Battery equivalent circuit models (ECMs) are widely employed in online battery management applications. The model parameters are known to vary according to the operating conditions, such as the ...battery state of charge (SOC). Therefore, online recursive ECM parameter estimation is one means that may help to improve the modelling accuracy. Because a battery system consists of both fast and slow dynamics, the classical least squares (LS) method, that estimates together all the model parameters, is known to suffer from numerical problems and poor accuracy. The aim of this paper is to overcome this problem by proposing a new decoupled weighted recursive least squares (DWRLS) method, which estimates separately the parameters of the battery fast and slow dynamics. Battery SOC estimation is also achieved based on the parameter estimation results. This circumvents an additional full-order observer for SOC estimation, leading to a reduced complexity. An extensive simulation study is conducted to compare the proposed method against the LS technique. Experimental data are collected using a Li ion cell. Finally, both the simulation and experimental results have demonstrated that the proposed DWRLS approach can improve not only the modelling accuracy but also the SOC estimation performance compared with the LS algorithm.
•A novel adaptive estimation method of battery ECM parameters and SOC is presented.•The battery parameters of the fast and slow dynamics are estimated separately.•The proposed method does not require a full-order observer for SOC estimation.•The battery modelling and SOC estimation accuracy is improved.•The proposed algorithm is suitable for both offline and online implementation.
Abstract
Original approaches to the solution of some common management problems with the use of latent variables are proposed in the work. Calculations of the values of the latent variables in the ...Rasch model is based on the least squares technique thus allowing to perform computations with the use of standard application packages.
Underwater vehicles (UVs) are subjected to various environmental disturbances due to ocean currents, propulsion systems, and un-modeled disturbances. In practice, it is very challenging to design a ...control system to maintain UVs stayed at the desired static position permanently under these conditions. Therefore, in this study, a nonlinear dynamics and robust positioning control of the over-actuated autonomous underwater vehicle (AUV) under the effects of ocean current and model uncertainties are presented. First, a motion equation of the over-actuated AUV under the effects of ocean current disturbances is established, and a trajectory generation of the over-actuated AUV heading angle is constructed based on the line of sight (LOS) algorithm. Second, a dynamic positioning (DP) control system based on motion control and an allocation control is proposed. For this, motion control of the over-actuated AUV based on the dynamic sliding mode control (DSMC) theory is adopted to improve the system robustness under the effects of the ocean current and model uncertainties. In addition, the stability of the system is proved based on Lyapunov criteria. Then, using the generalized forces generated from the motion control module, two different methods for optimal allocation control module: the least square (LS) method and quadratic programming (QP) method are developed to distribute a proper thrust to each thruster of the over-actuated AUV. Simulation studies are conducted to examine the effectiveness and robustness of the proposed DP controller. The results show that the proposed DP controller using the QP algorithm provides higher stability with smaller steady-state error and stronger robustness.
A right preconditioner for the LSMR method Hasanpour, Afsaneh; Mojarrab, Maryam
Journal of physics. Conference series,
08/2020, Letnik:
1592, Številka:
1
Journal Article
Recenzirano
Odprti dostop
The LSMR (Least Squares Minimal Residual) method is an absorbing solver that can solve linear system Ax = b and least squares problem min ||Ax = b|| where A is a sparse and large matrix. This method ...is based on the Golub-Kahan bidiagonalization process and sometimes it may converge slowly like other methods. I n order to prevent this event, a right preconditioner for LSMR method is presented to solve large and sparse linear system which used for LSQR (Least Squares with QR factorization) method before. Numerical examples and comparing the preconditioned LSMR method to unpreconditioned LSMR method would show the effectiveness of the preconditioner. I t is obtained from this paper that PLSMR (Preconditioned LSMR) method has a better performance in reducing the number of iterations and relative residual norm in comparing with the original LSMR method.
•Efficient iterative response surface method of reliability analysis.•Obtain a converged centre point iteratively to construct the final response surface.•Moving least squares method for efficiency ...and improved accuracy.•Reliability estimate comparison by both the second moment method and simulation approaches.
The moving least-squares method (MLSM) is a more accurate approach compare to the least-squares method (LSM) based approach in approximating implicit response of structure. The advantage of MLSM over LSM is explored to reduce the number of iterations required to obtain the updated centre point of design of experiment (DOE) to construct the final response surface for efficient reliability analysis of structures. The initial response surface is constructed based on a simplified DOE with mean values of the random variables as the centre point and updated successively to obtain the improved response surface. The reliability of structure is evaluated using this final response surface. The basis of the efficiency of the proposed method hinges on the use of simplified DOE instead of computationally involved full factorial design to achieve desired accuracy. As MLSM is more accurate compare to LSM in evaluating response surface polynomial, the centre point obtained is expected to be more accurate during iterations. Thus, the number of iteration in the update procedure will reduce and the accuracy of computed reliability will also improve. The improved performance of the proposed approach with regard to efficiency and accuracy is elucidated with the help of three numerical examples.
