On-line Identification Research Articles

Online Parameters Identification and State of Charge Estimation for Lithium-Ion Battery Using Adaptive Cubature Kalman Filter

The state of charge (SOC) of a lithium-ion battery plays a key role in ensuring the charge and discharge energy control strategy, and SOC estimation is the core part of the battery management system for safe and efficient driving of electric vehicles. In this paper, a model-based SOC estimation strategy based on the Adaptive Cubature Kalman filter (ACKF) is studied for lithium-ion batteries. In the present study, the dual polarization (DP) model is employed for SOC estimation and the vector forgetting factor recursive least squares (VRLS) method is utilized for model parameter online identification. The ACKF is then designed to estimate the battery’s SOC. Finally, the Urban Dynamometer Driving Schedule and Dynamic Stress Test are utilized to evaluate the performance of the proposed method by comparing with results obtained using the extended Kalman filter (EKF) and the cubature Kalman filter (CKF) algorithms. The simulation and experimental results show that the proposed ACKF algorithm combined with VRLS-based model identification is a promising SOC estimation approach. The proposed algorithm is found to provide more accurate SOC estimation with satisfying stability than the extended EKF and CKF algorithms.

Joint Estimation of SOC of Lithium Battery Based on Dual Kalman Filter

In order to improve the estimation accuracy of the state of charge (SOC) of electric vehicle power batteries, a dual Kalman filter method based on the online identification of model parameters is proposed to estimate the state of charge in lithium-ion batteries. Here, we build the first-order equivalent circuit model of lithium-ion batteries and derive its online identification model based on extended Kalman (EKF). Considering that the noise value in the EKF algorithm is difficult to select through experiments to achieve the best filtering effect, this paper combines an improved particle swarm optimization algorithm (IPSO) with EKF to realize online model parameter identification. At the same time, the EKF filtering method derived from the state space equation is also used in SOC estimation. It constitutes a dual Kalman filter method for online identification of model parameters and SOC estimation. The experimental and simulation results show that the IPSO–EKF algorithm can adaptively adjust the noise value according to the complex operating conditions of electric vehicles. Compared with the EKF algorithm, our algorithm can identify battery model parameters more accurately. The dual Kalman filter method composed of the IPSO–EKF algorithm and EKF applied to SOC estimation achieved a higher accuracy in the final algorithm verification.

Phenotypic Diversity of Eragrostis Wolf in Egypt

Eragrostis Wolf is considered one of the largest genera of family Gramineae (Poaceae). It is a cosmopolitan genus and represented in Egypt by 14 taxa. The species limits are often overlapping due to the wide range in variation within this genus. So, it is difficult to establish specific boundaries and diagnostic characters. The main goal of this work is to establish the limits and the diagnostic characters in the Egyptian taxa, using cluster analysis and principal coordinates analysis based on morphological characters. Interactive keys have been designed as an aid for online identification and looking for diagnostic characters. The morphological characters of Inflorescence, spikelets, panicle, glumes, palea keels and caryopsis are very important and necessary in the identification of the studied species. The results of coordinate analysis as well as UPGMA dendogram revealed that Egyptian Eragrostis species can be divided into 3 groups: group (A) includes: E. minor and E. cilianensis; group (B) includes E. tef, E. tenuifolia, E. sarmentosa, E. barrelieri, E. aegyptiaca and E. pilosa while group (C) includes E. aspera, E. ciliaris, E. japonica, E. lepida, E. tenella, and E. tremula. More research is needed to confirm the occurrence of several taxa in Egypt, such as Eragrostis purpurascens and E. nitida, which have been reported in some publications but are uncertain.

A three-stage online anomaly identification model for monitoring data in dams

The monitoring of data anomaly identification is an important basis for dam safety online monitoring and evaluation. In this research, a cluster of anomaly identification models for dam safety monitoring data was constructed, and a three-stage online anomaly identification method was proposed to discriminate outliers. The proposed method combined anomaly detection for measured values based on a single-point time series simulation, measurement error reduction based on remote retesting and spatio-temporal analysis, and environmental response mutation recognition. It brought about efficient and accurate detection for data mutation and online classified identification for its inducement. Additionally, problems such as missing outliers, misjudging normal values induced by the environmental response, and difficulty in online identification for measurement errors were effectively solved. The research productions were applied to the online monitoring system for the safety risk of reservoirs and dams in the Dadu River Basin. The results showed that the proposed method could effectively improve the accuracy of anomaly identification and reduce the misjudgment and omission rate to less than 2%. It could also successfully recognize and subtract nonstructural anomalies such as accidental errors, instrument faults, and environmental responses online, which provided reliable data for online dam safety monitoring.

