State of charge estimation for lithium-ion batteries: An adaptive approach

State of charge (SoC) estimation is of key importance in the design of battery management systems. An adaptive SoC estimator, which is named AdaptSoC, is developed in this paper. It is able to estimate the SoC in real time when the model parameters are unknown, via joint state (SoC) and parameter estimation. The AdaptSoC algorithm is designed on the basis of three procedures. First, a reduced-complexity battery model in state-space form is developed from the well-known single particle model (SPM). Then a joint local observability/identifiability analysis of the SoC and the unknown model parameters is performed. Finally, the SoC is estimated simultaneously with the parameters using the iterated extended Kalman filter (IEKF). Simulation and experimental results exhibit the effectiveness of the AdaptSoC.     

Robust optimal estimation over networks: Application to battery state of charge estimation

In this paper, we provide a general framework for robust optimal estimation over a lossy and delayed network. A threshold principle is introduced to integrate network‐induced uncertainties into packet losses, which are modeled with a Bernoulli process. Based on stability conditions derived from two Riccati equations, we show the existence of critical observation arrival probabilities below which the optimal estimator stochastically fails to converge. Moreover, the result is extended to a real system with variable process disturbance, which has an indicator for its admissible bound in terms of a given restriction of estimation accuracy. The proposed method is experimented on a specific automobile application, the battery state of charge estimation. Copyright © 2015 John Wiley & Sons, Ltd.     

A novel distance estimation approach for 3D localization in wireless sensor network using multi dimensional scaling

Node localization is very important in Wireless Sensor Network (WSN) and distance estimation between pairs of nodes is the prerequisite for localization and thus the applicability of the reported events. The paper proposes a novel distance estimation algorithm to estimate distances of each node to every other node in the network. The main contribution of the paper is the definition of a dissimilarity matrix representing the distance of each node to every other node in the network. MDS based localization algorithm is used to determine coordinates of the node in a local coordinate system and Helmert Transformation is used to convert the local coordinates of the node into a global coordinate system. The effect of various parameters affecting the performance of proposed algorithm is also presented in the paper. Finally, the efficiency of the proposed algorithm is established through the simulation results.     

Robust adaptive beamforming using a novel signal power estimation algorithm

Recently, many robust adaptive beamforming (RAB) methods based on covariance matrix reconstruction have been proposed. Motivated by the idea, in this paper, a novel and efficient signal power estimator is devised to reconstruct the interference-plus-noise covariance (INC) matrix, with the corresponding RAB algorithm proposed. Firstly, the steering vectors of the incoming sources are derived using the Capon spatial spectrum and known array geometry. Secondly, a set of linear equations is established based on the signal subspace projection, from which the powers of the incoming sources are estimated. Based on the presumed angular sector of the signal-of-interest (SOI), the steering vectors and powers of the SOI and interferences are distinguished, and the INC matrix is then reconstructed. Finally, the beamformer is determined by the estimated INC matrix and SOI steering vector. The proposed algorithm is computationally more efficient than other reconstruction-based methods because there are closed-form solutions for the signal powers. Simulation results indicate that our proposed algorithm performs better than the existing methods at high signal-to-noise ratios (SNRs), and achieves nearly optimal performance across a wide range of SNR.     

车用锂离子动力电池自适应状态联合估计研究

为确定动力电池的剩余电量和峰值功率这两个关键指标, 提出一种基于数据驱动的在线参数辨识方法, 通 过递归最小二乘法精确计算电池的实时参数; 然后设计了一种基于自适应扩展卡尔曼滤波的多状态联合估计算法, 准确估计电池的实时荷电状态; 并在电压、剩余电量和单体峰值电流的多约束条件下, 建立多采样间隔持续峰值功 率估算的数学模型. 最后在MATLAB/Simulink环境下搭建基于纯电动汽车实际运行工况的硬件在环测试模型. 结 果表明: 在初始误差较大时, 剩余电量的估计误差在3%左右, 硬件在环测试系统的端电压误差保持在20 mV以内, 峰值功率的平均误差为4.9745 W, 为联合估计算法的准确性提供了可靠理论依据.     

Robust state estimation for a class of uncertain time-delay systems

This paper considers a robust state estimation problem for a class of uncertain time-delay systems. In this problem, the noise and uncertainty are modelled deterministically via an integral quadratic constraint. The robust state estimation problem involves constructing the set of all possible states at the current time consistent with given output measurements and the integral quadratic constraint. This set is found to be an ellipsoid which is constructed via a linear state estimator.     