A physics-inspired hybrid method for extrapolating the conducting target's radar cross section (RCS) versus frequency is presented. Inspired by physical optics (PO), we propose using the non-linear ...least squares (NLS) method to capture the global trend of the conducting target's RCS and using Gaussian process regression (GPR) to automatically extrapolate the remaining local fluctuations. Experiments based on simulated and measured data are carried out to verify the proposed method. This method achieves a maximum root mean square error (RMSE) of only 0.444 dBsm on the simulated data of an electrically-large aircraft model, and 0.065 dBsm on the measured data of a combinatorial model. These results fully demonstrate its high extrapolation accuracy.
Interpolators-estimators that achieve zero training error-have attracted growing attention in machine learning, mainly because state-of-the art neural networks appear to be models of this type. In ...this paper, we study minimum ℓ2 norm ("ridgeless") interpolation least squares regression, focusing on the high-dimensional regime in which the number of unknown parameters p is of the same order as the number of samples n. We consider two different models for the feature distribution: a linear model, where the feature vectors xi ∈ Rp are obtained by applying a linear transform to a vector of i.i.d. entries, xi = Σ1/2 zi (with zi ∈ Rp); and a nonlinear model, where the feature vectors are obtained by passing the input through a random one-layer neural network, xi = φ(Wzi) (with zi ∈ Rd, W ∈ Rp×d a matrix of i.i.d. entries, and φ an activation function acting componentwise on Wzi). We recover-in a precise quantitative way-several phenomena that have been observed in large-scale neural networks and kernel machines, including the "double descent" behavior of the prediction risk, and the potential benefits of overparametrization.
It is well known that blowing agents (BAs) and polyol are essential components in polyurethane (PU) composition. Utilizing renewable sources in the material's formulation might reduce its ...environmental hazards while extending its possible engineering applications. In this study, the samples have been synthesized by using palm kernel oil‐based polyol (PKOP). Water and sodium hydrogen carbonate (SHB) have been used as BAs. Scanning electron microscopy (SEM) has shown that adding the mix of BAs is causing the cells' average size to increase up to 227% and have reduced the lamellae width by up to 2% in comparison with the reference sample. The water tests have illustrated that combining two parts per hundred polyol by weight (php) water and 25 php SHB to the sample has increased its water capacity up to 617%. However, the samples are only able to retain 6% of the absorbed water at the 7th day. It has been also found that porosity has affected the water uptake and all the samples are following Fick's diffusion law, and diffusion is correlated to the square root of time. Multivariable power least squares method (MPLSM) and moving least squares method (MLSM) have been applied to find the relation between tear resistance value and BAs ratio. It is found that both methods have a dominant variable compared to the other variables, but MLSM provided optimizable equation with better R2.
The effect of water and sodium hydrogen carbonate content on flexible polyurethane's microstructure, water and mechanical properties.
The probability density function (PDF) of wind speed is important in numerous wind energy applications. A large number of studies have been published in scientific literature related to renewable ...energies that propose the use of a variety of PDFs to describe wind speed frequency distributions. In this paper a review of these PDFs is carried out. The flexibility and usefulness of the PDFs in the description of different wind regimes (high frequencies of null winds, unimodal, bimodal, bitangential regimes, etc.) is analysed for a wide collection of models. Likewise, the methods that have been used to estimate the parameters on which these models depend are reviewed and the degree of complexity of the estimation is analysed in function of the model selected: these are the method of moments (MM), the maximum likelihood method (MLM) and the least squares method (LSM). In addition, a review is conducted of the statistical tests employed to see whether a sample of wind data comes from a population with a particular probability distribution. With the purpose of cataloguing the various PDFs, a comparison is made between them and the two parameter Weibull distribution (W.pdf), which has been the most widely used and accepted distribution in the specialised literature on wind energy and other renewable energy sources. This comparison is based on: (a) an analysis of the degree of fit of the continuous cumulative distribution functions (CDFs) for wind speed to the cumulative relative frequency histograms of hourly mean wind speeds recorded at weather stations located in the Canarian Archipelago; (b) an analysis of the degree of fit of the CDFs for wind power density to the cumulative relative frequency histograms of the cube of hourly mean wind speeds recorded at the aforementioned weather stations. The suitability of the distributions is judged from the coefficient of determination R2. Amongst the various conclusions obtained, it can be stated that the W.pdf presents a series of advantages with respect to the other PDFs analysed. However, the W.pdf cannot represent all the wind regimes encountered in nature such as, for example, those with high percentages of null wind speeds, bimodal distributions, etc. Therefore, its generalised use is not justified and it will be necessary to select the appropriate PDF for each wind regime in order to minimise errors in the estimation of the energy produced by a WECS (wind energy conversion system). In this sense, the extensive collection of PDFs proposed in this paper comprises a valuable catalogue.
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