Proposed Estimation Method Research Articles

Security-Ensured State of Charge Estimation of Lithium-Ion Batteries Subject to Malicious Attacks

This paper is concerned with the secure estimation problem of the state of charge (SOC) for Lithium-ion batteries subject to deception attacks. A second-order resistor-capacitor (RC) equivalent circuit model (ECM), whose parameters are identified by means of the extended Kalman filter algorithm, is applied to characterize the static and dynamic behaviors of a Lithium-ion battery. With the aid of the polynomial-function fitting technique, the nonlinear relationship between the open circuit voltage and SOC of the ECM is established. The deception attacks launched by the malicious adversary are taken into consideration during the data transmission from the voltage/current sensor to the estimator. By employing the Lyapunov stability theory, a second-order RC-ECM-based nonlinear observer is designed in the presence of deception attacks. In addition, sufficient conditions are derived to guarantee the uniform boundedness of the estimation error and the estimator gain matrix is implicitly parameterized by solving finite linear matrix inequalities with certain constraints. Finally, the effectiveness of the proposed estimation method is verified through the experiments under the Dynamic Stress Test and Federal Urban Driving Schedule driving cycle.

고조파원의 등가 전류원 모델 추정에 기반한 고조파 전류 기여도 평가

This paper presents a method for evaluating harmonic contributions to current distortion injected into a utility system from multiple harmonic sources at the point of common coupling (PCC). For effective harmonic management, it is necessary to identify harmonic sources and to evaluate their contribution to harmonic current distortion. Basically, to evaluate the harmonic contribution, the equivalent current source model of each harmonic source is required. The proposed method estimates the harmonic current source and impedance parameters of the equivalent model using the data measured at PCC and the recursive least squares (RLS) algorithm with variable forgetting factor. The harmonic current contribution can be also evaluated using the principle of current superposition with the estimated harmonic equivalent current model. The performance of equivalent model parameter estimation and harmonic contribution evaluation of the proposed method was analyzed through a case study using the PSCAD/EMTDC test system.

Bayes-informed mixture distribution for the EVD estimation and dynamic reliability analysis

In this paper, a Bayes-informed mixture distribution is proposed to capture the underlying implicit extreme value distribution of a nonlinear dynamic system subjected to stochastic seismic excitation. First, three possible candidate distributions considering the properties of the extreme value distribution are selected, i.e., Gumbel, inverse Gaussian and Gamma distributions. Then, the mixture of these possible candidate distributions is conducted from Bayesian perspective by using the proposed moment-generating function-guided likelihood function. The benchmark data for constructing the likelihood function is the function value of the moment-generating function related to the unknown extreme value distribution, which can be calculated by the Latinized partially stratified sampling technique. The predicted data can be calculated by the mixed moment-generating function of the mixture distribution. Finally, the mixture distribution’s unknown parameters can be calibrated via the Bayesian inference and transitional Markov chain Monte Carlo so that the calibrated mixture distribution can be a representation of the implicit extreme value distribution. Regarding the dynamic reliability analysis, the first passage failure probability can be readily obtained from the mixture distribution. Three nonlinear dynamic systems subjected to the fully non-stationary seismic excitations, i.e., a single-degree-of-freedom system, a 3-D reinforced concrete frame structure and a practical highway bridge are investigated to validate the efficiency and accuracy of the proposed method for extreme value distribution estimation.

Mean residual life cure models for right-censored data with and without length-biased sampling.

We propose a semiparametric mean residual life mixture cure model for right-censored survival data with a cured fraction. The model employs the proportional mean residual life model to describe the effects of covariates on the mean residual time of uncured subjects and the logistic regression model to describe the effects of covariates on the cure rate. We develop estimating equationsto estimate the proposed cure model for the right-censored data with and without length-biased sampling, the latter is often found in prevalent cohort studies. In particular, we propose two estimating equationsto estimate the effects of covariates in the cure rate and a method to combine them to improve the estimation efficiency. The consistency and asymptotic normality of the proposed estimates are established. The finite sample performance of the estimates is confirmed with simulations. The proposed estimation methods are applied to a clinical trial study on melanoma and a prevalent cohort study on early-onset type 2 diabetesmellitus.

