Parameter Identification Method Research Articles

Variable Pressure Difference Control Method for Chilled Water System Based on the Identification of the Most Unfavorable Thermodynamic Loop

A variable pressure differential fuzzy control method is proposed based on the online identification method for key parameters and the fuzzy subset inference fuzzy control method of the chilled water system network model. Firstly, a phase plane fuzzy identification method is proposed for the most unfavorable thermal loop. The study focuses on analyzing the trend of room temperature deviation and deviation change in different quadrants in the phase plane. Furthermore, we establish a chilled water pipe network model that recalculates flow variation in both the main pipe and each branch pipe section to eliminate the most unfavorable thermal loop. Finally, the test platform for the fan coil variable flow air conditioning water system was designed and constructed to meet the requirements of energy-saving regulation. Additionally, the network monitoring system for the test platform was completed. The calibration and debugging results demonstrate that the monitoring error is within ±5.0%, ensuring precise control of room temperature at the end of the branch within ±0.5 °C. Results demonstrate that our novel method exhibits superior stability in room temperature control compared to traditional linear variable pressure differential set point controls while achieving energy saving ranging from 4.7% to 6.5%.

Parameters online identification‐based data‐driven backstepping control of hypersonic vehicles

SummaryThe control problem of the hypersonic vehicles is studied in this article. A new control approach is presented. This approach consists of a data‐driven dynamic model established by multiple neural networks, an online identification method for system parameters, and a basic backstepping controller. The implementation of this approach requires a dynamic model and system parameters including the moment of inertia and aerodynamic parameters of the hypersonic vehicles. The parameter identification problem is regarded as a dynamic optimization process. The loss function is designed by the Lagrange criterion, and its constraints are determined by the physical and the numerical values. In the case of model mutation, the system parameters identified online are used as the nominal values of the output of the neural network in the data‐driven model to adjust the controller through its gradient descent. Simulation comparisons are given to show the effectiveness of the proposed data‐driven approach.

A simulation-driven prediction model for state of charge estimation of electric vehicle lithium battery

Accurately predicting the state of charge (SOC) of lithium-ion batteries in electric vehicles is crucial for ensuring their stable operation. However, the component values related to SOC in the circuit typically require estimation through parameter identification. This paper proposes a three-stage method for estimating the SOC of lithium batteries in electric vehicles. Firstly, the parameters of the constructed second-order RC circuit are identified using the Forgetting Factor Recursive Least Squares (FFRLS) method. Secondly, an innovative approach is employed to construct a battery simulation model using modal-data fusion method. Finally, the predicted values of the simulation model are corrected using the unscented Kalman filter (UKF). Validation through datasets demonstrates the high precision of this method in parameter identification. Moreover, in the comparison of SOC prediction corrections with Particle Filter (PF), Extended Kalman Filter (EKF), and the proposed UKF on simulated prediction data and experimental test data. The proposed method achieves the lowest root mean square error (RMSE) of 0.0025 for simulation prediction data and 0.0186 for experimental test data. It also maintained its error within 5 % on actual data.

Accurate Parameter Identification Method for Coupled Sub/Super-Synchronous Oscillations for High Penetration Wind Power Systems

As the penetration of renewable energy increases to a large scale and power electronic devices become widespread, power systems are becoming prone to synchronous oscillations (SO). This event has a major impact on the stability of the power grid. The recent research has been mainly concentrated on identifying the parameters of sub-synchronous oscillation. Sub/Super synchronous oscillations (Sub/Sup-SO) simultaneously occur, increasing the difficulty in accurately identify the parameters of SO. This work presents a novel method for parameter identification that effectively handles the Sub/Sup-SO components by utilizing the Rife-Vincent window and discrete Fourier transform (DFT) simultaneously. To mitigate the impact of spectral leakage and the fence effect of DFT, we integrate the tri-spectral interpolation algorithm with the Rife–Vincent window. We use the instantaneous data of the phasor measurement unit (PMU) to identify Sub/Sup-SO-related parameters (Sub/Sup-SO damping ratio, frequency, amplitude and phase). First, the spectrum of the Sub/Sup-SO signals is analyzed after incorporating the Rife-Vincent window, and the characteristics of the Sub/Sup-SO signal are determined. Then, the signal spectrum is identified using a three-point interpolation algorithm, and the damping ratio, amplitude, frequency, and phase of the Sub/Sup-SO signals are obtained. In addition, we consider the identification accuracy of the algorithm under various complex conditions, such as the effect of Sub/Sup-SO parameter variations on parameter identification in the presence of a non-nominal frequency and noise. The proposed algorithm accurately identifies the parameters of multiple Sub/Sup-SO components and two Sub-SO components that are in close proximity. Testing with synthetic and real data demonstrates that the proposed algorithm outperforms existing methods in terms of identification accuracy, identification bandwidth, and adaptability.

