Identification Of Model Parameters Research Articles

Identification of Photovoltaic Array Model Parameters by Robust Linear Regression Methods

The aim of this paper is to propose an approach for photovoltaic (PV) sources modeling based on robust least squares linear regression (LSR) parameter identification method. On the basis of experimental data of solar irradiance, cell temperature and voltage and currents at maximum power points for a given PV array, correlation functions among the considered quantities are defined. By implementing these functions in a Matlab® Simulink model, accurate I-V characteristics for the considered array are obtained, managing only the solar irradiance. The method is validated comparing the computed and the experimental maximum power points (MPPs). Its effectiveness is proven to be better with respect of parameter identification methods based on discrete approaches and standard LSR method

Modeling and Parameter Identification of Spark Plug Deposit Formation Mechanism for Different Fuel/Lubricant Combination

Abstract The identification of model parameters is complex and requires many approximations and laboratory investigations. Here, a novel approach to identifying these parameters is proposed, which uses experimental data, and a parameter estimation method to minimize a cost function made up of errors between the predicted and experimental deposit thickness. Parameters are determined for various ethanol–gasoline mixtures and lubricants. Simultaneously, the thermal and electrical properties of deposits are studied, and this information, coupled with the identified parameters, is utilized to infer potential deposit formation sources, mechanisms, and other elements that will influence the characteristics of the spark plug. It is found that the deposit formation model along with the identified parameters has the potential to reliably estimate the deposit growth with time as good correlations have been observed between measured and predicted spark plug deposit mass for all fuels and lubricants. The thermal and electrical conductivity of the spark plug deposit increases with a high ethanol fraction and high viscous oil, which results in increased chances of side sparking.

A dynamic parameter identification method for the 5-DOF hybrid robot based on sensitivity analysis

Purpose This paper aims to propose a dynamic parameter identification method based on sensitivity analysis for the 5-degree of freedom (DOF) hybrid robots, to solve the problems of too many identification parameters, complex model, difficult convergence of optimization algorithms and easy-to-fall into a locally optimal solution, and improve the efficiency and accuracy of dynamic parameter identification. Design/methodology/approach First, the dynamic parameter identification model of the 5-DOF hybrid robot was established based on the principle of virtual work. Then, the sensitivity of the parameters to be identified is analyzed by Sobol’s sensitivity method and verified by simulation. Finally, an identification strategy based on sensitivity analysis was designed, experiments were carried out on the real robot and the results were verified. Findings Compared with the traditional full-parameter identification method, the dynamic parameter identification method based on sensitivity analysis proposed in this paper converges faster when optimized using the genetic algorithm, and the identified dynamic model has higher prediction accuracy for joint drive forces and torques than the full-parameter identification models. Originality/value This work analyzes the sensitivity of the parameters to be identified in the dynamic parameter identification model for the first time. Then a parameter identification method is proposed based on the results of the sensitivity analysis, which can effectively reduce the parameters to be identified, simplify the identification model, accelerate the convergence of the optimization algorithm and improve the prediction accuracy of the identified model for the joint driving forces and torques.

Parameter sensitivity analysis of a multi-physics coupling aging model of lithium-ion batteries

A high-precision lithium-ion battery simulation model is the basis to ensure the reliability of battery management system (BMS), and obtaining the values of model parameters accurately and efficiently is the premise of its online application. In this work, the thermal and stress fields are coupled on the electrochemical model, and the various aging mechanisms under the influence of multi-physics coupling are considered. A multi-physics coupling aging model which can simulate the internal processes of battery is established. Based on up to 10,500 simulation cases, the sensitivities to the simulation results of terminal voltage and battery temperature of 42 model parameters are analyzed, including parameters related to multi-physics field and battery aging mechanisms. The model parameters are divided into three sensitivity levels: high, medium and low. The innovative aspects of this work include the extensive number of parameters studied, the numerous simulation cases, research objects related to multi-physics coupling and battery aging, and the concurrent parameter sensitivity analysis to both voltage and temperature simulation results. This work plays an important guiding role in the accurate and efficient identification of model parameters, and is of great significance for online application of model.

Rainfall distributional properties control hydrologic model parameter importance.

