Parameter Identification Method Research Articles

GLaSDI: Parametric physics-informed greedy latent space dynamics identification

A parametric adaptive physics-informed greedy Latent Space Dynamics Identification (gLaSDI) method is proposed for accurate, efficient, and robust data-driven reduced-order modeling of high-dimensional nonlinear dynamical systems. In the proposed gLaSDI framework, an autoencoder discovers intrinsic nonlinear latent representations of high-dimensional data, while dynamics identification (DI) models capture local latent-space dynamics. An interactive training algorithm is adopted for the autoencoder and local DI models, which enables identification of simple latent-space dynamics and enhances accuracy and efficiency of data-driven reduced-order modeling. To maximize and accelerate the exploration of the parameter space for the optimal model performance, an adaptive greedy sampling algorithm integrated with a physics-informed residual-based error indicator and random-subset evaluation is introduced to search for the optimal training samples on the fly. Further, to exploit local latent-space dynamics captured by the local DI models for an improved modeling accuracy with a minimum number of local DI models in the parameter space, a k-nearest neighbor convex interpolation scheme is employed. The effectiveness of the proposed framework is demonstrated by modeling various nonlinear dynamical problems, including Burgers equations, nonlinear heat conduction, and radial advection. The proposed adaptive greedy sampling outperforms the conventional predefined uniform sampling in terms of accuracy. Compared with the high-fidelity models, gLaSDI achieves 17 to 2,658× speed-up with 1 to 5% relative errors.

Identification of the Constitutive Parameters of Viscosity and the Prediction of the Cutting Force of S32760 Duplex Stainless Steel under a High Strain Rate

The mechanical properties of S32760 duplex stainless steel under dynamic loading conditions at high strain rates are significantly different from those under quasi-static conditions. As a result of large strain, high strain rate, and high temperature, the analysis of the cutting process needs to factor in the influence of the viscous behavior of the material on the plastic deformation process. Based on the viscous effect of the two phases and the mixing rule, a mechanical threshold stress (MTS) constitutive model of S32760 duplex stainless steel considering the viscous effect is established to analyze the effect of strain rate on flow stress. An inverse identification method of the constitutive parameters based on Oxley's theory is proposed. The constitutive parameters of S32760 duplex stainless steel were reversely modified using an equal shear zone model and an orthogonal cutting experiment. The results show that the viscosity of the austenite phase was greater than that of the ferrite phase, and the strain rate had the greatest influence on the viscosity effect in the constitutive model. The prediction error of the constitutive model constructed in this manuscript was less than 4%, which had high accuracy.

Parameter identification of contact model for tank saddle ring system based on genetic algorithm and BP neural network

This paper is devoted to present an experimental identification method of tank saddle ring system parameters based on BP neural network algorithm and genetic algorithm. The contact parameters in mechanical system cannot be obtained directly through measurement, and it is inevitable that there will be errors between the parameters estimated by engineering experience and the real value. In this paper, a 6-degree-of-freedom swing experimental device of tank turret is built, the excitation source is 1Hz sine function, the contact stiffness and contact damping coefficient are identified by constructing error functions based on simulation results and data of four laser displacement sensors distributed around the saddle ring, by the combination of BP neural network and genetic algorithm. Then, the sensor data in the test results and simulation results are compared to further verify the accuracy of the turret model and parameter identification method.

Thermal-Coupled Single Particle Modeling and Multi-Objective Stepwise Parameter Identification of Lithium-Ion Batteries Over Different Temperatures

The establishment of electrochemical-thermal coupling model for a lithium-ion battery (LIB) is an important issue in developing an appropriate thermal management system of LIB packs. In this paper, a novel thermal-coupled single particle model with few parameters is first developed to promote battery parameter identification at various temperatures. Then, after collecting the experimental profiles of battery voltage, current, and temperature, a multi-objective stepwise identification scheme based on genetic algorithm is proposed to identify the classified parameters of LIB at different temperatures. Finally, the proposed battery model and the stepwise parameter identification are validated in terms of the simulations and experiments. The results demonstrate that this proposed battery model and parameter identification method can not only describe inherent electrochemical and thermal characteristics of the battery, but also identify the battery electrochemical states with high precision, which provides a strong foundation for the development and implementation of battery thermal management system.

