Modeling and Parameter Identification of Supercapacitor Battery

In this paper, several reported blind adaptive beam- forming algorithms, including the stochastic gradient (SG) methods and the recursive least squares (RLS) methods are reviewed. Then, the conjugate gradient (CG) algorithm is introduced as a tradeoff between SG and RLS algorithms. On the basis of this idea, a novel constrained constant modulus (CCM) design criterion is described and employed for deriving a modified conjugate gradient (CG) optimization algorithm, which does not require the information of the desired user beforehand. Simulations are performed to compare the performance of the proposed method with both SG and RLS methods. The results indicate that the proposed approach has a significant faster convergence rate and superior resolution compared with the SG algorithms and lower computation complexity compared with the RLS algorithms.

Letter to the editor

In this thesis, four methods specifically designed for sparse systems estimation are originally developed and presented, which were called here: Relaxations method, Successive Expansions method, l 1 -norm Minimization method and Automatic Adjustment of the Regularization Factor method. The four proposed methods are based on the Least Squares (LS) Estimation method and incorporate techniques related to convex optimization and to the theory of compressive sensing. The simulation results show that the proposed methods herein present superior performance than the ordinary LS estimation method and the Recursive Least Squares (RLS) with convex regularization method (l 1 -RLS), in many cases achieving the same optimal performance presented by the LS Oracle method. Furthermore, the proposed methods demand lower computational cost than the l 1 -RLS method.

Recursive identification of multiple-input single-output fractional-order Hammerstein model with time delay

Chapter 6 - Equivalent modeling and parameter identification of power lithium-ion batteries

Vanadium redox flow battery (VRB) energy storage system has been widely utilized in renewable energy applications such as wind power integration and green buildings. An online electrical model of VRB is needed to monitor and estimate battery states by using real time data since the internal parameters of battery are varying during operation. In this paper, an improved second-order Thevenin model is proposed to describe the operating characteristics of VRB. To overcome the drawback of recursive least squares (RLS) algorithm in tracking parameter identification, this paper proposes a method based on time-varying forgetting factor recursive least Squares (TFF-RLS) algorithm to identify model parameters. According to the data collected by the pulse charging experiment, the RLS algorithm and the TFF-RLS algorithm are used to identify the model parameters. Experiments are carried out on a 5kWh/3kWh VRB experimental platform in Wuhan University of Technology. Two online identification methods are compared. The results show that the TFF-RLS method demonstrates a better performance on tracking the variation of parameters dynamically, and improving estimation accuracy compared with the RLS method.

This paper presents a method for evaluating the harmonic contributions of multiple harmonic sources to voltage distortion at the point of common coupling (PCC). The proposed method quantitatively represents harmonic contributions based on the equivalent voltage models of harmonic sources. The equivalent voltage models can be estimated using the recursive least square (RLS) algorithm and measured voltage and current at the PCC. The assessment of harmonic contribution heavily depends on the accuracy of equivalent model estimation. Many existing studies have assumed that the equivalent model parameters of harmonic sources are not changed. However, because harmonic source parameters and system operating conditions are not constant in real power systems, this assumption can lead large errors in harmonic contribution assessment. In this paper, we propose a new assessment method based on variable forgetting factor RLS and parameter change detection to solve the problem. The proposed method is also effective for evaluating comprehensive harmonic contributions as well as individual harmonic contributions. The case studies show that the proposed method is superior to the previous one even under parameter changing conditions.

System identification, which includes parameter identification and non-parameter identification, is to estimate its mathematical model based on the input and output observation in system. This paper discusses the system identification theory and establishes a transfer function of 1/4 vehicle’s second-order vibration system model. Through the discrete transfer function, the system’s difference equation can be obtained to identify the system in two ways, RLS (recursive least squares) and RELS (extended recursive least squares). Finally, the paper makes a comparative analysis about RLS and RELS in connection with the vehicle model. The results show that RELS method is more accurate and has stronger convergence than RLS method, which provides the basis for the researching of control system’s algorithm, simulation and making control strategy.

