To solve the problem of life estimation of reinforced concrete （RC） members after fire, an analysis is made of the resistance of RC members after fire. On basis of the resistance, the life of RC members after fire i...To solve the problem of life estimation of reinforced concrete （RC） members after fire, an analysis is made of the resistance of RC members after fire. On basis of the resistance, the life of RC members after fire is analyzed by using JC （Jukes and Cantor） method. Then the calculation models for the resistance and the life estimation of RC members after fire are put forward, and an example analysis proves their reliability and accuracy.展开更多
Internet of Things systems generate a large amount of sensor data that needs to be analyzed for extracting useful insights on the health status of the machine under consideration.Sensor data of all possible states of ...Internet of Things systems generate a large amount of sensor data that needs to be analyzed for extracting useful insights on the health status of the machine under consideration.Sensor data of all possible states of a system are used for building machine learning models.These models are further used to predict the possible downtime for proactive action on the system condition.Aircraft engine data from run to failure is used in the current study.The run to failure data includes states like new installation,stable operation,first reported issue,erroneous operation,and final failure.In the present work,the non-linear multivariate sensor data is used to understand the health status and anomalous behavior.The methodology is based on different sampling sizes to obtain optimum results with great accuracy.The time series of each sensor is converted to a 2D image with a specific time window.Converted Images would represent the health of a system in higher-dimensional space.The created images were fed to Convolutional Neural Network,which includes both time variation and space variation of each sensed parameter.Using these created images,a model for estimating the remaining life of the aircraft is developed.Further,the proposed net is also used for predicting the number of engines that would fail in the given time window.The current methodology is useful in avoiding the health index generation for predicting the remaining useful life of the industrial components.Better accuracy in the classification of components is achieved using the TimeImagenet-based approach.展开更多
Remaining useful life(RUL)prediction is an advanced technique for system maintenance scheduling.Most of existing RUL prediction methods are only interested in the precision of RUL estimation;the adverse impact of over...Remaining useful life(RUL)prediction is an advanced technique for system maintenance scheduling.Most of existing RUL prediction methods are only interested in the precision of RUL estimation;the adverse impact of overestimated RUL on maintenance scheduling is not of concern.In this work,an RUL estimation method with risk-averse adaptation is developed which can reduce the over-estimation rate while maintaining a reasonable under-estimation level.The proposed method includes a module of degradation feature selection to obtain crucial features which reflect system degradation trends.Then,the latent structure between the degradation features and the RUL labels is modeled by a support vector regression(SVR)model and a long short-term memory(LSTM)network,respectively.To enhance the prediction robustness and increase its marginal utility,the SVR model and the LSTM model are integrated to generate a hybrid model via three connection parameters.By designing a cost function with penalty mechanism,the three parameters are determined using a modified grey wolf optimization algorithm.In addition,a cost metric is proposed to measure the benefit of such a risk-averse predictive maintenance method.Verification is done using an aero-engine data set from NASA.The results show the feasibility and effectiveness of the proposed RUL estimation method and the predictive maintenance strategy.展开更多
Accurately predicting the remaining useful life(RUL)of bearings in mining rotating equipment is vital for mining enterprises.This research aims to distinguish the features associated with the RUL of bearings and propo...Accurately predicting the remaining useful life(RUL)of bearings in mining rotating equipment is vital for mining enterprises.This research aims to distinguish the features associated with the RUL of bearings and propose a prediction model based on these selected features.This study proposes a hybrid predictive model to assess the RUL of rolling element bearings.The proposed model begins with the pre-processing of bearing vibration signals to reconstruct sixty time-domain features.The hybrid model selects relevant features from the sixty time-domain features of the vibration signal by adopting the RReliefF feature selection algorithm.Subsequently,the extreme learning machine(ELM)approach is applied to develop a predictive model of RUL based on the optimal features.The model is trained by optimizing its parameters via the grid search approach.The training datasets are adjusted to make them most suitable for the regression model using the cross-validation method.The proposed hybrid model is analyzed and validated using the vibration data taken from the public XJTU-SY rolling element-bearing database.The comparison is constructed with other traditional models.The experimental test results demonstrated that the proposed approach can predict the RUL of bearings with a reliable degree of accuracy.展开更多