Study regionSemi-arid region of the Western United States of America in 16.6 km2 WS10 watershed using data from the highly instrumented Walnut Gulch Experimental Watershed managed by the USDA-Agricultural Research Services. Study focusHydrologic model parameters are generally designed to represent watershed physiographic properties. This study investigates the possible impact of climate forcing, particularly precipitation across a watershed, on model parameter identification – a topic that has historically received minimal attention. To address this gap, we conducted time-varying and time-aggregate sensitivity analysis of a physically based distributed hydrologic model on a heavily instrumented watershed under various observed and synthetic storm events to assess how the distributional properties of rainfall in space and time influence the importance model parameters. Particularly, we focused on a semi-arid watershed with an area larger than the typical convective storms in the region. New hydrologic insightResults show that there is a significant variation in parameter importance following rainfall spatial and temporal properties and that characterizing the relationship between parameter importance and rainfall properties is essential for identifying parameter values that adequately capture the watershed behaviors. We further showed that a power-law function linking parameter importance to precipitation properties can extrapolate results to other climate regimes. More analyses across different watersheds, climate forcing, and models are required to improve our understanding and strategies for parametrization and calibration of watershed models.

Parameter identification of solar photovoltaic models by multi strategy sine–cosine algorithm

AbstractAccurate modeling and parameter identification of photovoltaic (PV) cells is a difficult task due to the nonlinear characteristics of PV cells. The goal of this paper is to propose a multi strategy sine–cosine algorithm (SCA), named enhanced sine–cosine algorithm (ESCA), to evaluate nondirectly measurable parameters of PV cells. The ESCA introduces the concept of population average position to increase the population exploration ability, and at the same time introduces the personal destination agent mutation mechanism and competitive selection mechanism into SCA to provide more search directions for ESCA while ensuring the search accuracy and diversity maintenance. To prove that the proposed ESCA is the best choice for extracting nondirectly measurable parameters of PV cells, ESCA is evaluated by the single‐diode model, the double‐diode model, the three‐diode model, and the photovoltaic module model (PVM), and compared with eight existing popular methods. Experimental results show that ESCA outperforms similar methods in terms of diversity maintenance, high efficiency, and stability. In particular, the proposed ESCA method is less than the SCA by 0.081, 0.144, and 0.578 in the standard deviation statistics metrics of the three PVM models (PV‐PWP201, STM6‐40/36, and STP6‐120/36), respectively. Therefore, the proposed ESCA is an accurate and reliable method for parameter identification of PV cells.

Assessing Feed-Forward Backpropagation Artificial Neural Networks for Strain-Rate-Sensitive Mechanical Modeling.

The manufacturing processes and design of metal and alloy products can be performed over a wide range of strain rates and temperatures. To design and optimize these processes using computational mechanics tools, the selection and calibration of the constitutive models is critical. In the case of hazardous and explosive impact loads, it is not always possible to test material properties. For this purpose, this paper assesses the efficiency and the accuracy of different architectures of ANNs for the identification of the Johnson-Cook material model parameters. The implemented computational tool of an ANN-based parameter identification strategy provides adequate results in a range of strain rates required for general manufacturing and product design applications. Four ANN architectures are studied to find the most suitable configuration for a reduced amount of experimental data, particularly for cases where high-impact testing is constrained. The different ANN structures are evaluated based on the model's predictive capability, revealing that the perceptron-based network of 66 inputs and one hidden layer of 30 neurons provides the highest prediction accuracy of the effective flow stress-strain behavior of Ti64 alloy and three virtual materials.

Leveraging opposition-based learning for solar photovoltaic model parameter estimation with exponential distribution optimization algorithm

Given the multi-model and nonlinear characteristics of photovoltaic (PV) models, parameter extraction presents a challenging problem. This challenge is exacerbated by the propensity of conventional algorithms to get trapped in local optima due to the complex nature of the problem. Accurate parameter estimation, nonetheless, is crucial due to its significant impact on the PV system’s performance, influencing both current and energy production. While traditional methods have provided reasonable results for PV model variables, they often require extensive computational resources, which impacts precision and robustness and results in many fitness evaluations. To address this problem, this paper presents an improved algorithm for PV parameter extraction, leveraging the opposition-based exponential distribution optimizer (OBEDO). The OBEDO method, equipped with opposition-based learning, provides an enhanced exploration capability and efficient exploitation of the search space, helping to mitigate the risk of entrapment in local optima. The proposed OBEDO algorithm is rigorously verified against state-of-the-art algorithms across various PV models, including single-diode, double-diode, three-diode, and photovoltaic module models. Practical and statistical results reveal that the OBEDO performs better than other algorithms in estimating parameters, demonstrating superior convergence speed, reliability, and accuracy. Moreover, the performance of the proposed algorithm is assessed using several case studies, further reinforcing its effectiveness. Therefore, the OBEDO, with its advantages in terms of computational efficiency and robustness, emerges as a promising solution for photovoltaic model parameter identification, making a significant contribution to enhancing the performance of PV systems.

