Vehicle Dynamic State Estimation： State of the Art Schemes and Perspectives

Next-generation vehicle control and future autonomous driving require further advances in vehicle dynamic state estimation. This article provides a concise review, along with the perspectives, of the recent developments in the estimation of vehicle dynamic states. The definitions used in vehicle dynamic state estimation are first introduced, and alternative estimation structures are presented. Then, the sensor configuration schemes used to estimate vehicle velocity, sideslip angle, yaw rate and roll angle are presented. The vehicle models used for vehicle dynamic state estimation are further summarized, and representative estimation approaches are discussed. Future concerns and perspectives for vehicle dynamic state estimation are also discussed.

A Polynomial Prediction Filter Method for Estimating Multisensor Dynamically Varying Biases

The estimation of the sensor measurement biases in a multisensor system is vital for the sensor data fusion. A solution is provided for the estimation of dynamically varying multiple sensor biases without any knowledge of the dynamic bias model parameters. It is shown that the sensor bias pseudomeasurement can be dynamically obtained via a parity vector. This is accomplished by multiplying the sensor uncalibrated measurement equations by a projection matrix so that the measured variable is eliminated from the equations. Once the state equations of the dynamically varying sensor biases are modeled by a polynomial prediction filter, the dynamically varying multisensor biases can be obtained by Kalman filter. Simulation results validate that the proposed method can estimate the constant biases and dynamic biases of multisensors and outperforms the methods reported in literature.

Interaction of In-wheel Permanent Magnet Synchronous Motor with Tire Dynamics

Drive wheel systems combined with the in-wheel permanent magnet synchronous motor（I-PMSM） and the tire are highly electromechanical-coupled. However, the deformation dynamics of this system, which may influence the system performance, is neglected in most existing literatures. For this reason, a deformable tire and a detailed I-PMSM are modeled using Matlab/Simulink. Furthermore, the influence of tire/road contact interface is accurately described by the non-linear relaxation length-based model and magic formula pragmatic model. The drive wheel model used in this paper is closer to that of a real tire in contrast to the rigid tire model which is widely used. Based on the near-precise model mentioned above, the sensitivity of the dynamic tire and I-PMSM parameters to the relative error of slip ratio estimation is analyzed. Additionally, the torsional and longitudinal vibrations of the drive wheel are presented both in time and frequency domains when a quarter vehicle is started under conditions of a specific torque curve, which includes an abrupt torque change from 30 N·m to 200 N·m. The parameters sensitivity on drive wheel vibrations is also studied, and the parameters include the mass distribution ratio of tire, the tire torsional stiffness, the tire damping coefficient, and the hysteresis band of the PMSM current control algorithm. Finally, different target torque curves are compared in the simulation, which shows that the estimation error of the slip ratio gets violent, and the longitudinal force includes more fluctuation components with the increasing change rate of the torque. This paper analyzes the influence of the drive wheel deformation on the vehicle dynamic control, and provides useful information regarding the electric vehicle traction control.

Robust description and recognition of various viewpoint dynamic textures

The problem of recognizing natural scenes, such as water, smoke, fire, wind-blown vegetation and a flock of flying birds, is considered. These scenes exhibit the characteristic dynamic pattern, but have stochastic extent. They are referred to as dynamic texture（DT）. In reality, the diversity of DTs on different viewpoints and scales are very common, which also bring great difficulty to recognize DTs. In the previous studies, due to no considering of the deformable and transient nature of elements in DT, the motion estimation method is based on brightness constancy assumption,which seem inappropriate for aggregate and complex motions. A novel motion model based on relative motion in the neighborhood of two-dimensional motion fields is proposed. The estimation of non-rigid motion of DTs is based on the continuity equation, and then the local vector difference（LVD） is proposed to characterize DT local relative motion. Spatiotemporal statistics of the LVDs is used as the representation of DT sequences. Excellent performances of classifying all DTs in UCLA database demonstrate the capability of the proposed method in describing DT.

