提出一种基于系统状态空间模型和归一化鲁棒最小均方根(NR-LMS,Normalized Robust Least Mean Square)理论的动力学结构参数辨识方法.利用系统的输入-输出数据建立其Hankel-Toeplitz模型,利用NR-LMS算法得到该模型参数的估计并求得系统...提出一种基于系统状态空间模型和归一化鲁棒最小均方根(NR-LMS,Normalized Robust Least Mean Square)理论的动力学结构参数辨识方法.利用系统的输入-输出数据建立其Hankel-Toeplitz模型,利用NR-LMS算法得到该模型参数的估计并求得系统的Hankel矩阵,对Hankel矩阵进行奇异值分解即可确定系统的阶次,进而确定系统状态空间模型的参数.仿真研究和实验结果表明,此方法可以准确、快速地提取出结构的参数,且抗噪能力较强.展开更多
In order to characterize the voltage behavior of a lithium-ion battery for on-board electric vehicle battery management and control applications,a battery model with a moderate complexity was established.The battery o...In order to characterize the voltage behavior of a lithium-ion battery for on-board electric vehicle battery management and control applications,a battery model with a moderate complexity was established.The battery open circuit voltage (OCV) as a function of state of charge (SOC) was depicted by the Nernst equation.An equivalent circuit network was adopted to describe the polarization effect of the lithium-ion battery.A linear identifiable formulation of the battery model was derived by discretizing the frequent-domain description of the battery model.The recursive least square algorithm with forgetting was applied to implement the on-line parameter calibration.The validation results show that the on-line calibrated model can accurately predict the dynamic voltage behavior of the lithium-ion battery.The maximum and mean relative errors are 1.666% and 0.01%,respectively,in a hybrid pulse test,while 1.933% and 0.062%,respectively,in a transient power test.The on-line parameter calibration method thereby can ensure that the model possesses an acceptable robustness to varied battery loading profiles.展开更多
文摘提出一种基于系统状态空间模型和归一化鲁棒最小均方根(NR-LMS,Normalized Robust Least Mean Square)理论的动力学结构参数辨识方法.利用系统的输入-输出数据建立其Hankel-Toeplitz模型,利用NR-LMS算法得到该模型参数的估计并求得系统的Hankel矩阵,对Hankel矩阵进行奇异值分解即可确定系统的阶次,进而确定系统状态空间模型的参数.仿真研究和实验结果表明,此方法可以准确、快速地提取出结构的参数,且抗噪能力较强.
基金Project(50905015) supported by the National Natural Science Foundation of China
文摘In order to characterize the voltage behavior of a lithium-ion battery for on-board electric vehicle battery management and control applications,a battery model with a moderate complexity was established.The battery open circuit voltage (OCV) as a function of state of charge (SOC) was depicted by the Nernst equation.An equivalent circuit network was adopted to describe the polarization effect of the lithium-ion battery.A linear identifiable formulation of the battery model was derived by discretizing the frequent-domain description of the battery model.The recursive least square algorithm with forgetting was applied to implement the on-line parameter calibration.The validation results show that the on-line calibrated model can accurately predict the dynamic voltage behavior of the lithium-ion battery.The maximum and mean relative errors are 1.666% and 0.01%,respectively,in a hybrid pulse test,while 1.933% and 0.062%,respectively,in a transient power test.The on-line parameter calibration method thereby can ensure that the model possesses an acceptable robustness to varied battery loading profiles.