Active noise control without secondary path modeling: algorithm and implementation

Active noise control (ANC) has been intensively studied for decades. The most classical ANC algorithm should be the filtered-x least mean square (FxLMS) algorithm, which needs the model of the secondary path to work. Thus, the residual error of the ANC system is closely related to the preciseness of the secondary path model. In many applications, the secondary path is often time-varying. Therefore, off-line identification of the secondary path is not applicable. However, on-line identification often requires an additional white noise as a stimulating signal of the secondary path, which will deteriorate the final noise reduction effect. This paper proposes an improved artificial bee colony (ABC) algorithm for ANC system, which does not require identification of the secondary path. In order to guarantee the convergence of the algorithm and accelerate the convergence speed, this paper introduces a variable forgetting factor into the fitness function, and improves the traditional ABC algorithm by integrating LMS algorithm into the ABC algorithm. A single channel ANC system equipped with an FPGA hardware platform is set up in an anechoic chamber, and experiments show that the proposed algorithm can produce a satisfactory noise reduction effect without modeling the secondary path.

Research on On-line Identification of Equivalent Circuit Model Parameters and SOC Estimation for Lithium-ion Batteries

The online identification model parameters can reflect the real terminal voltage state of the battery in real time and provide accurate observation data for battery SOC estimation. BMS is an important part of new energy vehicles, which supervises and controls the working process of the battery. The core of BMS is SOC estimation, and the accuracy of its estimation is related to whether the battery works efficiently and safely. Based on Thevenin model, FFRLS-EKF and KF-EKF were used to estimate the SOC of ternary lithium battery under DST condition. The estimation results show that FFRLS-EKF has a maximum error of 0.0079, which can estimate the SOC of the battery well.

Intelligent Diagnosis of Equipment Health Based on IOT and Operation Large Data Analysis

Predictive maintenance integrates equipment condition monitoring, fault diagnosis, fault prediction, maintenance decision support and maintenance activities. Intelligent manufacturing upgrades need to match the synchronous improvement of predictive maintenance capabilities, and predictive maintenance is the basic guarantee for enterprises to achieve intelligent manufacturing. Take the equipment condition monitoring and diagnosis system applicate in process industry as an example, this paper proposes IOT and operation big data analysis to equipment fault monitoring, diagnosis and preventive maintenance. Under the three-layer system framework of perception layer, network layer and application layer, machine learning algorithm is applied to carry out data mining on the equipment operation big data, establish expert knowledge base, obtain diagnosis rules, and realize the intelligent and efficient management mode integrating online monitoring, remote monitoring, remote diagnosis, fault matching and identification. Based on IOT and operation large data analysis, equipment health intelligent diagnosis provides the basic guarantee for equipment intelligent operation and maintenance, which helping enterprise establish new equipment management, maintenance, inspection and repair system under the concept of predictive maintenance and proactive maintenance.

A Review on Internet of Things (IOT) Based Garbage Monitoring System

“Swachh Bharat” is a national campaign by the statutory cities and towns to clean the roads streets and infrastructure of the country. When the massive amount of waste material is collected, it is difficult to separate and unhygienic. Now a day’s garbage is separately thrown i.e. dry and wet. The Internet of Things (IoT) shall be able to incorporate transparently and coherently a large number of different and heterogeneous end systems, while providing open access to selected subsets of data for the developing a digital services. Building a general architecture for the IoT is hence a very complex task, mainly because of the extremely large variety of devices, link layer technologies. One of the main concerns with our environment has been solid waste management which in addition to disturbing the balance of the environment also has adverse effects on the health of the society. The detection, monitoring and management of wastes are one of the primary problems of the present period. The traditional way of manually monitoring the wastes in waste bins is a complex, cumbersome process and utilizes more human effort, time and cost which is not compatible with the present day technologies in any way. This is an advanced method in which waste management is automated. In this “IOT based garbage monitoring system” The separate the wet and dry garbage using the moisturized sensor. This system is based on the separation and monitoring of garbage using Arduino Uno, Wi-Fi-module, ultrasonic sensor. Online identification of the garbage level using IOT, ultrasonic sensor and Wi-Fi module makes this system more effective than other system.

