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There is an opportunity in modern power systems to explore the demand flexibility by incentivizing consumers with dynamic prices. In this paper, we quantify demand flexibility using an efficient tool called time-varying elasticity, whose value may change depending on the prices and decision dynamics. This tool is particularly useful for evaluating the demand response potential and system reliability. Recent empirical evidences have highlighted some abnormal features when studying demand flexibility, such as delayed responses and vanishing elasticities after price spikes. Existing methods fail to capture these complicated features because they heavily rely on some predefined (often over-simplified) regression expressions. Instead, this paper proposes a model-free methodology to automatically and accurately derive the optimal estimation pattern. We further develop a two-stage estimation process with Siamese long short-term memory (LSTM) networks. Here, a LSTM network encodes the price response, while the other network estimates the time-varying elasticities. In the case study, the proposed framework and models are validated to achieve higher overall estimation accuracy and better description for various abnormal features when compared with the state-of-the-art methods. [ABSTRACT FROM AUTHOR]

A large number of non-linear hardware and control units exists in power electronic system used in grid connected devices. The analytical transient stability analysis of grid-connected converters presents numerous difficulties. A common method to tackle this problem is the stability analysis using Lyapunov’s method. By applying this method, difficulties arise not only from finding a suitable Lyapunov function, but also from checking the constraint of Lyapunov stability. If the appropriate Lyapunov function is a high-order polynomial, it is very challenging to test if it meets the constraints of Lyapunov stability in certain regions. In this paper, the sum-of-squares programming method is used to obtain the estimation of a converter’s domain of attraction with a relatively small number of iterations compared to classically applied methods, such as the Monte Carlo method. The estimation of the domain of attraction are verified by time-domain simulations and StarSim’s controller hardware-in-the-loop tests in this paper. [ABSTRACT FROM AUTHOR]

This paper proposed a method for estimating the suspension relative velocity using a single six-dimensional measurement unit attached to a vehicle body. This method can reduce the number of sensors, reduce costs, and address sensor packaging issues. A wheelbase preview assumption is used to overcome the lack of unsprung mass information. The proposed estimation method is verified by the vehicle simulator Carsim and the stability of the estimation method is also analyzed. In addition, the frequency response and the time response of the sky-hook controller based on the estimated relative velocity are compared with the sky-hook controller based on the actual relative velocity. The results show that the proposed estimator produces very robust performance on various road conditions. [ABSTRACT FROM AUTHOR]

Off-policy reinforcement learning (RL) holds the promise of better data efficiency as it allows sample reuse and potentially enables safe interaction with the environment. Current off-policy policy gradient methods either suffer from high bias or high variance, delivering often unreliable estimates. The price of inefficiency becomes evident in real-world scenarios such as interaction-driven robot learning, where the success of RL has been rather limited, and a very high sample cost hinders straightforward application. In this paper, we propose a nonparametric Bellman equation, which can be solved in closed form. The solution is differentiable w.r.t the policy parameters and gives access to an estimation of the policy gradient. In this way, we avoid the high variance of importance sampling approaches, and the high bias of semi-gradient methods. We empirically analyze the quality of our gradient estimate against state-of-the-art methods, and show that it outperforms the baselines in terms of sample efficiency on classical control tasks. [ABSTRACT FROM AUTHOR]

Long horizon state of health (SOH) monitoring and remaining useful life (RUL) prediction are of industrial value in prognostic and health management (PHM) of lithium-ion batteries (LIBs) to ensure their reliable functionality by early detection. Machine Learning, as a data-driven health diagnostic technique, has been widely utilized in solitary and hybrid structures. However, an accurate SOH estimation and RUL prediction method with less computational burden are highly desirable for the online state prediction in an electric vehicle application. This paper evaluates nonlinear autoregressive with external input (NARX) recurrent neural network (RNN) and time delay neural network (TDNN) in their prediction precision using the NASA dataset. The superior method, NARXRNN, is employed for two different datasets to estimate the battery's SOH and predict its RUL on a broad horizon. The results reveal the outstanding performance by presenting the root mean square error within 3% and mean absolute error within 2% for unseen data. Therefore, this method is capable to accurately predict the SOH of LIBS from historical data at low computational complexity. It is a promising model for long horizon SOH and RUL prediction and practical for online applications. [ABSTRACT FROM AUTHOR]

This paper studies the estimation problem for a class of nonlinear tunnel diode circuits with parameter perturbation. The dynamics of the nonlinear circuit is approximated by the Takagi–Sugeno fuzzy model with linear fractional parametric uncertainties. Considering the fuzzy filter with gain uncertainties, a filtering error system can be gotten naturally. The concentration of this paper lies in deriving the design conditions for the desired resilient filter such that the filtering error system preserves asymptotic stability as well as the $\boldsymbol {\mathcal {L}}_{\boldsymbol {\infty }}$ -norm of the transfer function from the external disturbance input to the filtering error output is below a specified bound. The filter design conditions for guaranteeing the prescribed peak-to-peak performance of the filtering error system are provided by a set of linear matrix inequalities. Finally, a simulation result is presented to illustrate the validity and effectiveness of the proposed filtering design for the parameter-controlled tunnel diode circuit. [ABSTRACT FROM AUTHOR]

Resting-state functional magnetic resonance imaging (rs-fMRI) has become a popular non-invasive way of diagnosing neurological disorders or their early stages by probing functional connectivity between different brain regions of interest (ROIs) across subjects. In the past decades, researchers have proposed many methods to estimate brain functional networks (BFNs) based on blood-oxygen-level-dependent (BOLD) signals captured by rs-fMRI. However, most of the existing methods estimate BFNs under the assumption that signals are independently sampled, which ignores the temporal dependency and sequential order of different time points (or volumes). To address this problem, in this paper, we first propose a novel BFN estimation model by introducing a latent variable to control the sequence of volumes for encoding the temporal dependency and sequential information of signals into the estimated BFNs. Then, we develop an efficient learning algorithm to solve the proposed model by the alternating optimization scheme. To verify the effectiveness of the proposed method, the estimated BFNs are used to identify subjects with mild cognitive impairment (MCIs) from normal controls (NCs). Experimental results show that our method outperforms the baseline methods in the terms of classification performance. [ABSTRACT FROM AUTHOR]