Robust maximum likelihood estimation for stochastic state space model with observation outliers

The objective of this paper is to develop a robust maximum likelihood estimation (MLE) for the stochastic state space model via the expectation maximisation algorithm to cope with observation outliers. Two types of outliers and their influence are studied in this paper: namely,the additive outlier (AO) and innovative outlier (IO). Due to the sensitivity of the MLE to AO and IO, we propose two techniques for robustifying the MLE: the weighted maximum likelihood estimation (WMLE) and the trimmed maximum likelihood estimation (TMLE). The WMLE is easy to implement with weights estimated from the data; however, it is still sensitive to IO and a patch of AO outliers. On the other hand, the TMLE is reduced to a combinatorial optimisation problem and hard to implement but it is efficient to both types of outliers presented here. To overcome the difficulty, we apply the parallel randomised algorithm that has a low computational cost. A Monte Carlo simulation result shows the efficiency of the proposed algorithms.     

Robust state estimation for singularly perturbed systems

This paper deals with the design of interval observers for singularly perturbed linear systems. The full-order system is first decoupled into slow and fast subsystems. Then, using the cooperativity theory, an interval observer is designed for the slow and fast subsystems assuming that the measurement noise and the disturbances are bounded and the singular perturbed parameter is uncertain. This decoupling leads to two observers that estimate the lower and upper bounds for the feasible state domain. A numerical example shows the efficiency of the proposed technique.     

Nonlinear HVDC link model reduction in multi AC-HVDC power system state estimation

Attempting to reduce the order of the HVDC link subspace in a multi AC-HVDC state estimator from 11 states to 4 increases the complexity and nonlinearity of the HVDC link measurement equations, decreases the HVDC link estimate accuracy, and increases the density of nonzero HVDC link Jacobian partial derivative terms.     

An adaptive sliding mode observer for lithium-ion battery state of charge and state of health estimation in electric vehicles

As the demand for electric vehicle (EV)'s remaining operation range and power supply life, Lithium-ion (Li-ion) battery state of charge (SOC) and state of health (SOH) estimation are important in battery management system (BMS). In this paper, a proposed adaptive observer based on sliding mode method is used to estimate SOC and SOH of the Li-ion battery. An equivalent circuit model with two resistor and capacitor (RC) networks is established, and the model equations in specific structure with uncertainties are given and analyzed. The proposed adaptive sliding mode observer is applied to estimate SOC and SOH based on the established battery model with uncertainties, and it can avoid the chattering effects and improve the estimation performance. The experiment and simulation estimation results show that the proposed adaptive sliding mode observer has good performance and robustness on battery SOC and SOH estimation.     

Tracking control of a pendulum using a numerically efficient state estimation approach for polynomic non-linear systems

This paper reports on the tracking control of a pendulum. The particular contributions of this paper lie in the development of a numerically efficient approach for the state estimation of a class of non-linear systems which fall under the extended generalized Wiener model structure and its application to the pendulum system. It details the estimation and control aspects for achieving the tracking control of the pendulum where attention is focused on Extended Kalman Filtering (EKF) methods based on Volterra series approximations of the non-linearity for the estimation of the pendulum states. Control is then effected by the Feedback Linearization (FL) technique and the Internal Model Principle (IMP). It is argued that this offers accuracy benefits over linear techniques while substantially reducing the computational burden associated with the standard EKF approaches. The arguments are supported by evidence from a case study system. It is demonstrated that this proposed approach is significantly faster and does not demand any additional hardware configurations. Estimation error and tracking error variance results are included for substantiating the numerical efficiency of the proposed approach.     

Robust receding-horizon state estimation for uncertain discrete-time linear systems

An approach to robust receding-horizon state estimation for discrete-time linear systems is presented. Estimates of the state variables can be obtained by minimizing a worst-case quadratic cost function according to a sliding-window strategy. This leads to state the estimation problem in the form of a regularized least-squares one with uncertain data. The optimal solution (involving on-line scalar minimization) together with a suitable closed-form approximation are given. The stability properties of the estimation error for both the optimal filter and the approximate one have been studied and conditions to select the design parameters are proposed. Simulation results are reported to show the effectiveness of the proposed approach.     

Robust adaptive-scale parametric model estimation for computer vision

Robust model fitting essentially requires the application of two estimators. The first is an estimator for the values of the model parameters. The second is an estimator for the scale of the noise in the (inlier) data. Indeed, we propose two novel robust techniques: the two-step scale estimator (TSSE) and the adaptive scale sample consensus (ASSC) estimator. TSSE applies nonparametric density estimation and density gradient estimation techniques, to robustly estimate the scale of the inliers. The ASSC estimator combines random sample consensus (RANSAC) and TSSE, using a modified objective function that depends upon both the number of inliers and the corresponding scale. ASSC is very robust to discontinuous signals and data with multiple structures, being able to tolerate more than 80 percent outliers. The main advantage of ASSC over RANSAC is that prior knowledge about the scale of inliers is not needed. ASSC can simultaneously estimate the parameters of a model and the scale of the inliers belonging to that model. Experiments on synthetic data show that ASSC has better robustness to heavily corrupted data than least median squares (LMedS), residual consensus (RESC), and adaptive least Kth order squares (ALKS). We also apply ASSC to two fundamental computer vision tasks: range image segmentation and robust fundamental matrix estimation. Experiments show very promising results.     