Lithium-Ion Battery SOC Estimation Based on Adaptive Forgetting Factor Least Squares Online Identification and Unscented Kalman Filter

In order to improve the estimation accuracy of the battery state of charge (SOC) based on the equivalent circuit model, a lithium-ion battery SOC estimation method based on adaptive forgetting factor least squares and unscented Kalman filtering is proposed. The Thevenin equivalent circuit model of the battery is established. Through the simulated annealing optimization algorithm, the forgetting factor is adaptively changed in real-time according to the model demand, and the SOC estimation is realized by combining the least-squares online identification of the adaptive forgetting factor and the unscented Kalman filter. The results show that the terminal voltage error identified by the adaptive forgetting factor least-squares online identification is extremely small; that is, the model parameter identification accuracy is high, and the joint algorithm with the unscented Kalman filter can also achieve a high-precision estimation of SOC.

A novel recursive approach for online identification of continuous‐time switched nonlinear systems

AbstractThis article focuses on the identification of switched nonlinear systems, which are characterized as a collection of nonlinear dynamical systems. Each nonlinear subsystem is activated by a discrete‐valued variable (switching signal). Specifically, we consider the continuous‐time switched nonlinear systems in the state‐space form in our article. The identification of switched nonlinear systems amounts to simultaneous estimation of the switching signal and the nonlinear dynamic subsystems via all measured state‐input vectors. However, the problem is challenging and generally requires a large computational complexity to be solved. In this article, we propose a novel online approach to address the identification problem of switched nonlinear systems, which is capable to handle the measured state‐input vectors in sequence. In particular, the principle used for estimating the switching signal is developed based on the subspace method. Subsequently, the integral concurrent learning identifier is extended to identify the dynamics of each subsystem recursively. The effectiveness of the proposed identification approach is demonstrated via simulation results.

Novel trajectory prediction algorithms for hypersonic gliding vehicles based on maneuver mode on-line identification and intent inference

Aiming at the trajectory prediction problem in hypersonic glide vehicle defense, novel trajectory prediction algorithms based on maneuver mode on-line identification and intent inference are proposed. For solving the maneuver mode identification and modeling problems, the auto-regressive method is introduced to reveal the hidden internal law in the historical tracking data and present the parametric model of the maneuver mode, thereby realizing the accurate trajectory prediction. To deal with the maneuver mode mutation problem, a novel intent inference based long-term trajectory prediction algorithm is proposed: firstly, the cost function of maneuver intent is constructed reasonably. Then, to solve the vehicle’s intent recursive calculation problem, the recurrence formula of the vehicle’s intent is derived based on Bayesian estimation theory, and the vehicle’s reachable region rapid resolution and discretization methods are proposed. Finally, the vehicle’s state recursive formula is derived with Bayesian estimation theory, and the trajectory prediction problem is transformed into the probability prediction problem of the vehicle appearing in a specific region. To avoid the large calculation problem in long-term trajectory prediction, the random sampling shooting method is adopted. The proposed trajectory prediction algorithms are combined to are combined to realize complementary advantages and provide as much valuable prediction information as possible for the defender. The simulation results show that the proposed trajectory prediction algorithms share high prediction accuracy and applicability.

An online identification approach for a nonlinear ship motion model based on a receding horizon

The ship motion system is a nonlinear control object, and its system parameters exhibit time-varying characteristics with the ship motion state, which increases the difficulty of control. Therefore, parameter identification has an important significance for the stability of ship motion control. Aiming at the real-time identification problem of the nonlinear and time-varying ship motion system during movement, this paper reconstructs the ship motion system with the propeller speed and rudder angle as control variables and designs an online identification algorithm with the sliding time window method based on the extended Kalman filter algorithm. In addition, to solve the problem of noise in ship motion data collected in real-time, a real-time wavelet filter is developed to perform online preprocessing of the input data of the identification algorithm. The applicability of the method is further demonstrated via a model-scale Korea Research Institute of Ships and Ocean Engineering container ship free-running experiments in a basin.

