Accurately estimating the State of Health(SOH)of batteries is of great significance for the stable operation and safety of lithiumbatteries.This article proposes amethod based on the combination of Capacity Incrementa...Accurately estimating the State of Health(SOH)of batteries is of great significance for the stable operation and safety of lithiumbatteries.This article proposes amethod based on the combination of Capacity Incremental Curve Analysis(ICA)andWhale Optimization Algorithm-Radial Basis Function(WOA-RBF)neural network algorithm to address the issues of low accuracy and slow convergence speed in estimating State of Health of batteries.Firstly,preprocess the battery data to obtain the real battery SOH curve and Capacity-Voltage(Q-V)curve,convert the Q-V curve into an IC curve and denoise it,analyze the parameters in the IC curve that may serve as health features;Then,extract the constant current charging time of the battery and the horizontal and vertical coordinates of the two IC peaks as health features,and perform correlation analysis using Pearson correlation coefficient method;Finally,theWOA-RBF algorithmwas used to estimate the battery SOH,and the training results of LSTM,RBF,and PSO-RBF algorithms were compared.The conclusion was drawn that theWOA-RBF algorithm has high accuracy,fast convergence speed,and the best linearity in estimating SOH.The absolute error of its SOHestimation can be controlled within 1%,and the relative error can be controlled within 2%.展开更多
Battery state-of-health(SOH)estimation is vital across applications ranging from portable electronics to electric vehicles,particularly in second-life applications where accurate prediction becomes complex due to vary...Battery state-of-health(SOH)estimation is vital across applications ranging from portable electronics to electric vehicles,particularly in second-life applications where accurate prediction becomes complex due to varying degradation levels.This paper introduces a novel SOH estimation model to address the lack of labeled data,employing domain-adversarial neural networks(DANNs)combined with one-dimensional convolutional neural networks(CNNs).The proposed method allows for effective transfer of knowledge between diverse battery conditions,enhancing adaptability and efficiency by utilizing both source and target datasets.Experimental results demonstrate that the proposed model achieves a mean absolute error(MAE)of 1.68%and a root mean squared error(RMSE)of 2.50%,with minimal data.Specifically,the model requires only one cell of unlabeled data from the second-life target domain,utilizing only the dQ/dV curve for estimation.Proposed model sets a new standard in second-life battery health monitoring and management by effectively leveraging a minimal amount of data for training,and this approach offers a robust solution for accurate SOH estimation,particularly in scenarios with limited access to labeled data.展开更多
In this paper,battery aging diversity among independent cells was studied in terms of available capacity degradation.During the aging process of LiFePO_(4)batteries,the phenomenon of aging diversity can be observed.Wh...In this paper,battery aging diversity among independent cells was studied in terms of available capacity degradation.During the aging process of LiFePO_(4)batteries,the phenomenon of aging diversity can be observed.When batteries with same specification were charged and discharged repeatedly under the same working conditions,the available capacity of different cell decreased at different rates along the cycle number.In this study,accelerated aging tests were carried out on multiple new LiFePO_(4)battery samples of different brands.Experimental results show that under the same working conditions,the actual available capacity of all cells decreased as the number of aging cycle increased,but an obvious aging diversity was observed even among different cells of same brand with same specification.This aging diversity was described and analysed in detail,and the common aging features of different cells beneath this aging diversity was explored.Considering this aging diversity,a probability density concept was adopted to estimate battery’s state of health(SOH).With this method,a relationship between battery SOH and its aging feature parameter was established,and a dynamic sliding window optimization technique was designed to ensure the optimal quality of aging feature extraction.Finally,the accuracy of this SOH estimation method was verified by random test.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
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
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
