Abuse of Lithium-ion batteries,both physical and electrochemical,can lead to significantly reduced operational capabilities.In some instances,abuse can cause catastrophic failure,including thermal runaway,combustion,a...Abuse of Lithium-ion batteries,both physical and electrochemical,can lead to significantly reduced operational capabilities.In some instances,abuse can cause catastrophic failure,including thermal runaway,combustion,and explosion.Many different test standards that include abuse conditions have been developed,but these generally consider only one condition at a time and only provide go/no-go criteria.In this work,different types of cell abuse are implemented concurrently to determine the extent to which simultaneous abuse conditions aggravate cell degradation and failure.Vibrational loading is chosen to be the consistent type of physical abuse,and the first group of cells is cycled at different vibrational frequencies.The next group of cells is cycled at the same frequencies,with multiple charge pulses occurring during each discharge.The final group of cells is cycled at the same frequencies,with a partial nail puncture occurring near the beginning of cycling.The results show that abusing cells with vibrational loading or vibrational loading with current pulses does not cause a significant decrease in operational capabilities while abusing cells with vibrational loading and a nail puncture drastically reduces operational capabilities.The cells with vibration only experience an increase in internal resistance by a factor of 1.09–1.26,the cells with vibration and current pulses experience an increase in internal resistance by a factor of 1.16–1.23,and all cells from each group reach their rated lifetime of 500 cycles without reaching their end-of-life capacity.However,the cells with vibration and nail puncture experience an increase in internal resistance by a factor of 6.83–22.1,and each cell reaches its end-of-life capacity within 50 cycles.Overall,the results show that testing multiple abuse conditions simultaneously provides a better representation of the extreme limitations of cell operation and should be considered for inclusion in reference test standards.展开更多
Capacity estimation plays a crucial role in battery management systems,and is essential for ensuring the safety and reliability of lithium-sulfur(Li-S)batteries.This paper proposes a method that uses a long short-term...Capacity estimation plays a crucial role in battery management systems,and is essential for ensuring the safety and reliability of lithium-sulfur(Li-S)batteries.This paper proposes a method that uses a long short-term memory(LSTM)neural network to estimate the state of health(SOH)of Li-S batteries.The method uses health features extracted from the charging curve and incre-mental capacity analysis(ICA)as input for the LSTM network.To enhance the robustness and accuracy of the network,the Adam algorithm is employed to optimize specific hyperparameters.Experimental data from three different groups of batteries with varying nominal capac-ities are used to validate the proposed method.The results demonstrate the effectiveness of the method in accurately estimating the capacity degradation of all three batteries.Also,the study examines the impact of different lengths of network training sets on capacity estimation.The results reveal that the ICA-LSTM model achieves a prediction accuracy of mean absolute error 4.6%and mean squared error 0.21%with three different training set lengths of 20%,40%,and 60%.The analysis demonstrates that the lightweight model maintains high SOH estimation accu-racy even with a small training set,and exhibits strong adaptive and generalization capabilities when applied to different Li-S batteries.Overall,the proposed method,supported by experimental validation and analysis,demonstrates its efficacy in ensuring accurate and reliable SOH estimation,thereby enhancing the safety and per-formance of Li-S batteries.Index Terms—Adam algorithm,incremental capacity analysis,Li-S battery,long short-term memory,state of health.展开更多