Time-domain Dynamic State Estimation for Unbalanced Three-phase Power Systems

In this paper,we present a time-domain dynamic state estimation for unbalanced three-phase power systems.The dynamic nature of the estimator stems from an explicit consideration of the electromagnetic dynamics of the network,i.e.,the dynamics of the electrical lines.This enables our approach to release the assumption of the network being in quasi-steady state.Initially,based on the line dynamics,we derive a graphbased dynamic system model.To handle the large number of interacting variables,we propose a port-Hamiltonian modeling approach.Based on the port-Hamiltonian model,we then follow an observer-based approach to develop a dynamic estimator.The estimator uses synchronized sampled value measurements to calculate asymptotic convergent estimates for the unknown bus voltages and currents.The design and implementation of the estimator are illustrated through the IEEE 33-bus system.Numerical simulations verify the estimator to produce asymptotic exact estimates,which are able to detect harmonic distortion and sub-second transients as arising from converterbased resources.

Dynamic State Estimation of Power Systems with Uncertainties Based on Robust Adaptive Unscented Kalman Filter

In this paper,a robust adaptive unscented Kalman filter(RAUKF)is developed to mitigate the unfavorable effects derived from uncertainties in noise and in the model.To address these issues,a robust M-estimator is first utilized to update the measurement noise covariance.Next,to deal with the effects of model parameter errors while considering the computational complexity and real-time requirements of dynamic state estimation,an adaptive update method is produced.The proposed method is integrated with spherical simplex unscented transformation technology,and then a novel derivative-free filter is proposed to dynamically track the states of the power system against uncertainties.Finally,the effectiveness and robustness of the proposed method are demonstrated through extensive simulation experiments on an IEEE 39-bus test system.Compared with other methods,the proposed method can capture the dynamic characteristics of a synchronous generator more reliably.

Dynamic State Estimation for DFIG with Unknown Inputs Based on Cubature Kalman Filter and Adaptive Interpolation

Dynamic state estimation(DSE)accurately tracks the dynamics of power systems and demonstrates the evolution of the system state in real time.This paper proposes a DSE approach for a doubly-fed induction generator(DFIG)with unknown inputs based on adaptive interpolation and cubature Kalman filter(AICKF-UI).DFIGs adopt different control strategies in normal and fault conditions;thus,the existing DSE approaches based on the conventional control model of DFIG are not applicable in all cases.Consequently,the DSE model of DFIGs is reformulated to consider the converter controller outputs as unknown inputs,which are estimated together with the DFIG dynamic states by an exponential smoothing model and augmented-state cubature Kalman filter.Furthermore,as the reporting rate of existing synchro-phasor data is not sufficiently high to capture the fast dynamics of DFIGs,a large estimation error may occur or the DSE approach may diverge.To this end,in this paper,a local-truncation-error-guided adaptive interpolation approach is developed.Extensive simulations conducted on a wind farm and the modified IEEE 39-bus test system show that the proposed AICKF-UI can(1)effectively address the divergence issues of existing cubature Kalman filters while being computationally more efficient;(2)accurately track the dynamic states and unknown inputs of the DFIG;and(3)deal with various types of system operating conditions such as time-varying wind and different system faults.

Does financial development moderate the link between technological innovation and environmental indicators?An advanced panel analysis

This study analyzes the role of financial development(FD)on the impact of technologi-cal innovation(TI)on six environmental quality indicators for the 25 economies that are part of the Organization for Economic Cooperation and Development for the period from 2000 to 2019.We use a two-step dynamic generalized method of moments approach to understand this relationship.The results show that FD augments the posi-tive effects of TI on four of the six environmental indicators,namely ecological foot-print,adjusted net savings,pressure on nature,and environmental performance.However,no significant effects on environmental sustainability and environmental vulnerability indices were found.When considering all of the environmental quality indicators,TI appears to enhance environmental quality.We find evidence to support the existence of the environmental Kuznets curve in the context of each environmen-tal indicator and economic growth.Moreover,FD and energy consumption appear to accelerate environmental degradation.Based on these results,FD should be viewed as an important parameter in designing policies for innovation to achieve the goal of net-zero carbon emissions.Highlights.Technological innovation and environmental quality nexus is studied.The moderating role of financial development is analyzed.Six different environmental quality indicators are used for OECD countries.Financial development intensifies the environmental benefits of innovation.•The EKC hypothesis is confirmed for all six environmental indicators.