Model Parameter Identification as an Index of Fluid Responsiveness

This paper presents a method for identifying cardiovascular parameters in real-time by pre-generating synthetic data and training a support vector machine (SVM) classification algorithm. In this use case, the support vector machine classification can be described as a nearest-neighbour search to reduce computational load. This paper also investigates the effect of different time-varying elastance (TVE) terms on the robustness of the proposed matching algorithm. The method was tested using invasively measured data from a porcine subject. Matching was performed for a single heartbeat, using three different TVE terms, to cardiovascular parameters utilised by a lumped cardiovascular model. It was found that the proposed method can robustly identify cardiovascular parameters across certain TVE terms, indicating that there is promise for this method in clinical applications.

Research on Parameter Identification of Lithiumion Batteries Based on Improved SCE Algorithm

Evaluating the charging status of power batteries is very important in battery management systems, and the accuracy and parameter identification of battery models are crucial for it. Using DST and FUDS lithium-ion battery dynamic mode datasets for simulation verification, and comparing with particle swarm optimization algorithm, grey wolf algorithm, and genetic algorithm. The simulation results show that this method has advantages in recognition accuracy, with an average quadratic error of 0.0166V for parameter recognition. Compared with other optimization algorithms, it decreased by 7.8%, 8.3%, and 14.9% respectively.

A Dual-Feedback Adaptive Clone Selection Algorithm With Golden Sine Search for Parameter Identification of Photovoltaic Models

A Dual-Feedback Adaptive Clone Selection Algorithm With Golden Sine Search for Parameter Identification of Photovoltaic Models

Subject-Specific Parametric Identification of a Spine-Equivalent Beam Model for Lumbar Force Prediction in Sagittal Plane

Subject-Specific Parametric Identification of a Spine-Equivalent Beam Model for Lumbar Force Prediction in Sagittal Plane

Extended Linear Regression and Interior Point Optimization for Identification of Model Parameters of Fixed Wing UAVs

Extended Linear Regression and Interior Point Optimization for Identification of Model Parameters of Fixed Wing UAVs

Modeling Patient-specific Apnea-bradycardia Patterns in Preterm Newborn.

Preterm infants are particularly exposed to severe cardio-respiratory events, associating apnea with bradycardia and oxygen desaturation. A patient-specific and event-specific model-based approach is proposed in this work to analyze the acute heart rate response to apnea-bradycardia events in preterm newborn. A novel model integrating the main cardio-respiratory interactions which are specific to the neonatal period is proposed. An evolutionary algorithm is applied to estimate patient-specific model parameters from a database of 37 apnea-bradycardia episodes acquired from 10 preterm newborns. Unsupervised clustering (K-means) was applied to the identified parameters to define phenogroups of cardio-respiratory responses to apnea. A significant correspondence was observed between simulated and experimental heart rate series in all identifications (median RMSE = 8.85 bpm). Three clusters of parameters were found and were associated to three different pathophysiological dynamics related to apnea-bradycardia. The proposed method, based on patient and event-specific model parameter identification, provides a novel approach to characterize bradycardia dynamics in response to apnea, opening the way to the proposal of new personalized diagnosis and treatment possibilities in this particularly sensitive population.

Analysis of Electrical Aging Effects on AC High Frequency Motor Based on Exchange Market Algorithm Model Parameter Identification

Analysis of Electrical Aging Effects on AC High Frequency Motor Based on Exchange Market Algorithm Model Parameter Identification

An Improved Multi-time Scale Lithium-ion Battery Model Parameter Identification Algorithm Based on Discrete Wavelet Transform Method

An Improved Multi-time Scale Lithium-ion Battery Model Parameter Identification Algorithm Based on Discrete Wavelet Transform Method

Application research of interior space design elements based on image processing

In order to quantitatively analyze the application performance of elements in interior space design, a detection method of texture visual features of elements distribution images in interior space design based on image processing and texture edge contour features detection is proposed. A multi-resolution visual information collection model for detecting the texture visual characteristics of element distribution images in indoor space design is constructed, and a visual characteristic identification model for element application configuration in indoor space design is established. Multi-texture grid block area combination design method is adopted to realize the fusion of element application configuration visual images in indoor space design. Through the edge feature detection method of texture grid area, Establish a visual parameter identification model of element application configuration in interior space design, extract the visual information feature quantity of element application configuration in interior space design, adopt Harris corner feature matching method, realize distributed detection of texture edge contour features of element application configuration visual features in interior space design, and fuse detection of high-resolution texture information through combined filtering and collaborative filtering. The collection and optimization of visual features of element application configuration in interior space design are realized, and the distributed detection algorithm of texture edge contour features is adopted to improve the convergence level of detection of texture visual features of element distribution images in interior space design, and the application of elements in interior space design based on image processing is realized. The simulation results show that this method has good resolution and strong visual expression ability in extracting the texture visual features of element distribution images in interior space design, which improves the quantitative analysis ability of elements in interior space design.