Merging experiment data and simulation data for parameter identification of shaft seal

A parameter identification method which merges experimentally monitored signals and physics-based simulation data is proposed, targeting the identification tasks in shaft seals which are challenging due to the high-dimensional parameter space. Features were extracted from the monitored acoustic emission signals following a proposed cross-timescale analysis routine, and the simulation data were augmented using Kriging surrogate model to obtain a dataset with stratified fidelity. Then, a transferable architecture of convolutional neural network modified for periodical data was proposed, with which part of the parameters trained by simulation data were reserved when training model using acoustic emission data acquired in experiments. Cross validation shows that transfer learning can effectively improve the performance, provided data augmentation and proper transfer mode. In conclusion, the study provides an effective parameter identification method which merges the simulation data which carry the physical knowledge and the experiment data which carry directly monitored results.

A Review of Cross-Scale Theoretical Contact Models for Bolted Joints Interfaces

Bolted joints structures are critical fastening components widely used in mechanical equipment. Under long-term loading conditions, the bolted joints interface generates strong nonlinearities within the system. The nonlinear stiffness inside the bolt leads to changes in the stiffness of the whole system. This affects the dynamic characteristics of the whole system. It brings challenges and difficulties to the performance prediction and reliability assessment of the equipment. A cross-scale theoretical model study based on the microscopic contact mechanism can provide a more comprehensive understanding and cognition of the degradation behavior of bolted joints interfaces. The current development status and deformation process of asperity models are summarized. The research progress of statistical summation model and contact fractal model based on microscopic contact mechanism is analyzed. The experimental methods for parameter identification of connection interfaces are reviewed. The study of numerical modelling of bolted joints structures from the surface contact mechanism is briefly described. Future research directions for cross-scale modelling of bolted joints structures are outlined.

Robust noise-correction recursive least square method for parameter identification of equivalent circuit model in battery management system using Bayes’ theorem-based preprocessing technique

Robust noise-correction recursive least square method for parameter identification of equivalent circuit model in battery management system using Bayes’ theorem-based preprocessing technique

Parameter identification procedure for hysteretic shear-resistant properties of beech wood dowels

To evaluate the shear-resistant behavior of wooden dowels used in Blockhaus shear walls under cyclic load, 19 specimens under ten groups of conditions were prepared and tested. The failure modes, hysteresis curves, mechanical properties, stiffness degradations, and energy dissipation capacities of the specimens were studied. The test results showed that with the increase in the number of dowels, the initial stiffness and peak load of the specimens increased greatly. The diameter of the dowels had little influence on the mechanical properties of the specimens. Furthermore, the test findings demonstrated that the pretension load between the walls greatly enhanced the initial stiffness and energy dissipation capacity of specimens. A simplified finite element model was established in Opensees. Considering the effect of material variability, the parameters of single dowel shear spring and friction spring were identified by Genetic Algorithm with modified objective function in Matlab. The identified parameters were applied to the finite element model of the multi-dowel specimens. The simulation results were in good agreement with the test results, and the validity of the numerical model and parameter identification method was verified.