Lithium-ion batteries have become the third-generation space batteries and are widely utilized in a series of spacecraft. Remaining Useful Life （RUL） estimation is essential to a spacecraft as the battery is a criti...Lithium-ion batteries have become the third-generation space batteries and are widely utilized in a series of spacecraft. Remaining Useful Life （RUL） estimation is essential to a spacecraft as the battery is a critical part and determines the lifetime and reliability. The Relevance Vector Machine （RVM） is a data-driven algorithm used to estimate a battery＇s RUL due to its sparse feature and uncertainty management capability. Especially, some of the regressive cases indicate that the RVM can obtain a better short-term prediction performance rather than long-term prediction. As a nonlinear kernel learning algorithm, the coefficient matrix and relevance vectors are fixed once the RVM training is conducted. Moreover, the RVM can be simply influenced by the noise with the training data. Thus, this work proposes an iterative updated approach to improve the long-term prediction performance for a battery＇s RUL prediction. Firstly, when a new estimator is output by the RVM, the Kalman filter is applied to optimize this estimator with a physical degradation model. Then, this optimized estimator is added into the training set as an on-line sample, the RVM model is re-trained, and the coefficient matrix and relevance vectors can be dynamically adjusted to make next iterative prediction. Experimental results with a commercial battery test data set and a satellite battery data set both indicate that the proposed method can achieve a better performance for RUL estimation.展开更多
In the industrial fields, the mechanical equipment will inevitably wear out in the process of operation. With the accumulation of losses, the probability of equipment failure is increasing. Therefore, if the remaining...In the industrial fields, the mechanical equipment will inevitably wear out in the process of operation. With the accumulation of losses, the probability of equipment failure is increasing. Therefore, if the remaining useful life(RUL) of the equipment can be accurately predicted, the equipment can be maintained in time to avoid the downtime caused by equipment failure and greatly improve the production efficiency of enterprises. This paper aims to use independently recurrent neural network(IndRNN) to learn health degradation of turbofan engine and make accurate predictions of its RUL, which not only effectively solves the problem of gradient explosion and vanishing, but also increases the interpretability of neural networks. IndRNN can be used to process longer time series which matches the scene with high frequency sampling sensor in industrial practical applications. The results demonstrate that IndRNN for RUL estimation significantly outperforms traditional approaches, as well as convolutional neural network(CNN) and long short-term memory network(LSTM) for RUL estimation.展开更多
To explore the influence of path deflection on crack propagation,a path planning algorithm is presented to calculate the crack growth length.The fatigue crack growth life of metal matrix composites(MMCs)is estimated b...To explore the influence of path deflection on crack propagation,a path planning algorithm is presented to calculate the crack growth length.The fatigue crack growth life of metal matrix composites(MMCs)is estimated based on an improved Paris formula.Considering the different expansion coefficient of different materials,the unequal shrinkage will lead to residual stress when the composite is molded and cooled.The crack growth model is improved by the modified stress ratio based on residual stress.The Dijkstra algorithm is introduced to avoid the cracks passing through the strengthening base and the characteristics of crack steps.This model can be extended to predict crack growth length for other similarly-structured composite materials.The shortest path of crack growth is simulated by using path planning algorithm,and the fatigue life of composites is calculated based on the shortest path and improved model.And the residual stress caused by temperature change is considered to improve the fatigue crack growth model in the material.The improved model can well predict the fatigue life curve of composites.By analyzing the fatigue life of composites,it is found that there is a certain regularity based on metal materials,and the new fatigue prediction model can also reflect this regularity.展开更多
Suggests some calculating formulas and methods with respect to the damage evolvingrate da / dN|i and the fatigue life and in varied history from uncrack to microcrackinitiation until fracture for a crankshaft, which ...Suggests some calculating formulas and methods with respect to the damage evolvingrate da / dN|i and the fatigue life and in varied history from uncrack to microcrackinitiation until fracture for a crankshaft, which are suitable to stress concentration positionsabout its journal fillets and oil holes on a crankshaft, that it is undergone to bending, twistingand shearing loading and subjected to unsymmetric cyclic many-stage loading. Last the total lifein whole process is estimated by展开更多
Real time remaining useful life(RUL) prediction based on condition monitoring is an essential part in condition based maintenance(CBM). In the current methods about the real time RUL prediction of the nonlinear degrad...Real time remaining useful life(RUL) prediction based on condition monitoring is an essential part in condition based maintenance(CBM). In the current methods about the real time RUL prediction of the nonlinear degradation process, the measurement error is not considered and forecasting uncertainty is large. Therefore, an approximate analytical RUL distribution in a closed-form of a nonlinear Wiener based degradation process with measurement errors was proposed. The maximum likelihood estimation approach was used to estimate the unknown fixed parameters in the proposed model. When the newly observed data are available, the random parameter is updated by the Bayesian method to make the estimation adapt to the item's individual characteristic and reduce the uncertainty of the estimation. The simulation results show that considering measurement errors in the degradation process can significantly improve the accuracy of real time RUL prediction.展开更多