Additive singular spectral model of a dendrochronological signal

Relevance. Allocation of structural components in a dendrochronological signal of annual rings of coniferous trees expands the possibilities of the bioindication method and allows obtaining information about changes in environmental conditions in the past for extended territories. Aim. Creation of an additive singular spectral model based on the frequency trigonometric components of the dendrochronological signal; reconstruction of changes in the total ozone content in the atmosphere in the past, affecting the level of ultraviolet radiation in the B range radiation. Objects. Time series of total ozone content in the atmosphere (data from 1932), width and density of annual rings (data from 1686–2004) on the example of the territory near the observatory in Arosa, Switzerland, time series of stable oxygen isotopes δ18Oc, France. Methods. Time series analysis (decomposition of a time series, identification of model parameters, prediction of a time series), statistical analysis (F-criterion, χ2 – Pearson criterion), experimental measurements of the percentage components of the wood of annual rings, correlation analysis, spectral singular analysis. Results. Decomposition of the dendrochronological signal of individual chronologies into trigonometric components in the Caterpillar program. Correlation analysis of the sensitivity of trees to atmospheric effects. Reconstruction of the ozone level in the stratosphere using the first trigonometric component of the dendrochronological signal. In cellulose, the first low-frequency component of the signal and the structural component of wood, a reliable response of trees to long-period fluctuations in the total ozone content is recorded. This allows assessment of the impact of the stress factor on conditions of exploitation of forest resources. The use of data on the width of the annual rings allows you to expand the territorial boundaries of the method.

A fractional-order modelling and parameter identification method via improved driving training-based optimization for piezoelectric nonlinear system

The accurate identification of the parameters of fractional-order systems is still a challenging problem. The purpose of this paper is to establish a fractional hysteresis model based on a Bouc-Wen differential equation and an improved driving training-based optimization (DTBO) parameter identification method to accurately characterize the nonlinear characteristics of the piezoelectric hysteresis system. In this paper, there are two contributions. On the one hand, an accurate fractional-order hysteresis model is proposed and its rate-dependent property is proved based on the definition of fractional calculus; on the other hand, an improved DTBO optimization method with enhanced performance is proposed to achieve excellent precision and robustness for the purpose of parameter identification. The unique features of the improved DTBO algorithm include an instructor-learner separation strategy, a new update mechanism of instructor position, and an improved phase of the learner group. The results show that the output of the model is consistent with that of the actual piezoelectric platform. Compared with the standard DTBO algorithm and Antlion Optimization algorithm, the improved DTBO algorithm has better performance. The novelty of this paper is that a new high-performance heuristic optimization method is proposed for the particular problem of the parameter identification of fractional-order hysteresis models. The proposed method holds promise as a good candidate in the field of hysteresis modelling and identification.

Battery parameter identification method of a battery module based on a multi-physical measurement system

The secondary utilization of retired electric vehicle batteries is beneficial for improving resource utilization efficiency. Capacity and internal resistance are battery parameters that can reflect the battery state. To identify the parameters of a single battery in a battery module, it is usually necessary to disassemble the battery module. The process is complex, time-consuming, and unsafe. In this paper, a battery parameter identification method without disassembling the battery module is developed based on a multi-physical measurement system. First, a multi-physical electrical-thermal-spatial measurement system is constructed to measure the physical parameters of the battery module during charging and discharging. Then, the electrical model and the thermal model are developed. To obtain the capacity and internal resistance of each cell within the battery module, a battery parameter identification model is established with temperature and total battery current as input parameters and battery capacity and internal resistance as output parameters. Batteries with different capacities and internal resistances are connected in series and parallel to simulate retired battery modules for electric vehicles. The battery parameters are identified using the method presented in this paper. According to the experiments, the relative errors of parameter identification for battery capacity and internal resistance are both within 9 %. This method can improve the efficiency of the battery screening process as well as the reliability of the battery system.