The growth in computer processing power has made it possible to use time-consuming analysis methods such as incremental dynamic analysis（IDA） with higher accuracy in less time.In an IDA study,a series of earthquake ...The growth in computer processing power has made it possible to use time-consuming analysis methods such as incremental dynamic analysis（IDA） with higher accuracy in less time.In an IDA study,a series of earthquake records are applied to a structure at successively increasing intensity levels,which causes the structure to shift from the elastic state into the inelastic state and finally into collapse.In this way,the limit-states and capacity of a structure can be determined.In the present research,the IDA of a concrete gravity dam considering a nonlinear concrete behavior,and sliding planes within the dam body and at the dam-foundation interface,is performed.The influence of the friction angle and lift joint slope on the response parameters are investigated and the various limit-states of the dam are recognized.It is observed that by introducing a lift joint,the tensile damage can be avoided for the dam structure.The lift joint sliding is essentially independent of the base joint friction angle and the upper ligament over the inclined lift joint slides into the upstream direction in strong earthquakes.展开更多
为了提高锂离子电池健康状态(state of health,SOH)估计的精确度,本研究结合卷积神经网络(convolutional neural networks,CNN)强大的局部特征提取能力和Transformer的序列处理能力,提出了基于多项式特征扩展的CNN-Transformer融合模型...为了提高锂离子电池健康状态(state of health,SOH)估计的精确度,本研究结合卷积神经网络(convolutional neural networks,CNN)强大的局部特征提取能力和Transformer的序列处理能力,提出了基于多项式特征扩展的CNN-Transformer融合模型。该方法提取了与电池容量高度相关的增量容量(incremental capacity,IC)曲线峰值、IC曲线对应电压、面积及充电时间作为健康因子,然后将其进行多项式扩展,增加融合模型对输入特征的非线性处理能力。引入主成分分析法(principal component analysis,PCA)对特征空间进行降维,有利于捕获数据有效信息,减少模型训练时间。采用美国国家宇航局(National Aeronautics and Space Administration,NASA)数据集和马里兰大学数据集,通过加入多项式特征前后的CNN-Transformer模型对比、加入多项式特征的CNN-Transformer模型和单一模型算法对比,验证了加入多项式特征的CNN-Transformer融合算法的有效性和精确度,结果表明提出模型的SOH估计精度相较于未加入多项式特征的CNN-Transformer模型,对于B0005、B0006、B0007、B0018数据集分别提高了38.71%、50.28%、4.71%、17.58%。展开更多
对锂离子电池的健康状态SOH(state of health)进行准确估计是锂离子电池安全稳定运行的重要保障,提出了一种基于容量增量分析ICA(incremental capacity analysis)和Box-Cox变换的锂离子电池SOH估计方法。首先,将电池恒流充电过程的IC曲...对锂离子电池的健康状态SOH(state of health)进行准确估计是锂离子电池安全稳定运行的重要保障,提出了一种基于容量增量分析ICA(incremental capacity analysis)和Box-Cox变换的锂离子电池SOH估计方法。首先,将电池恒流充电过程的IC曲线峰值高度ICP(peak of incremental capacity curve)作为健康特征HF(health factor),数学推导出ICP与健康状态的强相关性。结合卡尔曼滤波算法提取光滑的容量增量曲线。将电池容量衰退过程的前部分周期作为训练周期,通过Box-Cox变换将训练周期的ICP和SOH序列变换成线性关系,然后通过线性拟合来实现剩余周期的SOH估计。在Oxford和NASA数据集上进行实验验证,并与机器学习算法进行对比,结果表明所提方法具有较高的估计精度、较短的计算时间和较强的鲁棒性。展开更多
Current building design codes allow the appearance of structural and nonstructural damage under design basis earthquakes.The research regarding probabilistic seismic loss estimation in domestic building structure is u...Current building design codes allow the appearance of structural and nonstructural damage under design basis earthquakes.The research regarding probabilistic seismic loss estimation in domestic building structure is urgent.The evaluation in this paper is based on a 11-story reinforced concrete office building,incremental dynamic analysis(IDA)is conducted in Perform 3D program using models capable to simulate all possible limit states up to collapse.Next,the probability distribution of post-earthquake casualties,rebuild costs repair costs and business downtime loss are calculated in PACT software for the studied building considering the modified component vulnerability groups and population flow models.The evaluation procedure can also shed light on other types of buildings in China.For non-typical functional building structures,this article proposes to build a finite element model of structural components and to classify the vulnerability groups based on the construction drawings,and to supply and improve the vulnerability library of appendages in FEMA P-58 according to the actual situation.In this way,the application scope of building seismic performance evaluation can be expanded.展开更多
本文中对纯电动汽车用磷酸铁锂电池SOC估算方法进行研究。首先,利用基本的测试手段测得反映电池外特性的充放电电压曲线(V vs Q),并用它求得反映电池电化学特性的容量增量曲线(ΔQ/ΔV vs V)。接着,采用容量增量分析法研究充放电倍率、...本文中对纯电动汽车用磷酸铁锂电池SOC估算方法进行研究。首先,利用基本的测试手段测得反映电池外特性的充放电电压曲线(V vs Q),并用它求得反映电池电化学特性的容量增量曲线(ΔQ/ΔV vs V)。接着,采用容量增量分析法研究充放电倍率、温度和老化程度对电池性能的影响。最后,建立了电池内部相变阶段的容量增量峰与电池SOC的对应关系,并利用这一关系来估算电池SOC,为电动汽车制定动力电池管理策略提供依据。展开更多
基金Funding for this research has been provided by the Office of Naval Research(ONR)under the Grant N00014-20-1-2227(Program Manager:Dr.Maria Medeiros and Dr.Corey Love).