In past years, LLC resonant converters have become a mainstream topology for dc–dc power conversion due to their advantages, such as the superior efficiency obtained with the soft switching of MOSFETs. In order to further improve the efficiency of the converter, synchronous rectification (SR) can be implemented as an alternative for diode rectification. As a result, the vast majority of the literature related to this field of study presents different LLC SR control algorithms, which aim to improve the operation of the rectification. Unlike prior work on SR controllers, this paper contributes to the area of power loss estimation using rectifier current equations (RCE). The developed method based on time-domain analysis of SR currents provides a new analytical framework to characterize the behavior of SR. Implications in SR power losses of different time delays are investigated using the developed loss estimation method. In addition, different converter design parameters, such as choice of inductance ratio, can be analyzed. The RCE captures the true discontinuous and complex behavior of SR, which is often oversimplified by the conventional first-harmonic approximation (FHA). As a result, the proposed method facilitates the design of LLC resonant converters and provides increased precision in SR power loss estimation when compared to FHA, and in a considerably faster fashion when compared with precise yet computationally intensive simulation software. This paper is validated with simulation and experimental results. [ABSTRACT FROM AUTHOR]

This paper proposes a generator polynomial model-based eye diagram estimation method for the error-correction codes (ECCs), which are the Bose–Chaudhuri–Hocquenghem (BCH) code and the Reed–Solomon (RS) code. The ECCs compensate for the channel loss at the bit level, so most of the systems include the ECC block. The BCH and RS codes are the generator polynomial-based ECCs; this paper proposes the generator polynomial model-based method. The model provides the accurate probability density function of the code words for the BCH and RS code. Thus, the proposed method is capable of achieving the signal integrity analysis for the system level. For verification, we design the BCH and RS encoders. The estimated eye diagrams are nearly the same as that of the transient simulation for the BCH and RS codes. Furthermore, the voltage bathtub curves are the same up to 10−8. For a smaller bit-error rate value, the proposed method only provides the bathtub curve. This paper successfully verifies the eye diagram estimation method for the BCH and RS codes. [ABSTRACT FROM AUTHOR]

Reliability of any electrical system heavily depends on the accuracy of the model used for representing the integrated device components. The design of renewable power sources and energy management systems using ultracapacitor demands adoption of precise mathematical representation of ultracapacitor dynamics. An appropriate model of ultracapacitor not only allows an accurate assessment of its charge/discharge characteristics but also provides a precise estimation of energy storage and power delivery capabilities. However, due to the lack in the usability of data/information provided by the manufacturer, conventional calculus-based ultracapacitor modeling and energy estimation approach often leads to its inaccurate and inefficient utility. In this regard, this paper has focused on fractional calculus-based analytical formulation for power, energy, and efficiency measurement of ultracapacitor and has deployed an upgraded model of the ultracapacitor, which in turn provides a better estimation of its energy efficiency. The work presented in this paper has been supported by extensive experimentation with the ultracapacitor under different input excitations, considering the dependence of energy storage (state of energy) and power delivery capacity of ultracapacitor on the charge/discharge profile. Furthermore, the hybrid optimization-based analysis recommends the requirement and challenges for the design and development of optimal charging/discharging of ultracapacitors for achieving higher energy efficiency. [ABSTRACT FROM AUTHOR]

This paper proposes eye diagram estimation methods for linear feedback shift register (LFSR)-based bit streams in pseudorandom binary sequence (PRBS) test and scrambling. The PRBS test uses the LFSR as a random data source; scrambling suppresses the radiated EMI by exclusive-OR (XOR) between the data and the LFSR. Both cases include the LFSR as the last block, thus, the LFSR dominantly determines the eye diagram. This paper introduced the deterministic and statistical eye diagram for the PRBS test and scrambling, respectively. The deterministic eye diagram was verified by comparing to the measurement; the statistical eye diagram was verified by comparing to the transient simulation. We also compared the voltage bathtub curves for the scrambling. The measurement and proposed method were correlated up to the BER of 10−10. [ABSTRACT FROM AUTHOR]

Time-of-flight (TOF) measures are valuable in the estimation of displacements, distances, and moving fluids’ speed, e.g., the wind. Of the different types of methods that measure wind speed, the ultrasound is highlighted. As part of the calculation required to acquire the wind speed, one must find the time that an ultrasonic wave takes to travel from one particular place to another. This time is known as TOF. The biggest challenge in measuring TOF is to determine the correct moment when the ultrasonic wave arrived at the reception because it will be immersed in noise and its envelope probably will be corrupted by the actions of the wind. The methodology proposed by this paper presents a combined algorithm between an extended Kalman Filter (EKF) and a zero-crossing detector (ZCD) technique. The EKF method is responsible for the noise elimination stage and estimation of the real ultrasound’s envelope, while the ZCD detects the correct TOF of the estimated signal. This paper also presents the flaws and solutions found for each technique. It was shown that for speeds up to 75 km/h, the coefficient that was found to be $R^{2}\,\,=0.99977$. [ABSTRACT FROM AUTHOR]

This paper presents a modified version of the sine wave histogram test for analog-to-digital converter (ADC) characterization. To accurately estimate the transition levels, the input signal has to fulfill strict conditions, which are coherent sampling and uniform distribution of the phases. Noncoherent sampling of the sine wave leads to systematic errors in the transition level estimation, while the nonuniform distribution of the phases increases the variance of the results using the original sine wave histogram test method. The source of the errors is the improper modeling of the histogram of the measured signal. This paper shows that the true histogram of the measured sine wave can be reconstructed by estimating the phases of the sampled signal. It is shown that integrating this information into the estimation of the transition levels can significantly decrease the effect of noncoherent sampling, thus the ADC can be characterized accurately even if the conditions are not fulfilled. The proposed method is verified on both simulated and real measurement data. [ABSTRACT FROM AUTHOR]