Dynamic State Estimation for Integrated Electricity-gas Systems Based on Kalman Filter

In recent years, integrated electricity-gas systems(IEGSs) have attracted widespread attention. The unifiedscheduling and control of the IEGS depends on high-precisionoperating data. To this end, it is necessary to establish anappropriate state estimation (SE) model for IEGS to filter theraw measured data. Considering that power systems and naturalgas systems have different time scales and sampling periods, thispaper proposes a dynamic state estimation (DSE) method basedon a Kalman filter that can consider the dynamic characteristicsof natural gas pipelines. First, the standardized state transitionequations for the gas system are developed by applying the finitedifference method to the partial differential equations (PDEs) ofthe gas system;then the DSE model for IEGS is formulatedbased on a Kalman filter;also, the measurements from theelectricity system and the gas system with different samplingperiods are fused to ensure the observability of DSE by using theinterpolation method. The IEEE 39-bus electricity system and the18-nodes Belgium gas system are integrated as the test systems.Simulation results verify the proposed method’s accuracy andcalculation efficiency.

Measurement-based Frequency Dynamic Response Estimation Using Geometric Template Matching and Recurrent Artificial Neural Network

Understanding power system dynamics after an event occurs is essential for the purpose of online stability assessment and control applications.Wide area measurement systems(WAMS)based on synchrophasors make power system dynamics visible to system operators,delivering an accurate picture of overall operating conditions.However,in actual field implementations,some measurements can be inaccessible for various reasons,e.g.,most notably communication failure.To reconstruct these inaccessible measurements,in this paper,the radial basis function artificial neural network(RBF-ANN)is used to estimate the system dynamics.In order to find the best input features of the RBF-ANN model,geometric template matching(GeTeM)and quality-threshold(QT)clustering are employed from the time series analysis to compute the similarity of frequency dynamic responses in different locations of the power system.The proposed method is tested and verified on the Eastern Interconnection(EI)transmission system in the United States.The results obtained indicate that the proposed approach provides a compact and efficient RBF-ANN model that accurately estimates the inaccessible frequency dynamic responses under different operating conditions and with fewer inputs.

Dynamic Event-triggered Control and Estimation: A Survey

The efficient utilization of computation and communication resources became a critical design issue in a wide range of networked systems due to the finite computation and processing capabilities of system components(e.g., sensor, controller) and shared network bandwidth. Event-triggered mechanisms(ETMs) are regarded as a major paradigm shift in resource-constrained applications compared to the classical time-triggered mechanisms, which allows a trade-off to be achieved between desired control/estimation performance and improved resource efficiency. In recent years, dynamic event-triggered mechanisms(DETMs) are emerging as a promising enabler to fulfill more resource-efficient and flexible design requirements. This paper provides a comprehensive review of the latest developments in dynamic event-triggered control and estimation for networked systems. Firstly, a unified event-triggered control and estimation framework is established, which empowers several fundamental issues associated with the construction and implementation of the desired ETM and controller/estimator to be systematically investigated. Secondly, the motivations of DETMs and their main features and benefits are outlined. Then, two typical classes of DETMs based on auxiliary dynamic variables(ADVs) and dynamic threshold parameters(DTPs) are elaborated. In addition, the main techniques of constructing ADVs and DTPs are classified, and their corresponding analysis and design methods are discussed. Furthermore, three application examples are provided to evaluate different ETMs and verify how and under what conditions DETMs are superior to their static and periodic counterparts. Finally, several challenging issues are envisioned to direct the future research.