Optimized adaptive filtering for fault estimation of the dynamics of high-speed train based on robust extended Kalman filter

In this paper, an optimized adaptive robust extended Kalman filter is proposed based on random weighting factors and an improved whale optimization algorithm for fault estimation of the dynamics of high-speed trains with constant time delays, drastically changing noise and stochastic uncertainties. Robust upper bounds are proposed to improve the performance of the extended Kalman filter by decreasing the influence of the linearization error on filtering for the dynamics of high-speed trains with constant time delays, and its robustness is proven to guarantee the feasibility of the proposed upper bounds. Furthermore, considering drastically changing noise with unknown statistics, a random weighting adaptive algorithm is proposed to implement unbiased noise estimation so that the robust extended Kalman filter can still be implemented well. In addition, a differential evolution algorithm and adaptive parameter are introduced to improve the performance of the whale optimization algorithm so that the stochastic uncertainties are optimized, and the influence of the stochastic uncertainties on filtering is further decreased. The simulation results in the three conditions show that, compared with the variational Bayes adaptive iterated extended Kalman filter, using the proposed method, the position, speed and fault estimation errors are decreased by 31.8%, 33.2% and 28.3%, respectively, on average, which depends on more accurate noise estimation.

An Enhanced Fusion Algorithm With Empirical Thermoelectric Models for Sensorless Temperature Estimation of Li-ion Battery Cells

An enhanced dual extended Kalman filter method is presented in this article for estimating and tracking the state-of-temperature of lithium-ion battery cells. A simple but effective dynamic and measurement empirical fit models are proposed and utilized to estimate the state-of-temperature concurrently with the state-of-charge. The proposed dual estimator improves the estimation accuracy of the temperature state by accounting for the variations in the state-of-charge. To test the performance of the proposed estimation method, two independent lithium-ion battery cell datasets were used to derive the empirical models and run the estimation algorithm. The obtained results show a promising performance of the estimation method in terms of the high estimation accuracy even in the case when the measurement contains high-magnitude noise or when the estimation algorithm is inaccurately initialized. The proposed models and the estimation algorithm are derived and experimentally tested in this article.

Rate-optimal robust estimation of high-dimensional vector autoregressive models

High-dimensional time series data appear in many scientific areas in the current data-rich environment. Analysis of such data poses new challenges to data analysts because of not only the complicated dynamic dependence between the series, but also the existence of aberrant observations, such as missing values, contaminated observations, and heavy-tailed distributions. For high-dimensional vector autoregressive (VAR) models, we introduce a unified estimation procedure that is robust to model misspecification, heavy-tailed noise contamination, and conditional heteroscedasticity. The proposed methodology enjoys both statistical optimality and computational efficiency, and can handle many popular high-dimensional models, such as sparse, reduced-rank, banded, and network-structured VAR models. With proper regularization and data truncation, the estimation convergence rates are shown to be almost optimal in the minimax sense under a bounded (2+2ϵ)th moment condition. When ϵ≥1, the rates of convergence match those obtained under the sub-Gaussian assumption. Consistency of the proposed estimators is also established for some ϵ∈(0,1), with minimax optimal convergence rates associated with ϵ. The efficacy of the proposed estimation methods is demonstrated by simulation and a U.S. macroeconomic example.

Data fusion in a two-stage spatio-temporal model using the INLA-SPDE approach

This paper proposes a two-stage estimation approach for a spatial misalignment scenario that is motivated by the epidemiological problem of linking pollutant exposures and health outcomes. We use the integrated nested Laplace approximation method to estimate the parameters of a two-stage spatio-temporal model — the first stage models the exposures using data fusion while the second stage links the health outcomes to exposures. The first stage is based on the Bayesian melding model, which assumes a common latent field for the different data sources for the pollutants. The second stage fits a generalized linear mixed model using the spatial averages of the estimated latent field, and additional spatial and temporal random effects. Uncertainty from the first stage is accounted for by simulating repeatedly from the posterior predictive distribution of the latent field. A simulation study was carried out to assess the impact of the sparsity of the data on the monitors, number of time points, and the specification of the priors in terms of the biases, RMSEs, and coverage probabilities of the parameters and the block-level exposure estimates. The results show that the parameters are generally estimated correctly but there is difficulty in estimating the Matèrn field parameters. The effect of exposures on the health outcomes is the primary parameter of interest for spatial epidemiologists and health policy makers, and our results show that the proposed method estimates these very well. The proposed method is applied to measurements of NO2 concentration and respiratory hospitalizations for year 2007 in England. The results show that an increase in NO2 levels is significantly associated with an increase in the relative risks of the health outcome. Also, there is a strong spatial structure in the risks, a strong temporal autocorrelation, and a significant spatio-temporal interaction effect.