A Generalized Per-Phase Equivalent Circuit Model of the PMSM With Predictable Core Loss

Equivalent circuit model (ECM) of the permanent magnet (PM) synchronous motor (PMSM) lays the foundation of the motor analysis, design, optimization, and control. However, the most widely used ECM of the PMSM ignores the core loss, which accounts for a considerable part of the electromagnetic loss and may exceed the copper loss in the medium and high-speed ranges. Previous researches of ECMs with predictable core loss are hard to satisfy the prediction precision over a broad range of speed and current. Therefore, a generalized per-phase ECM of the PMSM with predictable core loss is proposed. The proposed ECM topology and parameter identification method can effectively enhance the prediction accuracy of both the no-load and load core loss, and each component of the core loss in terms of the hysteresis, eddy current, and anomalous loss can be analyzed separately. Apart from the core loss, the proposed ECM has better precision in predicting the motor's output performance, such as mechanical characteristics, compared with traditional ECMs. The merits of the proposed ECM have been experimentally validated on the prototype of a PM transverse flux synchronous motor. This proposed ECM can be promotionally employed in other control strategies, such as model predictive control, to improve the PMSM performance.

Parameter identification of DC–DC converter based on dendrite net under fluctuating input voltages

AbstractThe traditional identification methods of direct current‐to‐direct current (DC–DC) converters usually consider the common situation where the training and testing sets have the same input voltage. In real‐world applications, the actual input voltage may fluctuate beyond its original value, thus negatively affecting the parameter identification accuracy. Therefore, this paper presents a parameter identification method under fluctuating input voltages. Firstly, to increase the identification accuracy, the paper proposes the coefficient of variation method to extract key features from all time‐ and frequency‐domain features as training data. Then, for fluctuating input voltages, the paper adds the input voltages into the training data to build the parameter identification network based on Dendrite Net. When using the established network to identify component parameters in practical applications, the actual input voltage may change to an unknown value because of irregular fluctuation. Hence, the actual input voltage needs to be identified before identifying the component parameters of a DC–DC converter. Finally, the paper builds the input voltage identification network. By combining the identification results of the actual input voltage, the parameter identification under fluctuating input voltages can be completed. The simulation and hardware experiments results validate the practicability and effectiveness of the proposed method.

Modeling and estimating the model adequacy in muscle tissue electrical stimulator designing

The research object is the mathematical modeling of human skeletal muscle electrical stimulation characteristics during therapy. The subject of research is mathematical models of electrical stimulation characteristics that relate muscle contraction amplitude to the amplitude, the rate of stimulating effects, and other parameters. The research purpose is to study such models, develop an algorithm for their correction and a method for estimating their adequacy. The methods used: mathematical modeling methods, methods of structural and parametric identification of models, optimization methods, methods for estimating the adequacy of models, and interval methods. The results: an algorithm for muscle electrical stimulation characteristics mathematical models correcting during several sessions in case of their change is proposed; a method for estimating the model adequacy area in the external variables space in order to control its adequacy is proposed; using the interval mathematics methods to construct the adequacy actual area is justified; an interval estimating of the error in modeling a certain output characteristic is introduced, that, in the case of characteristic monotonicity, allows checking the adequacy maintaining by checking some inequalities. The results can be used in the design of electrical stimulators and for determining the electrical stimulation effects of individual parameters during one session or a series of sessions. The scientific originality: the interval mathematics methods for approximating the mathematical model adequacy area in a hyperparallepiped and checking for nesting in the target area in the external parameters space in order to control the adequacy during the model correction in electrical stimulation is proposed and justified.