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 batteries(LIBs)are crucial for the large-scale utilization of clean energy.However,because of the com-plexity and real-time nature of internal reactions,the mechanism of capacity decline in LIBs is still u...Lithium-ion batteries(LIBs)are crucial for the large-scale utilization of clean energy.However,because of the com-plexity and real-time nature of internal reactions,the mechanism of capacity decline in LIBs is still unclear.This has become a bottleneck restricting their promotion and application.Electrochemical impedance spectroscopy(EIS)contains rich electrochemical connotations and significant application prospects,and has attracted widespread atten-tion and research on efficient energy storage systems.Compared to traditional voltage and current data,the state-of-health(SOH)estimation model based on EIS has higher accuracy.This paper categorizes EIS measurement methods based on different principles,introduces the relationship between LIBs aging mechanism and SOH,and compares the advantages of different SOH estimation methods.After a detailed analysis of the latest technologies,a review is given.The insights of this review can deepen the understanding of the relationship between EIS and the aging effect mechanism of LIBs,and promote the development of new energy storage devices and evaluation methods.展开更多
The research of the fuel cell and lithium battery hybrid system has attracted more and more researchers because of its advantages of low emission.However,the lower efficiency of energy management has been a critical f...The research of the fuel cell and lithium battery hybrid system has attracted more and more researchers because of its advantages of low emission.However,the lower efficiency of energy management has been a critical factor that obstructs the commercialization of the hybrid system.In this study,based on finite element ideas,a solid oxide full cell node model is developed to accurately estimate the operating status of the fuel cell.As for the lithium battery,previous energy management studies generally only focused on the state of charge,however,the state of health is also a key parameter for lithium batteries.In this paper,considering the strong coupling between variables,we use a particle filter algorithm to jointly estimate the state of charge and state of health.To reasonably distribute and manage the energy in the hybrid system,a T-S fuzzy controller for the hybrid system is designed by controlling the state variables in the SOFC model and the state of lithium battery.Finally,our algorithm is verified by using the DSPACE simulation platform.The results show that the hybrid system with an energy management strategy not only meets the real-time sharply changing demands,but also ensures the hybrid system is working efficiently and safely.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funded by the Basic Science(Natural Science)Research Project of Colleges and Universities in Jiangsu Province,grant number 22KJD470002.
文摘Accurately estimating the State of Health(SOH)of batteries is of great significance for the stable operation and safety of lithiumbatteries.This article proposes amethod based on the combination of Capacity Incremental Curve Analysis(ICA)andWhale Optimization Algorithm-Radial Basis Function(WOA-RBF)neural network algorithm to address the issues of low accuracy and slow convergence speed in estimating State of Health of batteries.Firstly,preprocess the battery data to obtain the real battery SOH curve and Capacity-Voltage(Q-V)curve,convert the Q-V curve into an IC curve and denoise it,analyze the parameters in the IC curve that may serve as health features;Then,extract the constant current charging time of the battery and the horizontal and vertical coordinates of the two IC peaks as health features,and perform correlation analysis using Pearson correlation coefficient method;Finally,theWOA-RBF algorithmwas used to estimate the battery SOH,and the training results of LSTM,RBF,and PSO-RBF algorithms were compared.The conclusion was drawn that theWOA-RBF algorithm has high accuracy,fast convergence speed,and the best linearity in estimating SOH.The absolute error of its SOHestimation can be controlled within 1%,and the relative error can be controlled within 2%.
文摘Battery state-of-health(SOH)estimation is vital across applications ranging from portable electronics to electric vehicles,particularly in second-life applications where accurate prediction becomes complex due to varying degradation levels.This paper introduces a novel SOH estimation model to address the lack of labeled data,employing domain-adversarial neural networks(DANNs)combined with one-dimensional convolutional neural networks(CNNs).The proposed method allows for effective transfer of knowledge between diverse battery conditions,enhancing adaptability and efficiency by utilizing both source and target datasets.Experimental results demonstrate that the proposed model achieves a mean absolute error(MAE)of 1.68%and a root mean squared error(RMSE)of 2.50%,with minimal data.Specifically,the model requires only one cell of unlabeled data from the second-life target domain,utilizing only the dQ/dV curve for estimation.Proposed model sets a new standard in second-life battery health monitoring and management by effectively leveraging a minimal amount of data for training,and this approach offers a robust solution for accurate SOH estimation,particularly in scenarios with limited access to labeled data.
