further enhance the science,reliability,stability,and robustness of lithium-ion battery management.
文摘Background: Worldwide elderly population and their life expectancy are increasing gradually. Longevity in most cases brings down poorer health as well as functional status. Thus, it is necessary to understand the problems as well as social, psychological, and medical needs of elderly people in order to plan their optimal care. Objectives: To assess the mental health status (depression and memory state) of elderly people attending Geriatric clinic in medical city, and to determine the influence of some sociodemographic factors on elderly mental health status. Subjects and Method: A cross-sectional study was conducted among elderly people aged 60 years and more who attended geriatric clinic of medical city in Baghdad, from 1st of April to the end of June 2015. Special questionnaire form had been used for data collection via direct interview. The evaluation of the mental state was performed by using modified version of Wechsler Memory Scale and geriatric depression scale. Results: A total of 400 elderly persons were enrolled in the study, 109 (27.3%) of them had impaired memory. The analysis of data revealed that the age and marital status had statistical significant association with memory state. Nearly three quarter (72.8%) of study group had depression according to geriatric depression scale. The majority of studied women had depression (90%), and the same percentage was observed among widowed elders joining in the study.
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 the National Natural Science Foundation of China(Grant No.62303007)Doctoral Research Start-up Funding(Grant No.S020318015/028)China Postdoctoral Science Foundation(No.2023M741452).
文摘The estimation of State of Health(SOH)for battery packs used in Electric Vehicles(EVs)is a complex task with significant importance,accompanied by several challenges.This study introduces a data-fusion model approach to estimate the SOH of battery packs.The approach utilizes dual Gaussian Process Regressions(GPRs)to construct a data-driven and non-parametric aging model based on charging-based Aging Features(AFs).To enhance the accuracy of the aging model,a noise model is established to replace the random noise.Subsequently,the statespace representation of the aging model is incorporated.Additionally,the Particle Filter(PF)is introduced to track the unknown state in the aging model,thereby developing the data-fusion-model for SOH estimation.The performance of the proposed method is validated through aging experiments conducted on battery packs.The simulation results demonstrate that the data-fusion model approach achieves accurate SOH estimation,with maximum errors less than 1.5%.Compared to conventional techniques such as GPR and Support Vector Regression(SVR),the proposed method exhibits higher estimation accuracy and robustness.
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.