<p>Location Based Services (LBS) in the realms of Smart City realizations require accurate real time positioning information of objects and people indoors. Both VLC based and the RF based positioning techniques have been studied in the literature. In RF system, we use Recursive Least Square (RLS) multilateration algorithm to estimate the position of unknown target indoor. The RLS multilateration algorithm is based on the trilateration solution using algebraic method. Solution obtained from the trilateration is used in RLS algorithm and the solution is updated recursively. Since Machine Learning (ML) is one of the widely used approaches to improve the accuracy in indoor positioning, we have used Artificial Neural Network (ANN) to fine tune the simulated distances with Rician noise. Therefore, the proposed technique hybridizes RLS with Artificial Neural Network (ANN) to solve a multilateration problem of different sets of anchor nodes. </p> <p>We use MATLABTM for the simulation purpose. While simulating distances, Rician noise is considered for anchor-target distance and, noisy distances are fed into ANN filter. Instead of using directly simulated distances, estimated distances from ANN are used for RLS implementation. Results from the hybrid RLS-ANN are compared with pure RLS, pure least square (LS) and LS-ANN approaches. Hybrid RLS-ANN provides the least Root Mean Square Error (RMSE) among all the techniques and improves the accuracy up to 80% compared to the pure LS multilateration technique. Complexity of the proposed technique is relatively low with significantly increased accuracy. </p> <p>We also compare the results with different sets of anchor nodes for RLS implementation. Hybrid RLS-ANN algorithm is used for anchor nodes <em>N </em>= 4 to <em>N </em>= 10. Comparing the results, it has been shown that as the number of anchor nodes increases, the RMSE decreases steadily. RLS multilateration with large number of anchor nodes performs better than LS multilateration. On the other hand, distance error is largely reduced by using ANN training and RLS with ANN estimated distances further improves the accuracy. </p> <p>In VLC system, we analyze the impact of SNR on indoor positioning. VLC indoor positioning is analyzed based on SNR distribution on the room indoor. Distance measurement is analyzed with three parameters: angle of incidence, angle of irradiance and transmitter FOV. Since SNR has the direct impact on the VLC indoor distance measurement, a comparative study of the SNR variation with respect to transmitter FOV, receiver FOV, angles of incidence and irradiance is presented, and corresponding distance measurement error is analyzed. </p>

Electrocardiogram (ECG) is easily submerged in noise of the complex environment during remote medical treatment, and this affects the intelligent diagnosis of cardiovascular diseases. Considering this situation, this paper proposes an echo state network (ESN) denoising algorithm based on recursive least square (RLS) for ECG signals. The algorithm trains the ESN through the RLS method, and can automatically learn the deep nonlinear and differentiated characteristics in the noisy ECG data, and then the network can use these characteristic to separate out clear ECG signals automatically. In the experiment, the proposed method is compared with the wavelet transform with subband dependent threshold and the S-transform method by evaluating the signal-to-noise ratio and root mean square error. Experimental results show that the denoising accuracy is better and the low frequency component of the signal is well preserved. This method can effectively filter out complex noise and effectively preserve the effective information of ECG signals, which lays a foundation for the recognition of ECG signal feature waveform and the intelligent diagnosis of cardiovascular disease.

A single-input and single-output (SISO) controlled autoregressive moving average system with scaled input-output data is considered here. Recursive least squares (RLSs) methods were used to estimate the nanosized parameters of a SISO linear model using input-output scaling factors. Thus, a general identification technique, through scaling data, was produced. Different variations of the RLS method were tested and compared. The first RLS method used a forgetting factor and the second method integrated a Kalman filter covariance. Using the described method, in order to estimate the resistance, time constant and inductance, the latter two lying within the nano range, the input signal must have both a high frequency and a high sampling rate, in relation to the time constant. The method developed here can be used to identify the nano parameters characterising the linear model, while allowing for a broader sampling rate and an input signal with lower frequency. Simulation results indicate that the proposed algorithm is both effective and robust at estimating the nano range parameters. The most powerful contribution contained here is the provision of a scaled identification bandwidth and sampling rate for the detecting signal in the identification process.