DISOPE distributed model predictive control of cascade systems with network communication

A novel distributed model predictive control scheme based on dynamic integrated system optimization and parameter estimation （DISOPE） was proposed for nonlinear cascade systems under network environment. Under the distributed control structure, online optimization of the cascade system was composed of several cascaded agents that can cooperate and exchange information via network communication. By iterating on modified distributed linear optimal control problems on the basis of estimating parameters at every iteration the correct optimal control action of the nonlinear model predictive control problem of the cascade system could be obtained, assuming that the algorithm was convergent. This approach avoids solving the complex nonlinear optimization problem and significantly reduces the computational burden. The simulation results of the fossil fuel power unit are illustrated to verify the effectiveness and practicability of the proposed algorithm.

Dynamic State Estimation of Medium-voltage DC Integrated Power System with Pulse Load

The dynamic characteristic evaluation is an important prerequisite for safe and reliable operation of the mediumvoltage DC integrated power system(MIPS),and the dynamic state estimation is an essential technical approach to the evaluation.Unlike the electromechanical transient process in a traditional power system,periodic change in pulse load of the MIPS is an electromagnetic transient process.As the system state suddenly changes in the range of a smaller time constant,it is difficult to estimate the dynamic state due to periodic disturbance.This paper presents a dynamic mathematical model of the MIPS according to the network structure and control strategy,thereby overcoming the restrictions of algebraic variables on the estimation and developing a dynamic state estimation method based on the extended Kalman filter.Using the method of adding fictitious process noise,it is possible to solve the problem that the linearized algorithm of the MIPS model is less reliable when an abrupt change occurs in the pulse load.Therefore,the accuracy of the dynamic state estimation and the stability of the filter can be improved under the periodic disturbance of pulse load.The simulation and experimental results confirm that the proposed model and method are feasible and effective.

Protection and control of microgrids using dynamic state estimation

High penetration of Converter Interfaced Generations(CIGs)presents challenges in both microgrid(μGrid)circuit and other system with CIG resources,such as wind farms and PV plants.Specifically,protection challenges are mainly brought by the insufficient separation between fault and load currents,especially forμGrids in islanded operation,and the short connection length inμGrids.In addition,CIG resources exhibit limited inertia and weak coupling to any rotating machinery,which can result in large transients during disturbances.To address the above challenges,this paper proposes a Dynamic State Estimation(DSE)based algorithm for protection and control of systems with substantial CIG resources such as aμGrid.It requires a high-fidelity dynamic model and time domain(sampled value)measurements.ForμGrid circuit protection,the algorithm dependably and securely detects internal faults by checking the consistency between the circuit model and available measurements.For CIG control,the algorithm estimates the frequency at other parts of aμGrid using CIG local information only and then utilizes it to provide supplementary feedback control.Simulation results prove that DSE based protection algorithm detects internal faults faster,ignores external faults and has improved sensitivity towards high impedance faults when compared to conventional protection methods.DSE based CIG control scheme also minimizes output oscillation and transient during system disturbances.

Efficient Estimation and Variable Selection in Dynamic Panel Data Partially Linear Varying Coefficient Models with Incidental Parameter

This paper is concerned with the statistical inference of partially linear varying coefficient dynamic panel data model with incidental parameter, including efficient estimation of the parametric and nonparametric components and consistent determination of the lagged order. For the parametric component, we propose an efficient semiparametric generalized method-of-moments（GMM） estimator and establish its asymptotic normality. For the nonparametric component, B-spline series approximation is employed to estimate the unknown coefficient functions, which are shown to achieve the optimal nonparametric convergence rate. A consistent estimator of the variance of error component is also constructed. In addition, by using the smooth-threshold GMM estimating equations, we propose a variable selection method to identify the significant order of lagged terms automatically and remove the irrelevant regressors by setting their coefficient to zeros. As a result, it can consistently determine the true lagged order and specify the significant exogenous variables. Further studies show that the resulting estimator has the same asymptotic properties as if the true lagged order and significant regressors were known prior, i.e., achieving the oracle property. Numerical experiments are conducted to evaluate the finite sample performance of our procedures. An example of application is also illustrated.