This paper describes a method to provide low-voltage four-wire three-phase power converters with the capability of unbalance estimation as an ancillary service to the main role that they play in the distribution system (distributed generator, energy storage system, drive, etc.). Typically, dedicated grid/load current sensors are needed to effectively comply with unbalance compensation tasks, increasing system cost and reducing reliability. This is due to the difficulties that arise in the extraction of the zero- and negative-voltage components from the voltages at the point of common coupling, such as the inadequate resolution of full-scaled voltage sensors and limited spectral separation. In this paper, the proposed method does not rely on additional sensors to those typically used in voltage-source converters, and in any case, those sensors are limited to the point of connection of the power converter. Impedance estimation only using converter-side current sensors is implemented by adding a high-frequency voltage excitation over the fundamental command. A new model approach is proposed for the real-time extraction of system impedance using a complex-valued compact form. Considering the voltage source at that frequency to be unique in the grid, it will be proved that the impedance and, thus, the resulting negative-sequence current, which is used for unbalance compensation, can be estimated. For the zero sequence, a special arrangement of the converter voltage sensors together with a repetitive controller is used. [ABSTRACT FROM AUTHOR]

Three-phase grid-connected inverter modeling depends on the equivalent resistance and inductance between the inverter and the grid. However, these parameters are not fixed during the operation of the inverter and vary with the operating conditions. In this paper, a new globally stable adaptive controller is proposed to estimate these values online and update the controller during its operation. A model reference adaptive system (MRAS) based on two fictitious quantities with no physical significance is utilized, namely $M = v \cdot i^*$ and $N = v^* \times i$ , where $v$ and $i$ are the steady-state values of voltage and current in synchronously rotating frame of reference. Detailed stability analysis of the proposed estimation technique has been presented using a Lyapunov energy function-based approach proving the global stability of the technique. Small-signal modeling and analysis of the proposed scheme has also been presented so as to understand the local dynamics of the system. The guidelines to select the parameter of the proposed MRAS system along with updating the controller are elaborated in this paper. Detailed simulation based on MATLAB/Simulink and PLECS, along with experimental studies through a Texas Instruments TMS320F28377S microcontroller, validates the feasibility of the proposed control architecture. [ABSTRACT FROM AUTHOR]

Nowadays, the availability of accurate vehicle position becomes more and more indispensable. The GNSS/INS (Global Navigation Satellite Systems/Inertial Navigation System) is currently the most widely-used integrated navigation scheme for land vehicles, which is capable of provide high-accuracy and continuous positioning results in the open-sky environments. However, under the GNSS-denied conditions, the existing GNSS/INS integrated system often fails to provide reliable positioning results due to various and nonlinear errors contained in the MEMS (Micro-Electro-Mechanical System) IMU (Inertial Measurement Unit) measurements. To improve the positioning accuracy during GNSS outage, deep learning has been introduced into the GNSS/INS integrated system in recent years. In this paper, we propose a residual attention network-based confidence (i.e., measurement noise covariance) estimation algorithm for non-holonomic constraint in GNSS/INS integrated navigation system, which adopts a residual attention network to dynamically estimate the noise covariance of the pseudo-observation (i.e., non-holonomic constraint) for optimal Kalman filtering (KF) fusion. To emphasize the more representative features with larger weights for accurate noise covariance estimation, we introduce an attention mechanism to automatically assign proper weights to the learned features according to their contributions. We evaluate our proposed method on three practical road datasets and compare it with other seven methods including the traditional KF, Pure INS, KF with three deep learning networks, K-means, and the Input-Delayed Neural Networks based method. Extensive experimental results demonstrate that our proposed RA-NHC bounds the errors associated with velocities and achieves reasonable accuracy improvement in position and velocity estimation. [ABSTRACT FROM AUTHOR]

The conventional Under Frequency Load Shedding (UFLS) scheme could result in unacceptably low frequency nadirs or overshedding in power systems with volatile inertia. This paper proposes a novel UFLS scheme for modern power systems whose inertia may vary in a wide range due to high penetration of renewable energy sources (RESs). The proposed scheme estimates the rate of change of frequency (RoCoF) of the center of inertia (CoI), and consequently, the loss of generation (LoG) size, using local frequency measurements only. An innovative inflection point detector technique is presented to remove the effect of local frequency oscillations. This enables fast and accurate LoG size calculation, thereby more effective load shedding. The proposed UFLS scheme also accounts for the effect of the inertia change resulting from LoG events. The performance of the proposed scheme is validated by conducting extensive dynamic simulations on the IEEE 39-bus test system using Real Time Digital Simulator (RTDS). Simulation results confirm that the proposed UFLS scheme outperforms the conventional UFLS scheme in terms of both arresting frequency deviations and the amount of load shed. [ABSTRACT FROM AUTHOR]

In heavy-duty vehicles, multiple signals are available to estimate the vehicle's kinematics, such as Inertial Measurement Unit (IMU), Global Positioning System (GPS) and linear and angular speed readings from wheel tachometers on the internal Controller Area Network (CAN). These signals have different noise variance, bandwidth and sampling rate (being the latter, possibly, irregular). In this paper we present a non-linear sensor fusion algorithm allowing asynchronous sampling and non-causal smoothing. It is applied to achieve accuracy improvements when incorporating odometry measurements from CAN bus to standard GPS+IMU kinematic estimation, as well as the robustness against missing data. Our results show that this asynchronous multi-sensor (GPS+IMU+CAN-based odometry) fusion is advantageous in low-speed manoeuvres, improving accuracy and robustness to missing data, thanks to non-causal filtering. The proposed algorithm is based on Extended Kalman Filter and Smoother, with exponential discretization of continuous-time stochastic differential equations, in order to process measurements at arbitrary time instants; it can provide data to subsequent processing steps at arbitrary time instants, not necessarily coincident with the original measurement ones. Given the extra information available in the smoothing case, its estimation performance is less sensitive to the noise-variance parameter setting, compared to causal filtering. Working Matlab code is provided at the end of this work. [ABSTRACT FROM AUTHOR]