参数摄动系统的鲁棒H ∞状态估计

基于Shaked提出的参数依赖型有界实引理，研究具有凸多面体参数摄动系统的鲁棒H∞状态估计问题．采用线性矩阵不等式技术，推导出此类不确定系统的全阶鲁棒H∞滤波器存在的充分条件，并将滤波器的设计转化为一个凸优化的求解问题．与传统的基于二次稳定的滤波方案相比，该滤波器设计方法具有较小的保守性．     

Robust estimation of BRDF model parameters

The effect of the Bidirectional Reflectance Distribution Function (BRDF) is one of the most important factors in correcting the reflectance obtained from remotely sensed data. Estimation of BRDF model parameters can be deteriorated by various factors; contamination of the observations by undetected subresolution clouds or snow patches, inconsistent atmospheric correction in multiangular time series due to uncertainties in the atmospheric parameters, slight variations of the surface condition during a period of observation, for example due to soil moisture changes, diurnal effects on vegetation structure, and geolocation errors [Lucht and Roujean, 2000]. In the present paper, parameter estimation robustness is examined using Bidirectional Reflectance Factor (BRF) data measured for paddy fields in Japan. We compare both the M-estimator and the least median of squares (LMedS) methods for robust parameter estimation to the ordinary least squares method (LSM). In experiments, simulated data that were produced by adding noises to the data measured on the ground surface were used. Experimental results demonstrate that if a robust estimation is sought, the LMedS method can be adopted for the robust estimation of a BRDF model parameter.     

Robust estimation of autoregressive processes using a mixture-based filter-bank

A mixture-based framework for robust estimation of ARX-type processes is presented. The ARX process is presumed to suffer from an unknown noise and/or distortion. The approach taken here is to model the overall degraded process via a mixture. Each component of this mixture uses the same ARX model but explores a different noise/distortion process. Estimation of this mixture unifies the preprocessing and process modelling tasks. The quasi-Bayes (QB) procedure for mixture identification is extended to yield a fast recursive update of the estimator statistics. This allows non-stationary noise/distortion effects to be tracked. An application in on-line outlier-robust estimation of an AR process is given.     

Robust fault estimation of uncertain systems using an LMI‐based approach

General recent techniques in fault detection and isolation (FDI) are based on H_∞ optimization methods to address the issue of robustness in the presence of disturbances, uncertainties and modeling errors. Recently developed linear matrix inequality (LMI) optimization methods are currently used to design controllers and filters, which present several advantages over the Riccati equation‐based design methods. This article presents an LMI formulation to design full‐order and reduced‐order robust H_∞ FDI filters to estimate the faulty input signals in the presence of uncertainty and model errors. Several cases are examined for nominal and uncertain plants, which consider a weight function for the disturbance and a reference model for the faults. The FDI LMI synthesis conditions are obtained based on the bounded real lemma for the nominal case and on a sufficient extension for the uncertain case. The conditions for the existence of a feasible solution form a convex problem for the full‐order filter, which may be solved via recently developed LMI optimization techniques. For the reduced‐order FDI filter, the inequalities include a non‐convex constraint, and an alternating projections method is presented to address this case. The examples presented in this paper compare the simulated results of a structural model for the nominal and uncertain cases and show that a degree of conservatism exists in the robust fault estimation; however, more reliable solutions are achieved than the nominal design. Copyright © 2008 John Wiley & Sons, Ltd.     

Robust fault detection of non-linear systems using set-membership state estimation based on constraint satisfaction

In this paper, the robust fault detection problem for non-linear systems considering both bounded parametric modelling errors and measurement noises is addressed. The non-linear system is monitored by using a state estimator with bounded modelling uncertainty and bounded process and measurement noises. Additionally, time-variant and time-invariant system models are taken into account. Fault detection is formulated as a set-membership state estimation problem, which is implemented by means of constraint satisfaction techniques. Two solutions are presented: the first one solves the general case while the second solves the time-variant case, being this latter a relaxed solution of the first one. The performance of the time-variant approach is tested in two applications: the well-known quadruple-tank benchmark and the dynamic model of a representative portion of the Barcelona's sewer network. In both applications, different scenarios are presented: a faultless situation and some faulty situations. All considered scenarios are intended to show the effectiveness of the presented approach.     

Robust global motion estimation and novel updating strategy for sprite generation

A new global motion estimation technique for sprite coding systems is presented. The proposed system can accurately register frames to a sprite without referencing to the sprite. This allows the motion estimation process to be performed in an environment independent of the quality of the sprite. The frame having the highest resolution of the scene is determined and all other frames can be projected on to the space of this chosen frame such that information loss due to decimation can be avoided. The static sprite is strategically updated, according to the major camera motion during decoding, to alleviate the problem of error propagation from the sprite image. Experimental results indicate that the proposed technique is very accurate and robust, which makes it suitable for MPEG-4 sprite coding