Partial Dynamic Dimension Reduction for Conditional Mean in Regression

In many regression analysis,the authors are interested in regression mean of response variate given predictors,not its the conditional distribution.This paper is concerned with dimension reduction of predictors in sense of mean function of response conditioning on predictors.The authors introduce the notion of partial dynamic central mean dimension reduction subspace,different from central mean dimension reduction subspace,it has varying subspace in the domain of predictors,and its structural dimensionality may not be the same point by point.The authors study the property of partial dynamic central mean dimension reduction subspace,and develop estimated methods called dynamic ordinary least squares and dynamic principal Hessian directions,which are extension of ordinary least squares and principal Hessian directions based on central mean dimension reduction subspace.The kernel estimate methods for dynamic ordinary least squares and dynamic Principal Hessian Directions are employed,and large sample properties of estimators are given under the regular conditions.Simulations and real data analysis demonstrate that they are effective.

Fuzzy Unknown Input Observer for Estimating Sensor and Actuator Cyber‑Attacks in Intelligent Connected Vehicles

The detection and mitigation of cyber-attacks in connected vehicle systems(CVSs)are critical for ensuring the security of intelligent connected vehicles.This paper presents a solution to estimate sensor and actuator cyber-attacks in CVSs.A novel method is proposed that utilizes an augmented system representation technique and a nonlinear unknown input observer(UIO)to achieve asymptotic estimation of both CVS dynamics and cyber-attacks.The nonlinear CVS dynamics is represented in a Takagi–Sugeno(TS)fuzzy form with nonlinear consequents,which allows for the effective use of the differential mean value theorem to handle unmeasured premise variables.Furthermore,via Lyapunov stability theory sufficient conditions are proposed,expressed in terms of linear matrix inequalities,to design TS fuzzy UIO.Several test scenarios are performed with high-fidelity Simulink-CarSim co-simulations to show the effectiveness of the proposed cyber-attack estimation method.

Resilient Smart Power Grid Synchronization Estimation Method for System Resilience with Partial Missing Measurements

With the increasing demand for power system stability and resilience,effective real-time tracking plays a crucial role in smart grid synchronization.However,most studies have focused on measurement noise,while they seldom think about the problem of measurement data loss in smart power grid synchronization.To solve this problem,a resilient fault-tolerant extended Kalman filter(RFTEKF)is proposed to track voltage amplitude,voltage phase angle and frequency dynamically.First,a threephase unbalanced network’s positive sequence fast estimation model is established.Then,the loss phenomenon of measurements occurs randomly,and the randomness of data loss’s randomness is defined by discrete interval distribution[0,1].Subsequently,a resilient fault-tolerant extended Kalman filter based on the real-time estimation framework is designed using the timestamp technique to acquire partial data loss information.Finally,extensive simulation results manifest the proposed RFTEKF can synchronize the smart grid more effectively than the traditional extended Kalman filter(EKF).

A measurement-based approach for power system instability early warning

Early warning of impending instability in a power system under disturbance conditions is important for preventing of system collapse.A measurement-based approach is proposed to assess the potential power system transient instability problem under cascading outages.Where a measurement-based index is obtained as the estimation accuracy of a linear autoregressive exogenous(ARX)model to estimate the dynamic response of the power system and indicate the system stability to some extent after a disturbance.The proposed approach was verified using a set of marginally stable cases in a 179-bus WECC equivalent power system.Then the instability early warning threshold for this system is obtained as 0.44.

Perioperative Evaluation of Cerebral Blood Flow Using ^123I-labeled N-isopropyl-p-iodoamphetamine Single-Photon Emission Computed Tomography without Blood Sampling in Patients Who Underwent Carotid Artery Stenting

The evaluation of regional cerebral vascular reserve （rCVR） with single-photon emission computed tomography （SPECT） is useful for predicting cerebral hyperperfusion following carotid artery stenting （CAS） and carotidendarterectomy （CEA）.