Accurate and reliable capacity estimation for lithium-ion batteries adapting to uncertain environment plays a significant role in the fields of electric vehicles and renewable energy systems. This paper proposes a novel capacity fusion estimation approach based on three-parameter capacity fade model and constant current charging curves using dual unscented Kalman filters. In view of the battery cycle aging problem under variable temperature situation, a three-parameter capacity fade model is introduced to continuously estimate the battery cell capacity for unknown changes of ambient temperature. Nevertheless, the model parameters may mismatch in practical applications, so the first unscented Kalman filter views the discrete capacity based on constant current charging curves as observations to periodically update the parameters. Thereafter, the second one performs a fusion operation depending on the capacity estimated by constant current charging curves and the three-parameter capacity fade model with the corrected parameters, so as to improve the capacity estimation accuracy online. The results show that the fusion estimation errors of capacity are controlled within 2% by updating the model parameters, and the robustness of the proposed method is also validated. [ABSTRACT FROM AUTHOR]

Timely identification of the size and location of loss of generation (LoG) events improves the effectiveness of remedial actions taken against this type of disturbances. This paper sets forth a novel method for LoG localization and size estimation by phasor measurements received in the control center within a reasonable wait time following the event inception. The bus impedance matrix is utilized to obtain a transfer function from the LoG location to variations of voltage and current phasors following the event. This results in an overdetermined system of linear equations whose closed-form solution provides the LoG location and size based upon the sum of squared residuals concept. The proposed method removes the need for the reception of specific measurements in the control center, and/or knowing the system inertia. This is in contrast with previous methods resorting to the swing equation of the center of inertia. The method lends itself to real-time applications for its robustness against partial communication network failures and losses of the time synchronization signal. Extensive simulations conducted on the IEEE 39-bus and 118-bus test systems verify the effectiveness and superiority of the proposed method over existing ones. [ABSTRACT FROM AUTHOR]

To improve the accuracy of faulty feeder selection based on zero sequence current mutation, distribution networks with inverter-interfaced distributed generators (IIDGs) are analyzed in detail. It is found that the IIDGs cannot be completely equivalent to a positive sequence current source due to their boosting increments. Thereby, a more accurate IIDG current estimation model based on the Gauss-Seidel method that considers the control strategy at the moment of fault is established. Moreover, a real-time estimation algorithm is introduced for faulty feeder detection to adjust the compensation degree of the Petersen coil. On this basis, this paper proposes a novel faulty feeder detection approach using the zero-sequence current space relative distance between the increments of each feeder and an estimation of the faulty path generated by Kalman filtering (KAF). The feasibility of the proposed approach is demonstrated in both a PSCAD/EMTDC simulation and a MATLAB experimental laboratory. The test results indicate that the proposed approach can achieve a high speed when employed for different fault conditions. [ABSTRACT FROM AUTHOR]

Dynamic battery modeling uncertainties, even if low, may lead to significant performance degradation or even divergence of the state-of-charge estimation algorithm. This paper investigates the integration of the extended-Kalman-filter with the smooth-variable-structure-filter algorithms for state-of-charge estimation of lithium-ion batteries. The robustness of the presented approach to modeling uncertainty is assessed for batteries operating in highly dynamic environments. The presented approach combines the benefit of the smooth- variable-structure-filter in its robustness to model uncertainty with the benefit of the extended-Kalman-filter in its near-optimality for a given dynamics and measurement noise sequences. The algorithm is rigorously tested using various datasets including standardrized and artificial drive cycles with added dynamics. The drive cycle power profile is calculated for an electric Ford F150 truck and scaled for the 18650PF cell used in the tests. Experimental validation is performed by investigating four different scenarios in which knowledge of the initial conditions as well as accuracy of the battery model were varied. The results demonstrate a substantially enhanced estimation accuracy achieved by the adopted approach through its optimality to measurement and model noise as well as its robustness to model uncertainty. The adopted approach results in a reduction in the complexity of the state-of-charge control algorithm and therefore enhances the battery management system. [ABSTRACT FROM AUTHOR]

This paper proposes a regularized blind deconvolution method for restoring Poissonian blurred image. The problem is formulated by utilizing the ${L}_{0}$ -norm of image gradients and total variation (TV) to regularize the latent image and point spread function (PSF), respectively, and combining them with the negative logarithmic Poisson log-likelihood. To solve the problem, we propose an approach which combines the methods of variable splitting and Lagrange multiplier to convert the original problem into three sub-problems, and then design an alternating minimization algorithm which incorporates the estimation of PSF and latent image as well as the updation of Lagrange multiplier into account. We also design a non-blind deconvolution method based on TV regularization to further improve the quality of the restored image. Experimental results on both synthetic and real-world Poissonian blurred images show that the proposed method can achieve restored images of very high quality, which is competitive with or even better than some state of the art methods. [ABSTRACT FROM AUTHOR]

Energy storage systems (ESSs) can improve energy management in distribution grids, especially with the increasing penetration of home energy management systems (HEMSs) that schedule household appliances and render them as smart loads. A large number of uncoordinated HEMSs can result in significant changes to the aggregated load profile of the distribution system. This paper proposes a framework and mathematical model for integrating ESS in the distribution grid to minimize the operation cost of the local distribution company (LDC) and alleviate the impact of uncoordinated HEMS operation on the distribution grid. A novel neural network (NN) based state of health (SoH) estimator for a lithium-ion (Li-ion) battery based ESS is proposed, which is incorporated within the LDC's planning problem. The results show that the proposed estimation model is an accurate estimation of the SoH of the ESS. The LDC's planning decisions are also compared, considering SoH of the ESS vis-á-vis linear degradation and no-degradation models. [ABSTRACT FROM AUTHOR]