As to the sonic fatigue problem of an aero-engine combustor liner structure under the random acoustic loadings,an effective method for predicting the fatigue life of a structure under random loadings was studied.First...As to the sonic fatigue problem of an aero-engine combustor liner structure under the random acoustic loadings,an effective method for predicting the fatigue life of a structure under random loadings was studied.Firstly,the probability distribution of Von Mises stress of thin-walled structure under random loadings was studied,analysis suggested that probability density function of Von Mises stress process accord approximately with two-parameter Weibull distribution.The formula for calculating Weibull parameters were given.Based on the Miner linear theory,the method for predicting the random sonic fatigue life based on the stress probability density was developed,and the model for fatigue life prediction was constructed.As an example,an aero-engine combustor liner structure was considered.The power spectrum density(PSD) of the vibrational stress response was calculated by using the coupled FEM/BEM(finite element method/boundary element method) model,the fatigue life was estimated by using the constructed model.And considering the influence of the wide frequency band,the calculated results were modified.Comparetive analysis shows that the estimated results of sonic fatigue of the combustor liner structure by using Weibull distribution of Von Mises stress are more conservative than using Dirlik distribution to some extend.The results show that the methods presented in this paper are practical for the random fatigue life analysis of the aeronautical thin-walled structures.展开更多
The low-cycle fatigue （LCF） behavior of directionally solidified nickel-based superalloy Ti-6A1-4V was studied under bare and electron beam welding condi- tions at room temperature. Results show that： （1） under t...The low-cycle fatigue （LCF） behavior of directionally solidified nickel-based superalloy Ti-6A1-4V was studied under bare and electron beam welding condi- tions at room temperature. Results show that： （1） under the same test conditions, all the joints exhibit lower LCF lifetime than Ti-6A1-4V; （2） the failure of welded structures is mainly ascribed to the welding defect. A novel lifetime prediction methodology based on continuum damage mechanics is proposed to predict the lifetime of Ti-6A1-4V and its welded joints.展开更多
A multi-layer adaptive optimizing parameters algorithm is developed forimproving least squares support vector machines (LS-SVM) , and a military aircraft life-cycle-cost(LCC) intelligent estimation model is proposed b...A multi-layer adaptive optimizing parameters algorithm is developed forimproving least squares support vector machines (LS-SVM) , and a military aircraft life-cycle-cost(LCC) intelligent estimation model is proposed based on the improved LS-SVM. The intelligent costestimation process is divided into three steps in the model. In the first step, a cost-drive-factorneeds to be selected, which is significant for cost estimation. In the second step, militaryaircraft training samples within costs and cost-drive-factor set are obtained by the LS-SVM. Thenthe model can be used for new type aircraft cost estimation. Chinese military aircraft costs areestimated in the paper. The results show that the estimated costs by the new model are closer to thetrue costs than that of the traditionally used methods.展开更多
The paper builds up a cost-benefit measuring model of green products in manufacturing industry throughout its full life cycle, which can quantify green products＇ cost and benefit completely and correctly under the ci...The paper builds up a cost-benefit measuring model of green products in manufacturing industry throughout its full life cycle, which can quantify green products＇ cost and benefit completely and correctly under the circumstance of satisfying enterprise, customer, environment and society. It also puts forth an operable method to estimate social benefit by opportunity cost and establishes a profit maximization-programming model. The model can be applied to justify whether some kinds of green products should be developed and produced.展开更多
The cost of Energy Storage System(ESS)for frequency regulation is difficult to calculate due to battery’s degradation when an ESS is in grid-connected operation.To solve this problem,the influence mechanism of actual...The cost of Energy Storage System(ESS)for frequency regulation is difficult to calculate due to battery’s degradation when an ESS is in grid-connected operation.To solve this problem,the influence mechanism of actual operating conditions on the life degradation of Li-ion battery energy storage is analyzed.A control strategy of Li-ion ESS participating in grid frequency regulation is constructed and a cost accounting model for frequency regulation considering the effect of battery life degradation is established.The estimated operating life and annual average cost of the Li-ion ESS under different dead bands and SOC set-points are calculated.The case studies show that the estimated operating life of the Li-ion ESS under the actual operating condition differs significantly from the nominal life provided by the manufacturer under the standard condition and the full discharge mode.This paper provides an accurate costing method for the ESS participating in grid frequency regulation to help the promotion of the ESS to participate in the ancillary service market.展开更多
文摘To solve the problem of life estimation of reinforced concrete （RC） members after fire, an analysis is made of the resistance of RC members after fire. On basis of the resistance, the life of RC members after fire is analyzed by using JC （Jukes and Cantor） method. Then the calculation models for the resistance and the life estimation of RC members after fire are put forward, and an example analysis proves their reliability and accuracy.