A parameter identification and state of charge estimation method of lithium-ion battery considering temperature bias

To eliminate the impact of inaccurate initial parameter value on the parameter identification results of lithium-ion battery (LIB) model, a method for parameter identification of LIB combining Matlab and 1stOpt is proposed, fully utilizing the powerful global optimization ability of 1stOpt to obtain accurate initial parameter value. Moreover, this method can also efficiently heighten the precision of parameter identification results. The hybrid pulse power characteristic (HPPC) experiment is carried out on a LIB in the laboratory to obtain the relationship among state of charge (SOC), open circuit voltage (OCV), and ambient temperature, and the data required for parameter identification. To enhance the accuracy of SOC estimation, based on the obtained high-precision identification parameters, a dual-polarization dynamic thermal model of coupling temperature and SOC of the LIB is established. We find that the battery temperature tested is inconsistent with the ambient temperature, which causes inaccurate temperature input of the battery model, thus affecting the accuracy of the battery model. In this work, a particle swarm optimization (PSO) algorithm is introduced to obtain exact temperature differences to rectify the temperature input of the battery model. Then, the simulated terminal voltage of the battery is compared and validated against the experimental data. It is obtained that the maximum and minimum average errors considering temperature bias are only 0.962 % and 0.0075 %, respectively, while the maximum and minimum average errors without temperature bias are 3.622 % and 0.109 %, respectively, which commendably prove the accuracy of the established model. Finally, the extended Kalman filter (EKF) is adopted to estimate the SOC under the dynamic stress test (DST), and the maximum error is less than 0.8 %. The results strongly show that the established model in this work still has high accuracy in a wide temperature range. The results provide a theoretical reference for the related research on parameter identification and modeling of batteries.

Adaptive modal identification of honeycomb thin-walled composite structures with pit defects under thermal modal testing using variational mode decomposition technique based on digital image correlation

The active imaging blue light high-speed three-dimensional digital image correlation (3D-DIC) system was combined with a transient aerodynamic heating system and random pulse excitation technology to establish a high-frequency thermal vibration optical test system capable of multi-temperature zone testing at 900 °C to accurately obtain the thermal modal parameters of thin-walled structures. The full-field temperature distribution of a novel honeycomb thin-walled composite structure (HTWCS) with pit defects under non-uniform temperature was accurately simulated through reinforcement learning. The first six modal frequencies of honeycomb thin-walled composite structure with pit defects at room temperature increased compared with the non-destructive honeycomb thin-walled composite structure, and the third and fourth modal shapes interchanged. An adaptive modal parameter identification method for full-field three-dimensional vibration measurements under high-temperature environments was developed by combining the successive multivariate variational mode decomposition (VMD) with the modal superposition method. This method can effectively identify close modes. The area ratio-based approach was used to evaluate the damping of the measured response. The finite element model (FEM) of the HTWCS with pit defects at different temperatures was updated using a multi-state step-by-step model updating method and the temperature-dependent material properties were established. The results showed that the introduction of a damping ratio improved the accuracy of the updated FEM of HTWCS with pit defects. The proposed technique is anticipated to provide an effective method for high-frequency thermal vibration optical measurement and modal identification of thin-walled structures under multi-temperature regions.

Radial Basis Function Based Meta-Heuristic Algorithms for Parameter Extraction of Photovoltaic Cell

Accurate parameter estimation of photovoltaic (PV) cells is crucial for establishing a reliable cell model. Based on this, a series of studies on PV cells can be conducted more effectively to improve power output; an accurate model is also crucial for the operation and control of PV systems. However, due to the high nonlinearity of the cell and insufficient measured current and voltage data, traditional PV parameter identification methods are difficult to solve this problem. This article proposes a parameter identification method for PV cell models based on the radial basis function (RBF). Firstly, RBF is used to de-noise and predict the data to solve the current problems in the parameter identification field of noise data and insufficient data. Then, eight prominent meta-heuristic algorithms (MhAs) are used to identify the single-diode model (SDM), double-diode model (DDM), and three-diode model (TDM) parameters of PV cells. By comparing the identification accuracy of the three models in two datasets in detail, the final results show that this method can effectively achieve parameter extraction, with advantages such as high extraction accuracy and stability, greatly improving the accuracy and reliability of parameter identification. Especially in the TDM, the I-V data and P-V data obtained from the PV model established through the identified parameters have very high fitting accuracy with the measured I-V and P-V data, reaching 99.58% and 99.65%, respectively. The research can effectively solve the low accuracy problem caused by insufficient data and noise data in the traditional method of identifying PV cells and can greatly improve the accuracy of PV cell modeling. It is of great significance for the operation and control of PV systems.