System identification is fundamental in many recent state-of-the-art developments in power electronic such as modeling, parameter tracking, estimation, self-tuning and adaptive control, health monitoring, and fault detection. Therefore, this paper presents a comprehensive review of parametric, non-parametric, and dual hybrid system identification for dc–dc switch mode power converter (SMPC) applications. The paper outlines the key challenges inherent with system identification for power electronic applications; speed of estimation, computational complexity, estimation accuracy, tracking capability, and robustness to disturbances and time-varying systems. Based on the literature in the field, modern solutions to these challenges are discussed in detail. Furthermore, this paper reviews and discusses the various applications of system identification for SMPCs; including health monitoring and fault detection. [ABSTRACT FROM AUTHOR]

Following an unintended disconnection of a synchronous generator from the power system, what is also known as a loss of generation (LoG), it is not trivial to precisely estimate the post-event power system's inertia and the LoG size. One of the reasons for that is that both of them are a function of the unknown inertia reduction. To solve this challenging problem, this paper presents an analytical method based on the rate-of-change-of-frequency (RoCoF). The method relies on a modified swing equation, allowing a simultaneous estimation of both unknowns. To this end, the values of mechanical starting time, apparent power, and loading of lost generator are formulated for the power system under study. In a practical application, the method can use RoCoF measured by phasor measurement units. The paper discusses the impact of various frequency estimation approaches to the proposed LoG estimation. Furthermore, a new method for LoG size estimation, based on the interpolated estimated inertial response, is proposed. The efficiency of the proposed approach is validated through extensive simulations with Matlab/Simulink using a simple power system and the IEEE 39-bus test network. [ABSTRACT FROM AUTHOR]

This paper investigates the role of artificial neural networks in enhancing the accuracy of instantaneous power estimation of electric vehicles’ batteries. In electric vehicles, a battery is used as a main or complementary bidirectional power source. To optimize the energy management of the vehicle, the power sourced or sinked by the battery must be estimated in real time under any condition. The power of the battery is a function of many variables including the current, the state of charge, the ambient temperature, and the state of health. This paper evaluates some existing equivalent circuit models for estimating the instantaneous power of electric vehicles' batteries and proposes new artificial neural network-based models to enhance the power estimation accuracy. The experimental data obtained by performing standardized electric vehicle tests using a 3.6-V/16.5-Ah lithium-ion battery cell and a 12.8-V/150-Ah lithium-ion battery pack are presented and used for models' evaluation. [ABSTRACT FROM AUTHOR]

This paper has a twofold goal. The first aim is to provide a deeper understanding of the family of the real elliptically symmetric (RES) distributions by investigating their intrinsic semiparametric nature. The second aim is to derive a semiparametric lower bound for the estimation of the parametric component of the model. The RES distributions represent a semiparametric model, where the parametric part is given by the mean vector and by the scatter matrix, while the non-parametric, infinite-dimensional, part is represented by the density generator. Since, in practical applications, we are often interested only in the estimation of the parametric component, the density generator can be considered as nuisance. The first part of this paper is dedicated to conveniently place the RES distributions in the framework of the semiparametric group models. In the second part, building on the mathematical tools previously introduced, the constrained semiparametric Cramér–Rao bound (CSCRB) for the estimation of the mean vector and of the constrained scatter matrix of a RES distributed random vector is introduced. The CSCRB provides a lower bound on the mean squared error of any robust $M$ -estimator of mean vector and scatter matrix when no a priori information on the density generator is available. A closed-form expression for the CSCRB is derived. Finally, in simulations, we assess the statistical efficiency of the Tyler's and Huber's scatter matrix $M$ -estimators with respect to the CSCRB. [ABSTRACT FROM AUTHOR]

The accurate estimation of within-footprint slope is very important for measuring earth’s surface characteristics using satellite light detection and ranging (LiDAR) data. Several models have previously been proposed for slope estimation; however, these models have limitations in either accuracy or applicability. Therefore, the main purpose of this paper is to explore the influence of various factors (e.g., ground vertical extent, footprint size, footprint shape, and footprint orientation) on slope estimation to better estimate the within-footprint slope using large-footprint waveform LiDAR data. The results indicated that the absolute slope error due to the coupling effect of ground vertical extent and footprint size increased with an increase in the ratio of ground vertical extent and footprint size, while the relative slope error had an opposite trend. The slope error caused by footprint shape was relatively low when the footprint eccentricity was small. However, the slope error due to footprint shape grew rapidly when the footprint eccentricity became larger; thus, it is essential to fully take into account the influence of footprint shape on within-footprint slope estimation. In addition, the results suggest that the slope error changed regularly based on the intersection angle between footprint orientation and terrain aspect. This paper also provided guidance for the determination of an easy and practical model for within-footprint slope estimation. The determination of best model is dependent on the value of intersection angle. Once the intersection value is given, the best model can easily be determined. Using the best model, the within-footprint terrain slope can be estimated with high accuracy. [ABSTRACT FROM AUTHOR]

This paper presents nonlinear optimization-based position and speed estimation scheme for IPMSM drives with arbitrary signal injection generated by inherent switching ripples associated with finite control set model predictive control (FCSMPC). The existing standard sensorless techniques are not suitable for FCSMPC which applies voltage vectors directly to an electrical machine without a modulator. The proposed method optimizes the nonlinear cost function derived from the standard IPMSM model with respect to position and speed at every sampling interval. This method can be applied to any type of signal injection and, hence, an ideal candidate for sensorless FCSMPC. In this method, the signal injection is needed only to generate persistent excitation to maintain the observability at low speeds. A strong persistent excitation is always present with FCSMPC except at standstill where the control applies null vector when the reference currents are zero. This situation is overcome in this paper by introducing a small negative $d$ -axis current at standstill. Thus, the proposed method can estimate the position and speed over a wide speed range starting from standstill to the rated speed without a changeover or additional signal injection. This paper also presents detailed convergence analysis and proposes a compensator for standstill operation that prevents converging to saddle and symmetrical solutions, and therefore, also eliminates the well-known ambiguity of $\pi$ rad in position estimation. The performance of the proposed sensorless scheme is experimentally verified for a wide range of operating conditions. [ABSTRACT FROM AUTHOR]