文摘Abuse of Lithium-ion batteries,both physical and electrochemical,can lead to significantly reduced operational capabilities.In some instances,abuse can cause catastrophic failure,including thermal runaway,combustion,and explosion.Many different test standards that include abuse conditions have been developed,but these generally consider only one condition at a time and only provide go/no-go criteria.In this work,different types of cell abuse are implemented concurrently to determine the extent to which simultaneous abuse conditions aggravate cell degradation and failure.Vibrational loading is chosen to be the consistent type of physical abuse,and the first group of cells is cycled at different vibrational frequencies.The next group of cells is cycled at the same frequencies,with multiple charge pulses occurring during each discharge.The final group of cells is cycled at the same frequencies,with a partial nail puncture occurring near the beginning of cycling.The results show that abusing cells with vibrational loading or vibrational loading with current pulses does not cause a significant decrease in operational capabilities while abusing cells with vibrational loading and a nail puncture drastically reduces operational capabilities.The cells with vibration only experience an increase in internal resistance by a factor of 1.09–1.26,the cells with vibration and current pulses experience an increase in internal resistance by a factor of 1.16–1.23,and all cells from each group reach their rated lifetime of 500 cycles without reaching their end-of-life capacity.However,the cells with vibration and nail puncture experience an increase in internal resistance by a factor of 6.83–22.1,and each cell reaches its end-of-life capacity within 50 cycles.Overall,the results show that testing multiple abuse conditions simultaneously provides a better representation of the extreme limitations of cell operation and should be considered for inclusion in reference test standards.
基金This work is supported by the Zhejiang Province Natural Science Foundation(No.LY22E070007)National Natural Science Foundation of China(No.52007170).
文摘Capacity estimation plays a crucial role in battery management systems,and is essential for ensuring the safety and reliability of lithium-sulfur(Li-S)batteries.This paper proposes a method that uses a long short-term memory(LSTM)neural network to estimate the state of health(SOH)of Li-S batteries.The method uses health features extracted from the charging curve and incre-mental capacity analysis(ICA)as input for the LSTM network.To enhance the robustness and accuracy of the network,the Adam algorithm is employed to optimize specific hyperparameters.Experimental data from three different groups of batteries with varying nominal capac-ities are used to validate the proposed method.The results demonstrate the effectiveness of the method in accurately estimating the capacity degradation of all three batteries.Also,the study examines the impact of different lengths of network training sets on capacity estimation.The results reveal that the ICA-LSTM model achieves a prediction accuracy of mean absolute error 4.6%and mean squared error 0.21%with three different training set lengths of 20%,40%,and 60%.The analysis demonstrates that the lightweight model maintains high SOH estimation accu-racy even with a small training set,and exhibits strong adaptive and generalization capabilities when applied to different Li-S batteries.Overall,the proposed method,supported by experimental validation and analysis,demonstrates its efficacy in ensuring accurate and reliable SOH estimation,thereby enhancing the safety and per-formance of Li-S batteries.Index Terms—Adam algorithm,incremental capacity analysis,Li-S battery,long short-term memory,state of health.
文摘The growth in computer processing power has made it possible to use time-consuming analysis methods such as incremental dynamic analysis（IDA） with higher accuracy in less time.In an IDA study,a series of earthquake records are applied to a structure at successively increasing intensity levels,which causes the structure to shift from the elastic state into the inelastic state and finally into collapse.In this way,the limit-states and capacity of a structure can be determined.In the present research,the IDA of a concrete gravity dam considering a nonlinear concrete behavior,and sliding planes within the dam body and at the dam-foundation interface,is performed.The influence of the friction angle and lift joint slope on the response parameters are investigated and the various limit-states of the dam are recognized.It is observed that by introducing a lift joint,the tensile damage can be avoided for the dam structure.The lift joint sliding is essentially independent of the base joint friction angle and the upper ligament over the inclined lift joint slides into the upstream direction in strong earthquakes.