基金Sponsored by the National Natural Science Foundation of China(Grant No.51877187)the Key Program of University Technology Plan of Hebei Province(Grant No.ZD2017081).
文摘In this paper,battery aging diversity among independent cells was studied in terms of available capacity degradation.During the aging process of LiFePO_(4)batteries,the phenomenon of aging diversity can be observed.When batteries with same specification were charged and discharged repeatedly under the same working conditions,the available capacity of different cell decreased at different rates along the cycle number.In this study,accelerated aging tests were carried out on multiple new LiFePO_(4)battery samples of different brands.Experimental results show that under the same working conditions,the actual available capacity of all cells decreased as the number of aging cycle increased,but an obvious aging diversity was observed even among different cells of same brand with same specification.This aging diversity was described and analysed in detail,and the common aging features of different cells beneath this aging diversity was explored.Considering this aging diversity,a probability density concept was adopted to estimate battery’s state of health(SOH).With this method,a relationship between battery SOH and its aging feature parameter was established,and a dynamic sliding window optimization technique was designed to ensure the optimal quality of aging feature extraction.Finally,the accuracy of this SOH estimation method was verified by random test.
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 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supported by the Youth Fund of Shandong Province Natural Science Foundation(No.ZR2020QE212)Key Projects of Shandong Province Natural Science Foundation(No.ZR2020KF020)+2 种基金the Guangdong Provincial Key Lab of Green Chemical Product Technology(GC202111)Zhejiang Province Natural Science Foundation(No.LY22E070007)National Natural Science Foundation of China(No.52007170).
文摘Lithium-ion batteries(LIBs)are crucial for the large-scale utilization of clean energy.However,because of the com-plexity and real-time nature of internal reactions,the mechanism of capacity decline in LIBs is still unclear.This has become a bottleneck restricting their promotion and application.Electrochemical impedance spectroscopy(EIS)contains rich electrochemical connotations and significant application prospects,and has attracted widespread atten-tion and research on efficient energy storage systems.Compared to traditional voltage and current data,the state-of-health(SOH)estimation model based on EIS has higher accuracy.This paper categorizes EIS measurement methods based on different principles,introduces the relationship between LIBs aging mechanism and SOH,and compares the advantages of different SOH estimation methods.After a detailed analysis of the latest technologies,a review is given.The insights of this review can deepen the understanding of the relationship between EIS and the aging effect mechanism of LIBs,and promote the development of new energy storage devices and evaluation methods.
基金supported by the National Key Research and Development Program of China(2024YFB4007502)The Foundation for Outstanding Research Groups of Hubei Province of China(2025AFA012)Hubei Provincial Natural Science Foundation,China(2024AFB226)。
文摘The research of the fuel cell and lithium battery hybrid system has attracted more and more researchers because of its advantages of low emission.However,the lower efficiency of energy management has been a critical factor that obstructs the commercialization of the hybrid system.In this study,based on finite element ideas,a solid oxide full cell node model is developed to accurately estimate the operating status of the fuel cell.As for the lithium battery,previous energy management studies generally only focused on the state of charge,however,the state of health is also a key parameter for lithium batteries.In this paper,considering the strong coupling between variables,we use a particle filter algorithm to jointly estimate the state of charge and state of health.To reasonably distribute and manage the energy in the hybrid system,a T-S fuzzy controller for the hybrid system is designed by controlling the state variables in the SOFC model and the state of lithium battery.Finally,our algorithm is verified by using the DSPACE simulation platform.The results show that the hybrid system with an energy management strategy not only meets the real-time sharply changing demands,but also ensures the hybrid system is working efficiently and safely.
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.
