For the unforced dynamical non-linear state–space model,a new Q1 and efficient square root extended kernel recursive least square estimation algorithm is developed in this article.The proposed algorithm lends itself ...For the unforced dynamical non-linear state–space model,a new Q1 and efficient square root extended kernel recursive least square estimation algorithm is developed in this article.The proposed algorithm lends itself towards the parallel implementation as in the FPGA systems.With the help of an ortho-normal triangularization method,which relies on numerically stable givens rotation,matrix inversion causes a computational burden,is reduced.Matrix computation possesses many excellent numerical properties such as singularity,symmetry,skew symmetry,and triangularity is achieved by using this algorithm.The proposed method is validated for the prediction of stationary and non-stationary Mackey–Glass Time Series,along with that a component in the x-direction of the Lorenz Times Series is also predicted to illustrate its usefulness.By the learning curves regarding mean square error(MSE)are witnessed for demonstration with prediction performance of the proposed algorithm from where it’s concluded that the proposed algorithm performs better than EKRLS.This new SREKRLS based design positively offers an innovative era towards non-linear systolic arrays,which is efficient in developing very-large-scale integration(VLSI)applications with non-linear input data.Multiple experiments are carried out to validate the reliability,effectiveness,and applicability of the proposed algorithm and with different noise levels compared to the Extended kernel recursive least-squares(EKRLS)algorithm.展开更多
研究了带未知模型参数和衰减观测率多传感器线性离散随机系统的信息融合估计问题.在模型参数和衰减观测率未知的情形下,应用递推增广最小二乘(Recursive extend least squares,RELS)算法和加权融合估计算法提出了分布式融合未知模型参...研究了带未知模型参数和衰减观测率多传感器线性离散随机系统的信息融合估计问题.在模型参数和衰减观测率未知的情形下,应用递推增广最小二乘(Recursive extend least squares,RELS)算法和加权融合估计算法提出了分布式融合未知模型参数辨识器;应用相关函数对描述衰减观测现象的随机变量的数学期望和方差进行在线辨识.将辨识后的模型参数、数学期望和方差代入到最优分布式融合状态滤波器中,获得了相应的自校正融合状态滤波算法.应用动态误差系统分析(Dynamic error system analysis,DESA)方法证明了算法的收敛性.仿真例子验证了算法的有效性.展开更多
For safe and reliable operation of lithium-ion batteries in electric vehicles,the real-time monitoring of their internal states is important.The purpose of our study is to find an easily implementable,online identific...For safe and reliable operation of lithium-ion batteries in electric vehicles,the real-time monitoring of their internal states is important.The purpose of our study is to find an easily implementable,online identification method for lithium-ion batteries in electric vehicles.In this article,we propose an equivalent circuit model structure.Based on the model structure we derive the recursive mathematical description.The recursive extended least square algorithm is introduced to estimate the model parameters online.The accuracy and robustness are validated through experiments and simulations.Real-road driving cycle experiment shows that the proposed online identification method can achieve acceptable accuracy with the maximum error of less than 5.52%.In addition,it is proved that the proposed method can also be used to estimate the real-time SOH and SOC of the batteries.展开更多
基金funded by Prince Sultan University,Riyadh,Saudi Arabia。
文摘For the unforced dynamical non-linear state–space model,a new Q1 and efficient square root extended kernel recursive least square estimation algorithm is developed in this article.The proposed algorithm lends itself towards the parallel implementation as in the FPGA systems.With the help of an ortho-normal triangularization method,which relies on numerically stable givens rotation,matrix inversion causes a computational burden,is reduced.Matrix computation possesses many excellent numerical properties such as singularity,symmetry,skew symmetry,and triangularity is achieved by using this algorithm.The proposed method is validated for the prediction of stationary and non-stationary Mackey–Glass Time Series,along with that a component in the x-direction of the Lorenz Times Series is also predicted to illustrate its usefulness.By the learning curves regarding mean square error(MSE)are witnessed for demonstration with prediction performance of the proposed algorithm from where it’s concluded that the proposed algorithm performs better than EKRLS.This new SREKRLS based design positively offers an innovative era towards non-linear systolic arrays,which is efficient in developing very-large-scale integration(VLSI)applications with non-linear input data.Multiple experiments are carried out to validate the reliability,effectiveness,and applicability of the proposed algorithm and with different noise levels compared to the Extended kernel recursive least-squares(EKRLS)algorithm.
文摘研究了带未知模型参数和衰减观测率多传感器线性离散随机系统的信息融合估计问题.在模型参数和衰减观测率未知的情形下,应用递推增广最小二乘(Recursive extend least squares,RELS)算法和加权融合估计算法提出了分布式融合未知模型参数辨识器;应用相关函数对描述衰减观测现象的随机变量的数学期望和方差进行在线辨识.将辨识后的模型参数、数学期望和方差代入到最优分布式融合状态滤波器中,获得了相应的自校正融合状态滤波算法.应用动态误差系统分析(Dynamic error system analysis,DESA)方法证明了算法的收敛性.仿真例子验证了算法的有效性.
基金supported by the National High Technology Research and Development Program("863" Project)(Grant No.2011AA05A109)the International Science and Technology Cooperation Program of China(Grant Nos.2011DFA70570,2010DFA72760)the National Natural Science Foundation of China(Grant No.51007088)
文摘For safe and reliable operation of lithium-ion batteries in electric vehicles,the real-time monitoring of their internal states is important.The purpose of our study is to find an easily implementable,online identification method for lithium-ion batteries in electric vehicles.In this article,we propose an equivalent circuit model structure.Based on the model structure we derive the recursive mathematical description.The recursive extended least square algorithm is introduced to estimate the model parameters online.The accuracy and robustness are validated through experiments and simulations.Real-road driving cycle experiment shows that the proposed online identification method can achieve acceptable accuracy with the maximum error of less than 5.52%.In addition,it is proved that the proposed method can also be used to estimate the real-time SOH and SOC of the batteries.