文摘Internet of Things systems generate a large amount of sensor data that needs to be analyzed for extracting useful insights on the health status of the machine under consideration.Sensor data of all possible states of a system are used for building machine learning models.These models are further used to predict the possible downtime for proactive action on the system condition.Aircraft engine data from run to failure is used in the current study.The run to failure data includes states like new installation,stable operation,first reported issue,erroneous operation,and final failure.In the present work,the non-linear multivariate sensor data is used to understand the health status and anomalous behavior.The methodology is based on different sampling sizes to obtain optimum results with great accuracy.The time series of each sensor is converted to a 2D image with a specific time window.Converted Images would represent the health of a system in higher-dimensional space.The created images were fed to Convolutional Neural Network,which includes both time variation and space variation of each sensed parameter.Using these created images,a model for estimating the remaining life of the aircraft is developed.Further,the proposed net is also used for predicting the number of engines that would fail in the given time window.The current methodology is useful in avoiding the health index generation for predicting the remaining useful life of the industrial components.Better accuracy in the classification of components is achieved using the TimeImagenet-based approach.
基金support by Natural Science Foundation of China(61873122)。
文摘Remaining useful life(RUL)prediction is an advanced technique for system maintenance scheduling.Most of existing RUL prediction methods are only interested in the precision of RUL estimation;the adverse impact of overestimated RUL on maintenance scheduling is not of concern.In this work,an RUL estimation method with risk-averse adaptation is developed which can reduce the over-estimation rate while maintaining a reasonable under-estimation level.The proposed method includes a module of degradation feature selection to obtain crucial features which reflect system degradation trends.Then,the latent structure between the degradation features and the RUL labels is modeled by a support vector regression(SVR)model and a long short-term memory(LSTM)network,respectively.To enhance the prediction robustness and increase its marginal utility,the SVR model and the LSTM model are integrated to generate a hybrid model via three connection parameters.By designing a cost function with penalty mechanism,the three parameters are determined using a modified grey wolf optimization algorithm.In addition,a cost metric is proposed to measure the benefit of such a risk-averse predictive maintenance method.Verification is done using an aero-engine data set from NASA.The results show the feasibility and effectiveness of the proposed RUL estimation method and the predictive maintenance strategy.
基金supported by the Anhui Provincial Key Research and Development Project(202104a07020005)the University Synergy Innovation Program of Anhui Province(GXXT-2022-019)+1 种基金the Institute of Energy,Hefei Comprehensive National Science Center under Grant No.21KZS217Scientific Research Foundation for High-Level Talents of Anhui University of Science and Technology(13210024).
文摘Accurately predicting the remaining useful life(RUL)of bearings in mining rotating equipment is vital for mining enterprises.This research aims to distinguish the features associated with the RUL of bearings and propose a prediction model based on these selected features.This study proposes a hybrid predictive model to assess the RUL of rolling element bearings.The proposed model begins with the pre-processing of bearing vibration signals to reconstruct sixty time-domain features.The hybrid model selects relevant features from the sixty time-domain features of the vibration signal by adopting the RReliefF feature selection algorithm.Subsequently,the extreme learning machine(ELM)approach is applied to develop a predictive model of RUL based on the optimal features.The model is trained by optimizing its parameters via the grid search approach.The training datasets are adjusted to make them most suitable for the regression model using the cross-validation method.The proposed hybrid model is analyzed and validated using the vibration data taken from the public XJTU-SY rolling element-bearing database.The comparison is constructed with other traditional models.The experimental test results demonstrated that the proposed approach can predict the RUL of bearings with a reliable degree of accuracy.
