Dynamic Stability Analysis of Linear Time-varying Systems via an Extended Modal Identification Approach

The problem of linear time-varying（LTV） system modal analysis is considered based on time-dependent state space representations, as classical modal analysis of linear time-invariant systems and current LTV system modal analysis under the ＂frozen-time＂ assumption are not able to determine the dynamic stability of LTV systems. Time-dependent state space representations of LTV systems are first introduced, and the corresponding modal analysis theories are subsequently presented via a stabilitypreserving state transformation. The time-varying modes of LTV systems are extended in terms of uniqueness, and are further interpreted to determine the system＇s stability. An extended modal identification is proposed to estimate the time-varying modes, consisting of the estimation of the state transition matrix via a subspace-based method and the extraction of the time-varying modes by the QR decomposition. The proposed approach is numerically validated by three numerical cases, and is experimentally validated by a coupled moving-mass simply supported beam exper- imental case. The proposed approach is capable of accurately estimating the time-varying modes, and provides anew way to determine the dynamic stability of LTV systems by using the estimated time-varying modes.

An Innovative State-of-charge Estimation Method of Lithium-ion Battery Based on 5th-order Cubature Kalman Filter

The lithium-ion batteries have drawn much attention as the major energy storage system.However,the battery state estimation still suffers from inaccuracy under dynamic operational conditions,with the unstable environmental noise influencing the robustness of estimation.This paper presents a 5th-order cubature Kalman filter with improvements on adaptivity for real-time state-of-charge estimation.The second-order equivalent circuit model is developed for describing the characteristics of battery,and parameter identification is carried out according to particle swarm optimization.The developed method is validated in stable and dynamic conditions,and simulation results show a satisfactory consistency with the experimental results.The maximum estimation error under static conditions is less than 3%and the maximum error under dynamic conditions is 5%.Numerical analysis indicates that the method has better convergence and robustness than the traditional method under the disturbances of initial error,which demonstrates the potential for EV applications in harsh environments.The proposed method shows application potential for both online estimations and cloud-computing system,indicating its diverse application prospect in electric vehicles.