Online identification of mechanistic milling force models

With increasing installation capacity and wind farms penetration, wind power plays more important role in power systems, and the modeling of wind farms has become an interesting research topic. In this paper, a coherency-based equivalent model has been discussed for the doubly fed induction generator (DFIG). Firstly, the dynamic models of wind turbines, DFIG and the mechanisms are briefly introduced. Some existing dynamic equivalent methods such as equivalent wind model, variable speed wind turbine model, parameter identification method and modal equivalent method to be used in wind farm aggregation are discussed. Then, considering wind power fluctuations, a new equivalent model of a wind farm equipped with doubly-fed induction generators is proposed to represent the interactions of the wind farm and grid. The method proposed is based on aggregating the coherent group wind turbines into an equivalent one. Finally, the effectiveness of the equivalent model is demonstrated by comparison with the wind farm response obtained from the detailed model. The dynamic simulations show that the present model can greatly reduce the computation time and model complexity.

<div class="section abstract"><div class="htmlview paragraph">The longitudinal dynamics control is an essential task of vehicle dynamics control. In present, it is usually applied by adjusting the slip ratio of driving wheels to achieve satisfactory performances both in stability and accelerating ability. In order to improve its performances, the coordination of different subsystems such as engine, transmission and braking system has to be considered. In addition, the proposed algorithms usually adopt the threshold methods based on less road condition information for simpleness and quick response, which cannot achieve optimal performance on various road conditions.</div><div class="htmlview paragraph">In this paper, an integrated longitudinal vehicle dynamics control algorithm with tire/road friction estimation was proposed. First, a road identification algorithm was designed to estimate tire forces of driving wheels and the friction coefficient by the method of Kalman Filter and Recursive Least Squares (RLS). Then, a rule based integrated control algorithm which coordinate the engine torque control, brake pressure control and transmission shifting control was built to improve the vehicle driving performance.</div><div class="htmlview paragraph">During the longitudinal dynamics control procedure, the transmission shifting control algorithm firstly decided whether the vehicle starting up at the 1st or the 2nd gear based on the road condition. Then, the engine torque control algorithm was applied to adjust the slip ratio of driving wheels. Its process was divided into three phases. In each phase, different control rules with integration of engine torque and brake torque were set by the combined feed forward and feedback methods to meet with requirements of accuracy, rapidity and stability. The brake pressure was determined by method of sliding mode control (SMC) for its rapid adjustment characteristic, in the meanwhile, it was also an important component to be considered in engine torque feedback control.</div><div class="htmlview paragraph">Finally, the algorithm was verified by using Matlab/Simulink and CarSim co-simulation, the results show that the proposed control algorithm could regulate slip ratios of driving wheels fast and accurately, and the vehicle driving performance could be improved effectively.</div></div>

Lithium-ion power battery is the power source of many electronic control systems. The battery state of charge (SOC) and state of health (SOH) are very important to the safety and reliability of the electronic system. Therefore, high-precision estimation of battery SOC and SOH has become one of the current research hotspots in the battery field. In order to realize the state estimation of lithium-ion power battery, an equivalent circuit model of lithium-ion power battery is established. This article is based on the HPPC pulse experiment data and the second-order Thevenin equivalent circuit model. At different temperatures (0°C, 25°C, 45°C), the recursive least square (RLS) method is used to identify of parameters of the equivalent model circuit. The state equation and measurement equation of the double extended Kalman filter (DEKF) algorithm are used to estimate SOC and SOH. Finally, this article uses experimental data of the single cell to estimate the battery state. The accuracy and adaptability of the combination of least squares algorithm and dual extended Kalman filter are verified. This is of great significance for improving the safety, reliability and economic benefits of the battery system.