Similar to the conventional squint mode synthetic aperture radar (SAR) imaging processing, the estimation of Doppler centroid is one key problem for the low-squint-mode (within the range of [−5°, 5°]) multichannel in an azimuth high-resolution and wide-swath (HRWS) SAR system. In this paper, based on the electromagnetic scattering model, a novel weighted Doppler centroid estimation approach is proposed for the multichannel in the azimuth HRWS (MC-HRWS) SAR system. First, Maxwell’s equations are employed to derive the echo model for the multichannel SAR system. Then, an improved backscattering model is presented based on the small perturbation method and the available echo model, which is adopted to produce the weights for Doppler centroid estimation. More importantly, in order to improve the precision of Doppler centroid estimation, a weighted ambiguity-free Doppler centroid estimation approach is proposed, where the range-variant characteristic of Doppler centroid is employed to resolve the ambiguity number of Doppler centroid, and the echoes from different range bins with different signal-to-noise ratios are considered. In addition, the Cramer–Rao low bound of the estimated Doppler centroid is also discussed in this paper. The effectiveness of the proposed Doppler centroid estimation approach is verified via simulated and real measured low-squint-mode MC-HRWS SAR data. [ABSTRACT FROM AUTHOR]

In recent times, lithium iron phosphate (LFP) batteries have found wide usage in electric vehicles (EVs). Efficient utilization of the storage capacity of any battery used in EVs is heavily dependent on the accuracy of state of charge (SOC) estimation. The classical SOC-OCV (open circuit voltage) based approach for estimation of SOC is quite ineffective under rapid variation in operating condition of EVs. The impact of parameter variations and unknown disturbances due to widely varying environmental conditions and drive terrain, demands a robust observer for SOC estimation. In this regard, this paper proposes a novel SOC estimation technique using a nonlinear fractional-order unknown input observer (FOUIO). The scheme based on the combined framework of fractional calculus and state estimation with unknown input is able to meet the challenges of SOC estimation in EVs by decoupling the plant disturbance inputs from the state estimation process. The optimally designed FOUIO using a linear matrix inequality (LMI) based formulation has been analytically proved for convergence. The proposed FOUIO has been validated and compared with reported SOC estimation schemes for different drive profiles in terms of accuracy and convergence speed. The numerical simulations and hardware in loop (HIL) based experimental results confirm the effectiveness of the proposed FOUIO in estimating the SOC robustly under different scenarios of battery operation. [ABSTRACT FROM AUTHOR]

In this paper we propose a one-dimensional convolutional neural network (CNN)-based state of charge estimation algorithm for electric vehicles. The CNN is trained using two publicly available battery datasets. The influence of different types of noises on the estimation capabilities of the CNN model has been studied. Moreover, a transfer learning mechanism is proposed in order to make the developed algorithm generalize better and estimate with an acceptable accuracy when a battery with different chemical characteristics than the one used for training the model, is used. It has been observed that using transfer learning, the model can learn sufficiently well with significantly less amount of battery data. The proposed method fares well in terms of estimation accuracy, learning speed and generalization capability. [ABSTRACT FROM AUTHOR]

In this paper, real outage data from utilities are analyzed to gain better understanding of cascading outage propagation. In order to accurately estimate the interactions between component outages, two mechanisms are introduced: the evolution of interactions over generations and the memory between consecutive generations. A metric, the expected number of outages following one component outage, is calculated based on the estimated interaction networks by solving a set of carefully formulated linear equations, considering loops due to the complex component interactions. Existence of unique positive solution for the linear equations is mathematically proved. Components that are critical for outage propagation are further identified based on the developed metric. Besides, the outage propagation properties are revealed by the interaction networks estimated from real outage data. Further, the cascades simulated from a highly probabilistic generation-dependent interaction model using the estimated interactions well capture the properties of the original outage data in terms of the distribution of the number of line outages, the offspring mean of the branching process, the distribution of the component metrics, and the identified critical components. Cascading failure risks are greatly mitigated by reducing the probability that the identified critical components fail. [ABSTRACT FROM AUTHOR]

Battery management system (BMS) is one of the key subsystems of electric vehicle, and the battery state-of -charge (SOC) is a crucial input for the calculations of energy and power. Therefore, SOC estimation is a significant task for BMS. In this paper, a new method for online estimating SOC is proposed, which combines a novel adaptive extended Kalman filter (AEKF) and a parameter identification algorithm based on adaptive recursive least squares (RLS). Specifically, according to the first order R-C network equivalent circuit model, the battery model parameters are identified online using the RLS with multiple forgetting factors. Based on the identified parameters, the novel AEKF is used to accurately estimate the battery SOC. The online identification of parameter tracks the varying model. At the same time, due to the novel AEKF algorithm to dynamically adjust the system noise parameter, excellent accuracy of the SOC real-time estimation is obtained. Experiments are set to evaluate the accuracy and robustness of the proposed SOC estimation method. The simulation test results indicate that under DST and UDDS conditions, the maximum absolute errors are less than 0.015 after filtering convergence. In addition, the maximum absolute error is less than 0.02 in the simulation of DST with current and voltage measurement noise, so is in DST with current offset sensor error. The tests indicate that the proposed method can accurately estimate battery SOC and has strong robustness. [ABSTRACT FROM AUTHOR]