A method of lithium-ion battery failure diagnosis based on parameter boundaries of heterogeneous multi-physics aging model

Lithium-ion batteries experience capacity fading during long-term operation, and they need to be replaced when reach a certain aging degree. However, it is difficult to measure a complete charge/discharge curve to obtain the capacity whether the battery is used for vehicle or energy storage. The aging state of the battery can only be judged according to the partial data of voltage and current based on the data-driven method, which is not reliable enough. In this work, a new method of battery failure diagnosis in terms of capacity fading is proposed based on the heterogeneous multi-physics aging model of lithium-ion batteries. The key parameters are obtained by parameter identification method, and the parameter boundaries when the battery is on the verge of failure are obtained by model driven method. In practical application of batteries, the failure of batteries can be diagnosed online by monitoring the key parameters, so that an early warning signal can be provided when the battery reaches the end of its life, which is of great significance to ensure that the battery can meet the performance and safety requirements.

GWO-Based Multi-Stage Algorithm for PMDC Motor Parameter Estimation.

During the design of a wheeled mobile robot, the problem of the proper selection of the parameters of its motor controllers was encountered. Knowing the parameters of the robot's Permanent Magnet Direct Current (PMDC) motors, precise tuning of the controllers can be performed, which then results in improved robot dynamics. Among many methods of parametric model identification, optimization-based techniques, particularly genetic algorithms, have gained more and more interest recently. The articles on this topic present the results of parameter identification, but they do not refer to the search ranges for individual parameters. With too wide a range, genetic algorithms do not find solutions or are time-inefficient. This article introduces a method for determining the parameters of a PMDC motor. The proposed method performs an initial estimation of the range of searched parameters to shorten the estimation time of the bioinspired optimization algorithm.

Physical modeling for digital twin of Continuous Damping Control damper

Continuous Damping Control (CDC) damper is a kind of active damper with adjustable damping coefficients, which has a broad application prospect for vibration attenuation in robots and manufacturing systems. To develop a digital twin of CDC damper for monitoring its working conditions, it is necessary to develop a physical model describing the characteristics of CDC damper. However, the existing physical models of CDC damper have some disadvantages such as expensive computing costs and low accuracy, which make it difficult to apply them in the digital twin. In order to solve these issues, this paper proposes a hybrid model which can accurately describe the physical characteristics of CDC damper by combining a lumped parameter model and a neural network model. First, the physical characteristics of a CDC damper are measured on a test rig under harmonic excitations and random excitations. Then the modeling and parameter identification methods for hybrid models are proposed to obtain model parameters. Finally, the output force of the CDC damper under random excitation is calculated using the hybrid model, and the accuracy of the hybrid model is validated after comparing the calculated results with the measured results. The proposed hybrid model has high accuracy and low computational cost, and is applicable to developing digital twins of CDC dampers which require high calculation accuracy and efficiency.

Research on Dynamic Modeling and Parameter Identification of the Grid-Connected PV Power Generation System