Lipase-catalyzed two-step transesterification of diols: Estimation of selectivities

Reactions for the lipase-catalyzed kinetic resolution of chiral diols or the desymmetrization of achiral meso-diols involve two alternative routes, each of which contains two steps. During such reactions, up to four different nucleophiles compete to attack the acyl-enzyme intermediate of the catalytic cycle. The selectivities of the lipase for these different nucleophiles determines the success of the desymmetrization or resolution process and are important parameters for time-based mathematical models of these processes. Current methods for estimating these selectivities are not sufficiently accurate. In the current work, we develop a new approach to determining selectivities in lipase-catalyzed desymmetrization or resolution reactions with diols. The method is based on a model in which the only parameters are selectivities. We demonstrate the application of the method using literature data for three case studies of increasing complexity: (i) the two-step acetylation of phlorizin; (ii) the kinetic resolution of racemic 1,3-butanediol and (iii) the two-step desymmetrization of a meso-diol to produce a monoacetylated diol that is a key intermediate in the synthesis of biotin. We demonstrate the advantages of our estimation method over previously proposed estimation methods. The selectivities estimated by our method will be important parameters in models describing the desymmetrization and resolution of diols.

Nonparametric estimation for uncertain differential equations

In recent years, the researches on parameter estimation of uncertain differential equations have developed significantly. However, when we deal with some nonparametric uncertain differential equations, the parameter estimation may not be used directly. To deal with these uncertain differential equations, it is important to consider the nonparametric estimation with the help of the observations. As an important branch of uncertain differential equation, autonomous uncertain differential equation may be properly applied to model some uncertain autonomous dynamic systems. In this paper, we propose a Legendre polynomial based method for the nonparametric estimation of autonomous uncertain differential equations. After that, some numerical examples are given and the residuals as well as uncertain hypothesis tests are used to prove the acceptability of these estimations. In application, we consider an atmospheric carbon dioxide model by the proposed method of nonparametric estimation.

Thermal Conductivity of Rocks and Estimates of Heat Flow in the Lena–Anabar Interfluve (Siberian Platform)

Abstract —We present results of measurements of the physical properties (thermal conductivity, porosity, permeability, and density) of 65 air-dry sedimentary-rock samples from the cores of six deep wells drilled in the Lena–Anabar interfluve. The rocks are compact low-porosity, almost impermeable siltstones, sandstones, and dolomites mainly of Paleozoic and Precambrian ages. Correlations of thermal conductivity with porosity and bulk density have been established. The available information about the thermal conductivity of rocks as well as the production thermograms recorded after drilling made it possible to estimate the geothermal gradient and heat flow (q) for the Ust’-Olenekskaya-2370, Charchykskaya-1, Khastakhskaya-930, and D’yappal’skaya-1 wells. The gradient was calculated from the temperature values at the lower boundary of the permafrost (0 ºC) and at the bottom-hole. The determined heat flow varies from 37 to 70 mW/m2. These q estimates are consistent with the available data on the distribution of heat flow in the north of the Siberian Platform. The proposed method for heat flow estimation is worthy of use in other northern regions of Siberia for obtaining more geothermal data.

Model-Based Estimation of Vehicle Center of Gravity Height and Load

AbstractWith an increasing number of driver assistance functions and the upcoming trend of autonomous driving, knowledge of the current state and changeable parameters of the vehicle becomes more and more important. One particularly significant parameter with regard to vehicle dynamics is the center of gravity (COG) height, which mainly accounts for its roll dynamics in combination with the mass of the vehicle. Thus, a highly increased vehicle mass and COG height might lead to rollover during cornering, which underlines the need for accurate knowledge of the load of the vehicle. Based on that, an improved rollover prevention could be implemented, for instance, by enhancing the electronic stability program (ESP) of the vehicle. Therefore, the main contribution of this work is the model-based online estimation of the additional load of the vehicle, comprising its COG position and mass. This is achieved by applying a joint extended Kalman Filter (EKF) for the simultaneous state and parameter estimation. Based on a nonlinear model with roll, pitch, and vertical dynamics, an accurate and reliable estimation is possible. One major novelty of this work is the consideration of air suspension systems on top of conventional steel spring suspension systems. Therefore, a nonlinear air spring model with sufficient complexity is proposed, making it suitable for real-time applications. Further, a system theoretical observability analysis allows for an online adaptation of the Kalman Filter weights in order to account for different driving situations. The proposed estimation method is tested and validated by considering a wide range of driving situations, considering distinct loading conditions on both a test track and public roads. The estimation accuracy lies within roughly 50 kg and 1.5 cm for the vehicle mass and COG height, respectively.