Multi‐innovation Newton recursive methods for solving the support vector machine regression problems

AbstractThe support vector machine has been widely used in binary classification applications because of the simplicity of its implementation. This article proposes an online identification method based on the support vector machine in the field of parameter identification. By substituting the constraint item into the original criterion function to form a new criterion function about the weight vector and the bias item, and then on the basis of the recursive identification methods, the newly established criterion function is minimized by using the Newton search to derive the Newton recursive support vector machine algorithm. Additionally, in order to improve the performance of the Newton recursive support vector machine algorithm, the multi‐innovation identification theory is applied to optimize the algorithm, and a multi‐innovation Newton recursive support vector machine algorithm is derived. In addition, the convergence and the calculation amount of the proposed algorithms are carefully analyzed in this article. Finally, a numerical simulation example is given to compare the Newton recursive algorithm and the corresponding multi‐innovation recursive algorithm. The results show that the algorithms and optimization strategy studied in this article are effective.

An Online Robot Collision Detection and Identification Scheme by Supervised Learning and Bayesian Decision Theory

This article is dedicated to developing an online collision detection and identification (CDI) scheme for human-collaborative robots. The scheme is composed of a signal classifier and an online diagnosor, which monitors the sensory signals of the robot system, detects the occurrence of a physical human–robot interaction, and identifies its type within a short period. In the beginning, we conduct an experiment to construct a data set that contains the segmented physical interaction signals with ground truth. Then, we develop the signal classifier on the data set with the paradigm of supervised learning. To adapt the classifier to the online application with requirements on response time, an auxiliary online diagnosor is designed using the Bayesian decision theory. The diagnosor provides not only a collision identification result but also a confidence index which represents the reliability of the result. Compared to the previous works, the proposed scheme ensures rapid and accurate CDI even in the early stage of a physical interaction. As a result, safety mechanisms can be triggered before further injuries are caused, which is quite valuable and important toward a safe human–robot collaboration. In the end, the proposed scheme is validated on a robot manipulator and applied to a demonstration task with collision reaction strategies. The experimental results reveal that the collisions are detected and classified within 20 ms with an overall accuracy of 99.6%, which confirms the applicability of the scheme to collaborative robots in practice. Note to Practitioners —This article is intended to provide a novel online collision event handling scheme for robots in industrial environments. This scheme is designed to quickly and accurately detect an accidental collision and distinguish it from the intentional human–robot interaction. The method takes the raw signals from external torque sensors and provides a collision diagnosis result with a reliability index. The simple structure makes it easy to be implemented as a regular fault monitoring routine for collaborative robots. Different from the conventional methods, the proposed collision identification scheme in this article especially focuses on overcoming the following two challenges in practice: first, to timely and accurately report a collision within its early stage, and second, to ensure a high identification accuracy in a complicated environment, where ubiquitous disturbance and noise are unneglectable. The experimental validation at the end of this article confirms its promising application value in human–robot collaboration.

An on-line capacitor state identification method based on improved RLS

Abstract As an essential part of DC-Link in the power converter, capacitor plays a crucial role in absorbing ripple current and suppressing ripple voltage. The health and residual service life of the DC-Link capacitor is one of the decisive factors for the safety, stability, and efficiency of the system in which it is located. Aiming at the shortcomings of existing methods, such as low dynamic sensitivity of data update and fluctuation of identification results, a capacitor state identification method based on improved RLS is proposed in this paper. The proposed method is optimized by introducing the forgetting factor algorithm and root means square algorithm to modify the iterative formula and final identification results. Compared with existing methods, this method can identify the capacitor's current state in real time and accurately. Finally, we successfully verified the accuracy, robustness, and adaptability of the proposed method by a series of experimental tests on a dSPACE platform.

Online detection and removal of eye blink artifacts from electroencephalogram

The most prominent type of artifact contaminating electroencephalogram (EEG) signals are the eye blink (EB) artifacts, which could potentially lead to misinterpretation of the EEG signal. Online identification and elimination of eye blink artifacts are crucial in applications such a Brain-Computer Interfaces (BCI), neurofeedback, and epilepsy diagnosis. In this paper, algorithms that combine unsupervised eye blink artifact detection (eADA) with modified Empirical Mode Decomposition (FastEMD) and Canonical Correlation Analysis (CCA) are proposed, i.e., FastEMD-CCA2 and FastCCA, to automatically identify eye blink artifacts and remove them in an online setting. The average accuracy, sensitivity, specificity, and error rate for eye blink artifact removal with FastEMD-CCA2 is 97.9%, 97.65%, 99.22%, and 2.1%, respectively, validated on a Hitachi dataset with 60 EEG signals, consisting of more than 5600 eye blink artifacts. FastCCA achieved an average of 99.47%, 99.44%, 99.74%, and 0.53% artifact removal accuracy, sensitivity, specificity, and error rate, respectively, validated on the Hitachi dataset too. FastEMD-CCA2 and FastCCA algorithms are developed and implemented in the C++ programming language, mainly to investigate the processing speed that these algorithms could achieve in a different medium. Analysis has shown that FastEMD-CCA2 and FastCCA took about 10.7 and 12.7 ms, respectively, on average to clean a 1-s length of EEG segment. As a result, they’re a viable option for applications that require online removal of eye blink objects from EEG signals.