This paper presents the results for model-independent control of uncertain ${n}$ -degree of freedom robotic manipulators in the presence of external disturbances and backlash hysteresis. In order to improve the response characteristics of the system and attenuate the uncertainties, the developed robust model-free controller incorporates time delay control (TDC) as well as adaptive terminal sliding mode control (ATSMC) methods. Particularly, the time delay estimation is designed to estimate the unknown dynamics of the robotic manipulators by adopting the theory of TDC. Further, the ATSMC is utilized to obtain the robustness, finite-time convergence, and an adaptive tuning is exploited to deal with bounded derivative of unknown dynamics. The overall system stability is investigated by the Lyapunov theorem application and computed the finite convergence rate thereafter. Finally, a simulation comparison with an existing adaptive fractional-order terminal sliding mode control under backlash hysteresis is provided to illustrate the effectiveness and the superiority of the proposed method. [ABSTRACT FROM AUTHOR]

Causality graphs are routinely estimated in social sciences, natural sciences, and engineering due to their capacity to efficiently represent the spatiotemporal structure of multi-variate data sets in a format amenable for human interpretation, forecasting, and anomaly detection. A popular approach to mathematically formalize causality is based on vector autoregressive (VAR) models and constitutes an alternative to the well-known, yet usually intractable, Granger causality. Relying on such a VAR causality notion, this paper develops two algorithms with complementary benefits to track time-varying causality graphs in an online fashion. Their constant complexity per update also renders these algorithms appealing for big-data scenarios. Despite using data sequentially, both algorithms are shown to asymptotically attain the same average performance as a batch estimator which uses the entire data set at once. To this end, sublinear (static) regret bounds are established. Performance is also characterized in time-varying setups by means of dynamic regret analysis. Numerical results with real and synthetic data further support the merits of the proposed algorithms in static and dynamic scenarios. [ABSTRACT FROM AUTHOR]

Suppose that ${ { y}}= \lvert \text {A} { x}_{0}\rvert +\eta $ where ${ x}_{0}\in {\mathbb R} ^{{d}}$ is the target signal and $\eta \in {\mathbb R}^{{m}}$ is a noise vector. The aim of phase retrieval is to estimate ${x}_{0}$ from ${ { y}}$. A popular model for estimating ${ x}_{0}$ is the nonlinear least squares ${\widehat { {x}}}:={\mathrm{ argmin}}_{ x} \| \lvert \text {A} { x}\rvert - { { y}}\|_{2}$. One has already developed many efficient algorithms for solving the model, such as the seminal error reduction algorithm. In this paper, we present the estimation performance of the model with proving that $\| {\widehat { {x}}}- { x}_{0}\|\lesssim {\|\eta \|_{2}}/{\sqrt {{m}}}$ under the assumption of A being a Gaussian random matrix. We also prove the reconstruction error ${\|\eta \|_{2}}/{\sqrt {{m}}}$ is sharp. For the case where ${ x}_{0}$ is sparse, we study the estimation performance of both the nonlinear Lasso of phase retrieval and its unconstrained version. Our results are non-asymptotic, and we do not assume any distribution on the noise $\eta $. To the best of our knowledge, our results represent the first theoretical guarantee for the nonlinear least squares and for the nonlinear Lasso of phase retrieval. [ABSTRACT FROM AUTHOR]

This paper presents a clustering technique to rank the dominant modes of low frequency oscillations in power systems under model order uncertainty of the signal. The rotational invariance property of the signal subspace is utilized to obtain the modes of oscillations. Rotational invariance of the signal under multiple shifts is considered to obtain the preliminary estimates of the modes. Subsequently, the modes are clustered to obtain the dominant modes that belong to the signal subspace. A signal subspace participation index (SSPI) is used to rank the modes according to their dominance in signal subspace. Thereafter, the convex combination of the preliminary modes, estimated from the multiple shifts of the signal subspace, is used to obtain the final mode estimates. The simulation results are presented for signal generated using the modes obtained from a two-area system. Additionally, the methodology is also tested on field data obtained from the PMU installed at a generating station in India. [ABSTRACT FROM AUTHOR]

Railway traction vehicles transfer forces between wheels and rails through an adhesion coefficient. The adhesion coefficient value can decrease abruptly during the train run. Therefore, the wheels can simply gain high value of the wheels slip velocity. Thus, the vehicles are equipped with slip controllers for this case. During the past decades, many types of slip control strategies were developed, and the new methods are also developed today. Some perspective methods are based on an estimation of the adhesion coefficient or an adhesion force. However, correct operation of these methods is not guaranteed in all cases. Moreover, these methods have some weakness that can decrease their efficiency. Therefore, a novel method based on the adhesion condition estimation is presented in this paper. The adhesion condition is estimated by an unscented Kalman filter. When the method is connected to a controller, it is possible to eliminate the rising wheel slip velocity at its beginning and limit it to the appropriate value. The output of the method is directly used as an input of the controller without any additional calculation as it is used in traditional methods. The input signal does not need any additional filtration, and the method does not require information about the actual train velocity for its proper work. The verification of the method is done with the measured data that were measured on a freight train hauled by an electric locomotive. This paper also presents simulation results of the method with a controller based on the locomotive mathematical model. [ABSTRACT FROM AUTHOR]

An important bottleneck in the design, operation, and exploitation of mechatronic powertrains is the lack of accurate knowledge of broadband external loading. This is caused by the intrusive nature of regular torque measurements. This paper proposes a novel nonintrusive approach to obtain torsional load information on mechatronic powertrains. Online coupled state/input estimation is performed through an augmented nonlinear Kalman filter. This estimation approach exploits general lumped-parameter physics-based models in order to create a widely applicable framework. This paper considers both extended (EKF) and unscented Kalman filtering approaches. Contrary to previous works, no considerable difference in accuracy is obtained from experiments, with a considerably lower computational load for the EKF. This paper reveals the benefits of including rotational acceleration measurements from a theoretical perspective, which is demonstrated through the experimental validation. This drastically increases the broadband accuracy. The result of this paper is an accurate and noninvasive virtual torque sensor with a sufficiently broad bandwidth for use in condition monitoring, control, and future design optimization. [ABSTRACT FROM PUBLISHER]