Probabilistic seismic performance assessment of nuclear containment structure considering statistical uncertainty

Statistical uncertainty is inherently involved in probabilistic seismic performance assessment of containment structure due to heavy computational efforts of nonlinear dynamic analysis. Aiming at the statistical uncertainty involved in seismic fragility and seismic risk evaluation of containment structure, this study proposes a method for quantifying the statistical uncertainty in probabilistic seismic performance assessment of the containment structure. Taking a three story reinforced concrete frame as an example, effectiveness and efficiency of the proposed method for confidence interval estimation of seismic fragility curve is verified. Then, the proposed method is applied to fragility analysis and seismic risk evaluation of the containment structure. Roof drift of the containment structure under different ground motion intensity levels is discussed. Confidence interval for seismic fragility curve and seismic risk of the containment structure considering statistical uncertainty is quantified by the proposed method. The results highlighted the necessity for quantifying the impact of statistical uncertainty involved in probabilistic seismic performance evaluation of the containment structure.

Investment Estimation in the Energy and Power Sector towards Carbon Neutrality Target: A Case Study of China

The transition towards low-carbon energy and power has been extensively studied by research institutions and scholars. However, the investment demand during the transition process has received insufficient attention. To address this gap, an energy investment estimation method is proposed in this paper, which takes the unit construction costs and potential development of major technology in the energy and power sector as input. The proposed estimation method can comprehensively assess the investment demand for various energy sources in different years, including coal, oil, natural gas, biomass, power, and hydrogen energy. Specifically, we applied this method to estimate the investment demand of China’s energy and power sector from 2020 to 2060 at five year intervals. The results indicate that China’s cumulative energy investment demand over this period is approximately 127 trillion CNY, with the power sector accounting for the largest proportion at 92.35%, or approximately 117 trillion CNY. The calculated cumulative investment demand is consistent with the findings of several influential research institutions, providing validation for the proposed method.

Stress–Strength Inference on the Multicomponent Model Based on Generalized Exponential Distributions under Type-I Hybrid Censoring

The stress–strength analysis is investigated for a multicomponent system, where all strength variables of components follow a generalized exponential distribution and are subject to the generalized exponential distributed stress. The estimation methods of the maximum likelihood and Bayesian are utilized to infer the system reliability. For the Bayesian estimation method, informative and non-informative priors combined with three loss functions are considered. Because the computational difficulty on working posteriors, the Markov chain Monte Carlo method is adopted to obtain the approximation of the reliability estimator posterior. In addition, the bootstrap method and highest probability density interval are used to obtain the reliability confidence intervals. The simulation study shows that the Bayes estimator with informative prior is superior to other competitors. Finally, two real examples are given to illustrate the proposed estimation methods.

New estimation method of limit pressures for thick curved pipes under internal pressure applicable to various bend angles

In this study, a new estimation method of limit pressures (loads) is suggested for reliability design of curved pipes under high internal pressure and temperature. The curved pipes are used in boiler pipes in a supercritical thermal power plants. To find any design parameters and their dimensions in reliability design of curved pipes, various boilers in supercritical thermal power plants in operation were investigated. In order to analyse the effect of the design parameters on the limit pressure, the design of experiment (DOE) was applied to design a curved pipes with various combinations of design parameters, and then the FE limit load analyses were performed to obtain limit pressures. The thickness of the curved pipe has the greatest effect on the limit pressure among the design parameters. Although the bend angle is design parameter, the proposed estimation methods for easily calculating the limit load do not include the bend angle and then there have been difficulties in reliability design of curved pipes with any band angle. Therefore, to solve such difficulties, two estimation methods of limit pressure (load) including bend angle were suggested and the validity of the proposed estimation methods of limit load (plastic pressure) under internal pressure was objectively verified through statistical error analysis with the 60 FE analysis results which are different from the data used when the method was derived. The proposed estimation method applicable to various bend angles shows the best results in the evaluation of mean error, maximum error, and standard deviation of error, which are evaluation criteria. The proposed estimation method shows a very good result compared to existing methods, having the mean error of 0.89%, a maximum error of 2.50%, and a standard deviation of 0.70% for all data regardless of the bend angle.