Polarization Measurements and Evaluation Based on Multidimensional Polarization Indices Applied in Analyzing Atmospheric Particulates

Online identification and characterization of suspended aerosols can provide a scientific basis for understanding aerosol transformations, quantitatively evaluating the impacts on air quality, public health, and the source apportionment of different atmospheric particulate matters. In this study, we confirm the validity of our developed high-throughput multi-angle polarized scattering vector detection of aerosols and multidimensional polarization scattering index systems. By observation of the mean values, variance, and Wilk’s Lambda of multidimensional polarization indices for different aerosol types, the polarization index shows unique characterization abilities for aerosol properties, and the optimal combination of polarization indices can always be found for a specific aerosol category with a high resolution and discrimination. Clearly, the multidimensional polarization indices of individual aerosols are more suitable for online and real-time aerosol identification and even help to explain the in situ microphysical characteristics of aerosols or evaluate the dynamic evolution of aerosols.

Online identification method for a system with unknown varying time-delay and parameter

An online identification method that can simultaneously estimate the unknown system parameter and the unknown time-delay is proposed. Firstly, with the help of Lagrange mean value theorem, the system with time-delay can be transformed into two terms that can be identified by modified least-square algorithm and one term that represents an approximate error. Then, a modified least-square algorithm is introduced to estimate all the unknown parameters in case of external disturbances. Additionally, an restrain term are added in the covariance matrix to enhance the robustness to deal with the approximate error which is related to the estimated error of system parameter and time-delay. Also, the boundedness of the estimation error is guaranteed via Lyapunov stability theory. Finally, the effectivity of the proposed method is verified by simulations results.

Algebraic Parameter Identification of Nonlinear Vibrating Systems and Non Linearity Quantification Using the Hilbert Transformation

A novel algebraic scheme for parameters’ identification of a class of nonlinear vibrating mechanical systems is introduced. A nonlinearity index based on the Hilbert transformation is applied as an effective criterion to determine whether the system is dominantly linear or nonlinear for a specific operating condition. The online algebraic identification is then performed to compute parameters of mass and damping, as well as linear and nonlinear stiffness. The proposed algebraic parametric identification techniques are based on operational calculus of Mikusiński and differential algebra. In addition, we propose the combination of the introduced algebraic approach with signals approximation via orthogonal functions to get a suitable technique to be applied in embedded systems, as a digital signals’ processing routine based on matrix operations. A satisfactory dynamic performance of the proposed approach is proved and validated by experimental case studies to estimate significant parameters on the mechanical systems. The presented online identification approach can be extended to estimate parameters for a wide class of nonlinear oscillating electric systems that can be mathematically modelled by the Duffing equation.

Identification of paralytic shellfish poison producing algae based on three-dimensional fluorescence spectra and quaternion principal component analysis

In view of the problem of the paralytic shellfish poison producing algae on-line measurement and identification, a new feature extraction method of paralytic shellfish poison producing algae measurement and identification based on quaternion principal component analysis (QPCA) is investigated. The three-dimensional (3D) fluorescence spectra of three common species of paralytic shellfish poison producing algae and eight species common of non paralytic shellfish poison producing algae are analyzed. The quaternion parallel representation model of algae three-dimensional fluorescence spectrum data is established, then the features of quaternion principal component is extracted to use as the input of k-nearest neighbor (KNN) classifier, and the identification of paralytic shellfish poison producing algae is realized by the three-dimensional fluorescence spectra coupled with quaternion principal component analysis. The results show that under the quaternion parallel representation model, the recognition accuracy rate of multiplication feature, modulus feature and summation feature is 90%, 95% and 100% respectively. Compared with that of the principal component analysis feature extraction method, the recognition accuracy rate in pure samples by summation feature of quaternion principal component is improved by 10%. This study provides an experimental basis for the accurate monitoring technology of three-dimensional fluorescence spectrum of paralytic shellfish poison producing algae.