As a common land vehicle motion constraint used in GNSS/INS integration system, the non-holonomic constraint (NHC) can effectively inhibit the navigation divergence of standalone inertial navigation system (INS) especially for micro-electro-mechanical system (MEMS) during GNSS signal outages. The correct utilization of non-holonomic constraint requires a compensation of lever arm between the IMU center and the valid point of this constraint on the vehicle. Non-holonomic constraint is not always valid for anywhere on the vehicle since rotation of the vehicle may produce a corresponding lateral/vertical velocity in motion. However, the lever arm impact to non-holonomic constraint has not been paid attention sufficiently and been researched thoroughly. The purpose of this paper is to investigate the effect of the lever arm error on the auxiliary ability of the non-holonomic constraint. The lateral/vertical velocity and additional position drift error during GNSS outages caused by the lever arm error were studied. Two integrated navigation systems with different grade of IMUs were used to analyze the accuracy requirements of the lever arm. Analysis results show that the forward lever arm is the most important influence factor, and the lever arm error should be controlled within sub-decimeter to ensure the accuracy and reliability of the non-holonomic constraint assistance. [ABSTRACT FROM AUTHOR]

System inertia plays a vital role in controlling the angular stability of the system during a disturbance. Due to increased penetration of power electronic interfaced sources, such as Solar Photovoltaic (SPV) source, the overall system inertia reduces and varies depending on their operating conditions. In this paper, an approach for online inertia estimation in the power system network with SPV sources is proposed, using the synchronized measurements from Phasor Measurement Units (PMUs). An equivalent swing equation is used to emulate the network dynamics. A relationship between the inertia constant and the roots of this equation is determined. In order to numerically obtain the roots, the Estimation of Signal Parameter via Rotational Invariance Techniques (ESPRIT) method is first used to find the modes present in the frequency signal. A new formulation is proposed to extract an equivalent mode from all the obtained modes. Also, to avoid phase step error, Rate Of Change Of Frequency (ROCOF) is estimated from the equivalent mode of the frequency signal. Results obtained for the 39 bus New England system for various test cases, using Real-Time Digital Simulator (RTDS), prove the efficacy and superiority of the proposed approach over the existing approaches in the literature. [ABSTRACT FROM AUTHOR]

Current non-Lambertian photometric stereo methods generally require a large number of images to ensure accurate surface normal estimation. To achieve accurate surface normal recovery under a sparse set of lights, this paper proposes a non-Lambertian photometric stereo network based on a derived inverse reflectance model with collocated light. The model is deduced using monotonicity of isotropic reflectance and the univariate property of collocated light to decouple the surface normal from the reflectance function. Thus, the surface normal can be estimated by three steps, i.e., model fitting, shadow rejection, and normal estimation. We leverage a supervised deep learning technique to enhance the shadow rejection ability and the flexibility of the inverse reflectance model. Shadows are handled through max-pooling. Information from a neighborhood image patch is utilized to improve the flexibility to various reflectances. Experiments using both synthetic and real images demonstrate that the proposed method achieves state-of-the-art accuracy in surface normal estimation. [ABSTRACT FROM AUTHOR]

This paper presents a parameter estimation technique for a variable-speed wind turbine with permanent magnet synchronous generator and back-to-back voltage source converter. The proposed technique applies the cubature Kalman filter for the joint estimation of the system dynamic state and a modified set of parameters from which the original model parameters can be algebraically recovered. To the authors knowledge, this work is the first attempt to apply such an innovative technique to a PMSM detailed estimation model including the control parameters of the voltage source converter. [ABSTRACT FROM AUTHOR]

The stator winding structure of the main exciter of the brushless synchronous starter/generator (BSSG) is changed at high speed for flux weakening, leading to a sudden excitation decrease of the main generator (MG) of BSSG. Consequently, when proportional-integral (PI) current loop-based control strategies are applied, large current overshoots will occur due to the lack of fast current control ability. This paper adopts model predictive current control (MPCC) to replace PI current control for a better dynamic performance. Besides, a parameter estimation scheme, based on the recursive least square (RLS) algorithm, is proposed to provide MG parameters for MPCC. An effectiveness analysis of the RLS algorithm is performed and two rules are given. According to the two rules, rather than the parameter dynamic characteristics adopted by traditional methods, three MG parameters, dq-axes inductances, and excitation flux linkage are estimated with two decoupled RLS estimators. In addition, a method is proposed to adjust the forgetting factor to track fast changing parameters of MG when the excitation changes suddenly. With the estimated parameters, MPCC is conducted to start BSSG. Experimental results show that fast current control of MG is achieved and large current overshoots caused by the sudden excitation change are avoided. [ABSTRACT FROM AUTHOR]

A novel and practical combined weighted (COM-W) method for target localization based on time difference of arrival (TDOA) is proposed in this paper. For 2-D situations with more than three nodes, the main idea of the proposed method is to obtain multiple preliminary localization results corresponding to the different three-node combinations first, and then use the weighted averaging technique based on the Cramér–Rao lower bound (CRLB) to obtain the final estimation. Considering target localization with a set of fixed-positioning nodes employing TDOA measurements as an example, the performance of the proposed method is evaluated by both simulations and acoustic indoor positioning experiments. The experimental results show that the COM-W method is a closed-form and computationally efficient method. It has an excellent positioning performance under both narrow and square geometric arrangements, specifically in the central cross-shaped area and in the vicinity of the nodes. Compared to the three classic weighted linear least squares (WLS)-based positioning methods, the proposed COM-W method is less sensitive to the measurement noise and the NLOS environment. The proposed method has an important application value for the realization of a TDOA-based indoor target positioning system. [ABSTRACT FROM AUTHOR]