Efficient Nonlinear Multi-Parameter Decoupled Estimation of PMSM Drives Based on Multi-State Voltage and Torque Measurements

Accurate and fast parameter estimation is essential for efficient operation and control of permanent magnet synchronous machine (PMSM). In this paper, a novel multi-parameter decoupling estimation method based on multi-state measurement is proposed to analyze and estimate the important parameters including winding resistance, permanent magnetic flux linkage, and <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$d-q$</tex-math></inline-formula> axis inductances. An improved PMSM model containing inverter distorted voltage and nonlinear inductance considering magnetic saturation is established at first, then the model is decoupled to reduce the cross influences among the parameters and improve the computation efficiency through model dimension reduction according to the proposed decoupling method, and finally each parameter is estimated from multi-state voltage and torque measurement with improved efficiency and accuracy. This decoupling method separates the resistance and the inductance based on electromagnetic torque and reluctance torque, which is the crucial part of the estimation method proposed in this paper. Experiment on a PMSM driving system and comparison with existing methods are conducted to show that the proposed estimation method can improve the accuracy of estimation and calculation efficiency.

Can we work more safely and healthily with robot partners? A human‐friendly robot–human‐coordinated order fulfillment scheme

This work addresses the issue of intelligent robot–human‐coordinated parts‐to‐picker order fulfillment carried out in a human‐friendly manner. One unique feature of the proposed approach involves integrating a real‐time data‐driven stochastic‐dynamic model with a fatigue accumulation function. The optimal solutions help achieve coordination between human pickers and robots, such that robots can agilely adapt to the coordinated pickers’ efficiency and fatigue conditions. Specifically, the proposed method estimates human pickers’ instantaneous performance and robot queue lengths, which are then fed back in real time as indexes to adjust robots’ speeds of handling racks and moving them to human pickers. Using data that are provided by a giant electronic commerce (e‐commerce) company, our analyses demonstrate that the proposed robot–picker coordination system permits alleviating a picker's fatigue without much influence on picking efficiency. In particular, a picker's accumulated fatigue can be reduced by 53.74% at the expense of lowering picker efficiency by 14.79% if the proposed robot–picker coordination system is applied in the focal firm of the study case. Through our scenario design and sensitivity analysis, additional findings and managerial insights, including the rules of human‐friendly robot behaviors for coordination with human pickers in different operational scenarios are provided. They facilitate the development of “human‐friendly” intelligent robot‐human‐coordinated order‐fulfillment systems for intelligent logistics operations.

Data-driven lithium-ion batteries capacity estimation based on deep transfer learning using partial segment of charging/discharging data

Accurate estimation of lithium-ion battery capacity is crucial for ensuring its safety and reliability. While data-driven modelling is a common approach for capacity estimation, obtaining cycling data during charging/discharging processes can be challenging. Collecting cycling data under various charging/discharging protocols is often unrealistic, and the collected data can be fragmented due to the random nature of working conditions in practice. To address these issues, we propose a deep transfer learning method that uses partial segments of charging/discharging data for battery capacity estimation. The proposed method utilizes capacity increment features of partial charging/discharging segments that is designed to satisfy practical scenarios. A deep transfer convolutional neural network (DTCNN) is trained with both source and target data, and a fine-tuning strategy is employed to effectively eliminate distribution discrepancies between different battery types or charging/discharging protocols, leading the improved estimation accuracy. Experimental results demonstrate that the proposed method accurately estimates the lithium-ion battery capacity, with values of RMSE, MAPE, and MD-MAPE of only 0.0220, 0.0247, and 0.0194, respectively, when using partial segments. These results highlight the promising prospects of the proposed method for lithium-ion battery capacity estimation.

An Alternative Method for Estimating the Parameters of Log-Cauchy Distribution

In this paper, we discuss the estimation problem of the location and scale parameters of the log-Cauchy distribution, a member of the super heavy-tailed distributions family. We consider several methods of estimation, including a new percentile method, maximum likelihood estimation and robust estimators. A Monte Carlo simulation experiment is conducted to compare the proposed estimation methods. Further, we illustrate the estimation methods via a real life example.