基金co-supported in part by the National Natural Science Foundation of China (Nos. 61301205 and 61571160)the Natural Scientific Research Innovation Foundation at Harbin Institute of Technology (No. HIT.NSRIF.2014017)
文摘Lithium-ion batteries have become the third-generation space batteries and are widely utilized in a series of spacecraft. Remaining Useful Life （RUL） estimation is essential to a spacecraft as the battery is a critical part and determines the lifetime and reliability. The Relevance Vector Machine （RVM） is a data-driven algorithm used to estimate a battery＇s RUL due to its sparse feature and uncertainty management capability. Especially, some of the regressive cases indicate that the RVM can obtain a better short-term prediction performance rather than long-term prediction. As a nonlinear kernel learning algorithm, the coefficient matrix and relevance vectors are fixed once the RVM training is conducted. Moreover, the RVM can be simply influenced by the noise with the training data. Thus, this work proposes an iterative updated approach to improve the long-term prediction performance for a battery＇s RUL prediction. Firstly, when a new estimator is output by the RVM, the Kalman filter is applied to optimize this estimator with a physical degradation model. Then, this optimized estimator is added into the training set as an on-line sample, the RVM model is re-trained, and the coefficient matrix and relevance vectors can be dynamically adjusted to make next iterative prediction. Experimental results with a commercial battery test data set and a satellite battery data set both indicate that the proposed method can achieve a better performance for RUL estimation.
基金supported by 2019 Industrial Internet Innovation Development Project of Ministry of Industry and Information Technology of China “Comprehensive Security Defense Platform Project for Industrial/Enterprise Networks”。
文摘In the industrial fields, the mechanical equipment will inevitably wear out in the process of operation. With the accumulation of losses, the probability of equipment failure is increasing. Therefore, if the remaining useful life(RUL) of the equipment can be accurately predicted, the equipment can be maintained in time to avoid the downtime caused by equipment failure and greatly improve the production efficiency of enterprises. This paper aims to use independently recurrent neural network(IndRNN) to learn health degradation of turbofan engine and make accurate predictions of its RUL, which not only effectively solves the problem of gradient explosion and vanishing, but also increases the interpretability of neural networks. IndRNN can be used to process longer time series which matches the scene with high frequency sampling sensor in industrial practical applications. The results demonstrate that IndRNN for RUL estimation significantly outperforms traditional approaches, as well as convolutional neural network(CNN) and long short-term memory network(LSTM) for RUL estimation.
基金National Natural Science Foundation of China(Grant No.51675324)。
文摘To explore the influence of path deflection on crack propagation,a path planning algorithm is presented to calculate the crack growth length.The fatigue crack growth life of metal matrix composites(MMCs)is estimated based on an improved Paris formula.Considering the different expansion coefficient of different materials,the unequal shrinkage will lead to residual stress when the composite is molded and cooled.The crack growth model is improved by the modified stress ratio based on residual stress.The Dijkstra algorithm is introduced to avoid the cracks passing through the strengthening base and the characteristics of crack steps.This model can be extended to predict crack growth length for other similarly-structured composite materials.The shortest path of crack growth is simulated by using path planning algorithm,and the fatigue life of composites is calculated based on the shortest path and improved model.And the residual stress caused by temperature change is considered to improve the fatigue crack growth model in the material.The improved model can well predict the fatigue life curve of composites.By analyzing the fatigue life of composites,it is found that there is a certain regularity based on metal materials,and the new fatigue prediction model can also reflect this regularity.
文摘Suggests some calculating formulas and methods with respect to the damage evolvingrate da / dN|i and the fatigue life and in varied history from uncrack to microcrackinitiation until fracture for a crankshaft, which are suitable to stress concentration positionsabout its journal fillets and oil holes on a crankshaft, that it is undergone to bending, twistingand shearing loading and subjected to unsymmetric cyclic many-stage loading. Last the total lifein whole process is estimated by
基金Projects(51475462,61374138,61370031)supported by the National Natural Science Foundation of China
文摘Real time remaining useful life(RUL) prediction based on condition monitoring is an essential part in condition based maintenance(CBM). In the current methods about the real time RUL prediction of the nonlinear degradation process, the measurement error is not considered and forecasting uncertainty is large. Therefore, an approximate analytical RUL distribution in a closed-form of a nonlinear Wiener based degradation process with measurement errors was proposed. The maximum likelihood estimation approach was used to estimate the unknown fixed parameters in the proposed model. When the newly observed data are available, the random parameter is updated by the Bayesian method to make the estimation adapt to the item's individual characteristic and reduce the uncertainty of the estimation. The simulation results show that considering measurement errors in the degradation process can significantly improve the accuracy of real time RUL prediction.