With the increasing proportion of renewable energy in the new power system, the grid-connected capacity of photovoltaic (PV) units shows an obvious upward trend, but its dynamic behavior under different penetration rates significantly affects the transient stability of the power system, so it is crucial to establish a dynamic model that meets the actual working conditions and select a suitable parameter identification method. Therefore, in this paper, based on the electromechanical transient characteristics of the grid-connected PV power generation system, the corresponding dynamic discrete equivalent model is established, and the simulation platform of the grid-connected PV power generation system is built in MATLAB/Simulink to study the adaptability of the dynamic discrete equivalent model of the grid-connected PV power generation system from the single and multiple scenarios using the ordinary least squares (OLS) and bat algorithm (BA) while comparing the generalization ability of the parameters identified by the two methods to the model. The simulation results show that the generalization ability of the parameters identified by the OLS and BA for the model in the single scenario is better, indicating that the model has good adaptability; the generalization ability of a set of general parameters identified by the BA for the model in the multiple scenarios is better than that of the OLS, indicating that the parameters identified by the BA have better adaptability. In conclusion, the dynamic discrete equivalent model of the grid-connected PV power generation system proposed in this paper can accurately reflect the dynamic characteristics of the grid-connected PV power generation system, and the parameters identified by the BA are more generalized than the OLS.

Identification Method of Source Term Parameters of Nuclear Explosion Based on GA and PSO for Lagrange-Gaussian Puff Model

Many well-established models exist for predicting the dispersion of radioactive particles that will be generated in the surrounding environment after a nuclear weapon explosion. However, without exception, almost all models rely on accurate source term parameters, such as DELFIC, DNAF-1, and so on. Unlike nuclear experiments, accurate source term parameters are often not available once a nuclear weapon is used in a real nuclear strike. To address the problems of unclear source term parameters and meteorological conditions during nuclear weapon explosions and the complexity of the identification process, this article proposes a nuclear weapon source term parameter identification method based on a genetic algorithm (GA) and a particle swarm optimization algorithm (PSO) by combining real-time monitoring data. The results show that both the PSO and the GA are able to identify the source term parameters satisfactorily after optimization, and the prediction accuracy of their main source term parameters is above 98%. When the maximum number of iterations and population size of the PSO and GA were the same, the running time and optimization accuracy of the PSO were better than those of the GA. This study enriches the theory and method of radioactive particle dispersion prediction after a nuclear weapon explosion and is of great significance to the study of environmental radioactive particles.

An extended SFR model based fast frequency security assessment method for power systems

The large-scale integration of renewable energy and the increasing access to power electronic equipment lead to the low inertia of power grid, which raises severe challenges to frequency security. The low inertia makes power system more sensible to power disturbances, and therefore, evaluating frequency stability is crucial. To address this issue, a fast frequency security assessment method for power grid based on the extended SFR (system frequency response) model is proposed. In this method, an extended SFR model is established based on the characteristics of future new power systems, which are comprised of multiple frequency response sources, including induction motor load, renewable energy, and other asynchronous resources. To obtain the parameters of the proposed model, the principle and applicability of model parameter aggregation methods are explained. Considering the difficulty of obtaining all the required information in real-world situations, the parameters are obtained by parameter identification method through optimization algorithms. Finally, the effectiveness of the proposed extended SFR model is verified in the WSCC-9 bus system and the parameter identification method is validated in a regional power grid. The calculation time is less than 0.03s and the accuracy is more precise than conventional SFR. The results demonstrate that the proposed extended SFR model and parameter identification method show characteristics of both rapidity and accuracy in judging the security of power systems.

Parameter Identification of Model for Piezoelectric Actuators.

Piezoelectric actuators are widely used in high-precision positioning systems. The nonlinear characteristics of piezoelectric actuators, such as multi-valued mapping and frequency-dependent hysteresis, severely limit the advancement of the positioning system's accuracy. Therefore, a particle swarm genetic hybrid parameter identification method is proposed by combining the directivity of the particle swarm optimization algorithm and the genetic random characteristics of the genetic algorithm. Thus, the global search and optimization abilities of the parameter identification approach are improved, and the problems, including the genetic algorithm's poor local search capability and the particle swarm optimization algorithm's ease of falling into local optimal solutions, are resolved. The nonlinear hysteretic model of piezoelectric actuators is established based on the hybrid parameter identification algorithm proposed in this paper. The output of the model of the piezoelectric actuator is in accordance with the real output obtained from the experiments, and the root mean square error is only 0.029423 μm. The experimental and simulation results show that the model of piezoelectric actuators established by the proposed identification method can describe the multi-valued mapping and frequency-dependent nonlinear hysteresis characteristics of piezoelectric actuators.