文摘为了提高锂离子电池健康状态(state of health,SOH)估计的精确度,本研究结合卷积神经网络(convolutional neural networks,CNN)强大的局部特征提取能力和Transformer的序列处理能力,提出了基于多项式特征扩展的CNN-Transformer融合模型。该方法提取了与电池容量高度相关的增量容量(incremental capacity,IC)曲线峰值、IC曲线对应电压、面积及充电时间作为健康因子,然后将其进行多项式扩展,增加融合模型对输入特征的非线性处理能力。引入主成分分析法(principal component analysis,PCA)对特征空间进行降维,有利于捕获数据有效信息,减少模型训练时间。采用美国国家宇航局(National Aeronautics and Space Administration,NASA)数据集和马里兰大学数据集,通过加入多项式特征前后的CNN-Transformer模型对比、加入多项式特征的CNN-Transformer模型和单一模型算法对比,验证了加入多项式特征的CNN-Transformer融合算法的有效性和精确度,结果表明提出模型的SOH估计精度相较于未加入多项式特征的CNN-Transformer模型,对于B0005、B0006、B0007、B0018数据集分别提高了38.71%、50.28%、4.71%、17.58%。
文摘对锂离子电池的健康状态SOH(state of health)进行准确估计是锂离子电池安全稳定运行的重要保障,提出了一种基于容量增量分析ICA(incremental capacity analysis)和Box-Cox变换的锂离子电池SOH估计方法。首先,将电池恒流充电过程的IC曲线峰值高度ICP(peak of incremental capacity curve)作为健康特征HF(health factor),数学推导出ICP与健康状态的强相关性。结合卡尔曼滤波算法提取光滑的容量增量曲线。将电池容量衰退过程的前部分周期作为训练周期,通过Box-Cox变换将训练周期的ICP和SOH序列变换成线性关系,然后通过线性拟合来实现剩余周期的SOH估计。在Oxford和NASA数据集上进行实验验证,并与机器学习算法进行对比,结果表明所提方法具有较高的估计精度、较短的计算时间和较强的鲁棒性。
基金This research has been supported by the National Natural ScienceFoundation of China (Grant No. 51778135 )the Natural Science Foundation of JiangsuProvince (Grant No. BK20160207)+1 种基金Aeronautical Science Foundation of China (GrantNo. 20130969010)the Fundamental Research Funds for the Central Universities andPostgraduate Research & Practice Innovation Program of Jiangsu Province (Grant No.KYCX18_0113 and KYLX16_0253).
文摘Current building design codes allow the appearance of structural and nonstructural damage under design basis earthquakes.The research regarding probabilistic seismic loss estimation in domestic building structure is urgent.The evaluation in this paper is based on a 11-story reinforced concrete office building,incremental dynamic analysis(IDA)is conducted in Perform 3D program using models capable to simulate all possible limit states up to collapse.Next,the probability distribution of post-earthquake casualties,rebuild costs repair costs and business downtime loss are calculated in PACT software for the studied building considering the modified component vulnerability groups and population flow models.The evaluation procedure can also shed light on other types of buildings in China.For non-typical functional building structures,this article proposes to build a finite element model of structural components and to classify the vulnerability groups based on the construction drawings,and to supply and improve the vulnerability library of appendages in FEMA P-58 according to the actual situation.In this way,the application scope of building seismic performance evaluation can be expanded.
文摘本文中对纯电动汽车用磷酸铁锂电池SOC估算方法进行研究。首先,利用基本的测试手段测得反映电池外特性的充放电电压曲线(V vs Q),并用它求得反映电池电化学特性的容量增量曲线(ΔQ/ΔV vs V)。接着,采用容量增量分析法研究充放电倍率、温度和老化程度对电池性能的影响。最后,建立了电池内部相变阶段的容量增量峰与电池SOC的对应关系,并利用这一关系来估算电池SOC,为电动汽车制定动力电池管理策略提供依据。