基金Supported by the National Aviation Fundamental Science Foundation of China(No.02C54007)
文摘As to the sonic fatigue problem of an aero-engine combustor liner structure under the random acoustic loadings,an effective method for predicting the fatigue life of a structure under random loadings was studied.Firstly,the probability distribution of Von Mises stress of thin-walled structure under random loadings was studied,analysis suggested that probability density function of Von Mises stress process accord approximately with two-parameter Weibull distribution.The formula for calculating Weibull parameters were given.Based on the Miner linear theory,the method for predicting the random sonic fatigue life based on the stress probability density was developed,and the model for fatigue life prediction was constructed.As an example,an aero-engine combustor liner structure was considered.The power spectrum density(PSD) of the vibrational stress response was calculated by using the coupled FEM/BEM(finite element method/boundary element method) model,the fatigue life was estimated by using the constructed model.And considering the influence of the wide frequency band,the calculated results were modified.Comparetive analysis shows that the estimated results of sonic fatigue of the combustor liner structure by using Weibull distribution of Von Mises stress are more conservative than using Dirlik distribution to some extend.The results show that the methods presented in this paper are practical for the random fatigue life analysis of the aeronautical thin-walled structures.
基金financially supported by the Hi-Tech Research and Development Program of China(No.2012AA052102)the Innovation Foundation for Ph.D.Graduates of Beihang University(No.YWF-14-YJSY-016)the Program of International Science and Technology Cooperation of China(No.2013DFA61590)
文摘The low-cycle fatigue （LCF） behavior of directionally solidified nickel-based superalloy Ti-6A1-4V was studied under bare and electron beam welding condi- tions at room temperature. Results show that： （1） under the same test conditions, all the joints exhibit lower LCF lifetime than Ti-6A1-4V; （2） the failure of welded structures is mainly ascribed to the welding defect. A novel lifetime prediction methodology based on continuum damage mechanics is proposed to predict the lifetime of Ti-6A1-4V and its welded joints.
文摘A multi-layer adaptive optimizing parameters algorithm is developed forimproving least squares support vector machines (LS-SVM) , and a military aircraft life-cycle-cost(LCC) intelligent estimation model is proposed based on the improved LS-SVM. The intelligent costestimation process is divided into three steps in the model. In the first step, a cost-drive-factorneeds to be selected, which is significant for cost estimation. In the second step, militaryaircraft training samples within costs and cost-drive-factor set are obtained by the LS-SVM. Thenthe model can be used for new type aircraft cost estimation. Chinese military aircraft costs areestimated in the paper. The results show that the estimated costs by the new model are closer to thetrue costs than that of the traditionally used methods.
基金This paper is supported by National Nature Science Foundation of China （No.70472034）.
文摘The paper builds up a cost-benefit measuring model of green products in manufacturing industry throughout its full life cycle, which can quantify green products＇ cost and benefit completely and correctly under the circumstance of satisfying enterprise, customer, environment and society. It also puts forth an operable method to estimate social benefit by opportunity cost and establishes a profit maximization-programming model. The model can be applied to justify whether some kinds of green products should be developed and produced.
基金This work is supported in part by Industrial Innovation of Jilin Province Development and Reform Commission(2017C017-2)Science&Technology Project of SGCC(Key technology and application of super large capac-ity battery energy storage system),and Jilin Provincial“13th Five-Year Plan”Science and Technology Project([2016]88).
文摘The cost of Energy Storage System(ESS)for frequency regulation is difficult to calculate due to battery’s degradation when an ESS is in grid-connected operation.To solve this problem,the influence mechanism of actual operating conditions on the life degradation of Li-ion battery energy storage is analyzed.A control strategy of Li-ion ESS participating in grid frequency regulation is constructed and a cost accounting model for frequency regulation considering the effect of battery life degradation is established.The estimated operating life and annual average cost of the Li-ion ESS under different dead bands and SOC set-points are calculated.The case studies show that the estimated operating life of the Li-ion ESS under the actual operating condition differs significantly from the nominal life provided by the manufacturer under the standard condition and the full discharge mode.This paper provides an accurate costing method for the ESS participating in grid frequency regulation to help the promotion of the ESS to participate in the ancillary service market.