Data-Driven Fault Detection of AUV Rudder System: A Mixture Model Approach

Based on data-driven and mixed models, this study proposes a fault detection method for autonomous underwater vehicle (AUV) rudder systems. The proposed method can effectively detect faults in the absence of angle feedback from the rudder. Considering the parameter uncertainty of the AUV motion model resulting from the dynamics analysis method, we present a parameter identification method based on the recurrent neural network (RNN). Prior to identification, singular value decomposition (SVD) was chosen to denoise the original sensor data as the data pretreatment step. The proposed method provides more accurate predictions than recursive least squares (RLSs) and a single RNN. In order to reduce the influence of sensor parameter errors and prediction model errors, the adaptive threshold is mentioned as a method for analyzing prediction errors. In the meantime, the results of the threshold analysis were combined with the qualitative force analysis to determine the rudder system’s fault diagnosis and location. Experiments conducted at sea demonstrate the feasibility and effectiveness of the proposed method.

Vibration Parameters Identification of Rotating Blades Based on Blade Tip-Timing Sensor Waveforms

Blade tip-timing is one of the most widely used non-contact measurement methods for rotating blade vibration. The classic blade tip-timing method is to identify the vibration parameters of the blade based on the time of blade tip arrival to the tip-timing sensor installed on the casing. Since the vibration measurement of blade tip-timing is a serious under-sampling method, the blade resonance frequency is usually higher than the sampling frequency of the BTT data acquisition system, resulting in a large identification error of blade vibration parameters. In view of the above shortcomings, a blade vibration parameters identification method is proposed based on the mapping matching between the static waveform of the sensor and the dynamic waveform of the blade tip-timing. The modulation effects of different vibration displacements, velocities and vibration amplitudes on the tip-timing waveform of the sensor are analyzed. The vibration displacements and velocities of the blade tip passing through the sensor are obtained by the waveform mapping method, and then the vibration parameters of the blade are identified. The influence of the number of blade tip-timing sensors on the identification accuracy of the blade vibration parameters identification algorithm is observed based on the waveform mapping theory. The identification accuracy of waveform mapping method is compared with other methods. The results show that compared with the under-sampled time of arrival (TOA) data of classic BTT applications, the modulation of the sensor waveform output by blade vibration can provide additional data, and the waveform mapping method can not only reduce the number of sensors but also improve the identification accuracy of blade vibration parameters. The waveform mapping method has higher identification accuracy of blade vibration parameters compared with the waveform method in the reference.

L-SHADE with parameter decomposition for photovoltaic modules parameter identification under different temperature and irradiance

The performance of photovoltaic (PV) system relies on the accurate determination of unknown parameters in PV models, such as photo-generated current, diode current, and resistance. These unknown parameters may vary with different temperature and irradiance conditions, which greatly increases the difficulty of identifying these unknown parameters. Although some parameter identification methods have been proposed to solve such a problem, the accuracy and reliability of solutions obtained by these methods suffers from great challenges when temperature and irradiance are changing. In this paper, a simple and effective approach called success-history adaptation differential evolution with linear population size reduction (L-SHADE) and decomposition referred as (L-SHADED), is presented to accurately and reliably identify the unknown parameters of PV models under different temperature and irradiance. In L-SHADED, firstly, an unknown parameters decomposition technique is used to reduce the complexity of the problem. Then an advanced evolutionary algorithm L-SHADE is employed to identify the optimal unknown parameter values. The performance of proposed L-SHADED has been investigated by testing two single-diode-based PV modules (multi-crystalline KC200GT and mono-crystalline SM55) under different temperature and irradiance. The experimental comparison results demonstrate that proposed L-SHADED is almost 10 times smaller in RMSE than other methods. Furthermore, the coefficient of determination of L-SHADED reaches 1.0 in almost all conditions, which indicates that the parameters identified by L-SHADED are quite accurate.