Performance evaluation and optimization of the cascade refrigeration system based on the digital twin model

Cascade refrigeration systems (CRSs) have become the priority choice in low-temperature applications, causing severe energy waste due to the lack of practical control strategies. Currently, refrigeration simulations are mainly based on the simplified thermodynamic or Artificial Intelligence (AI) model. However, the former is insufficiently accurate, while the latter is of unclear thermodynamic significance. By connecting the physical and digital space, the digital twin technology provides exciting opportunities for combining thermodynamic and AI methods. In this paper, an integrated digital twin model for the CRS is innovatively proposed based on the modified semi-empirical model of the twin-screw refrigeration compressor. As a major update, the identification and update method of characteristic parameters is developed based on real-time operating data to ensure performance consistency between digital and physical twins. By comparing with the simplified model, results show that the digital twin model can improve prediction accuracy by 7.42%–23.62%. Founded on the proposed model, the thermodynamic performance of the CRS is evaluated. To facilitate on-site applications, the definition of flat intermediate temperature is given, which is fitted to a control correlation with several easily observed variables as the inputs. Taking a food factory as a case study, simulation results reveal that the optimum intermediate temperature should be significantly lower than the original, and the coefficient of performance (COP) has an average improvement potential of 9.1%. By deploying the optimum strategy under the on-site condition, a superior energy-saving rate of up to 13.1% is attained according to test reports.

Model predictive control for Vienna rectifier with constant frequency based on inductance parameters online identification

When the finite control set model predictive control (FCS-MPC) is applied to the three-level converter, there are problems such as large current harmonics, high requirements for the computing efficiency of the microcontroller, complex multi-objective optimization and limited output vector switching. In addition,the mismatch of inductance parameter may directly affect the observation accuracy of FCS-MPC. To solve the above problem,a double vector model predictive control strategy with constant frequency strategy based on the inductance parameters on-line identification is presented.First, a single objective cost function based on the direct power is constructed by optimizing the redundant vector which is selected to balance the neutral-point potential, the design of weighting factor is avoided.At the same time, in order to effectively reduce the grid current ripple under single vector modulation, space vector pulse width modulation (SVPWM) is realized by combining with zero vector control mode. And, a parameter identification method based on model reference adaptive system(MARS) is proposed to overcome the effect of model parameter mismatch.Finally, the results show that the proposed F-MPCCF has good steady-state and dynamic performance from the static, transient and neutral-point potential control.

A novel dynamic parameter identification method for the multi-degrees-of-freedom series direct drive manipulator

Accurate dynamic parameters are essential for precise control of model-based manipulators. Due to the direct connection between the joint axis and the motor rotor of the direct drive manipulator, using additional joints would increase the disturbance of the system, leading to substantial trajectory tracking errors, which could potentially harm the manipulator if the tracking error exceeds the limited position of joints, and it also curtails the comprehensive parameter excitation and affects the identification accuracy. Restricting joint movement to a narrow area can prevent this injury, but it also curtails the comprehensive parameter excitation and affects the identification accuracy. To address these challenges, this paper presents a novel method for identifying dynamic parameters of multiple degree-of-freedom (DOF) series direct drive manipulators. Specifically, an open-loop gravity torque compensation strategy with spatial geometric characteristics is introduced to ensure secure operation during manipulator identification processes. Meanwhile, an improved model reference adaptive identification (MRAI) method is presented to estimate the nominal inertia quickly and accurately. Finally, a disturbance observer (DOB) based on minimum model perturbation bound is proposed to compensate for significant variations in model disturbances, enabling stable and precise tracking of planned exciting trajectories. This proves advantageous for fully exciting dynamic parameters and improving identification accuracy. Simulation experiments demonstrate that the tracking error of each joint’s exciting trajectory is within 0.07 radians, and the maximum absolute error of identifying dynamic parameters is 0.099169409, which verifies the feasibility and effectiveness of the proposed method.

SVD-Based Parameter Identification of Discrete-Time Stochastic Systems with Unknown Exogenous Inputs

This paper addresses the problem of parameter identification for discrete-time stochastic systems with unknown exogenous inputs. These systems form an important class of dynamic stochastic system models used to describe objects and processes under a high level of a priori uncertainty, when it is not possible to make any assumptions about the evolution of the unknown input signal or its statistical properties. The main purpose of this paper is to construct a new SVD-based modification of the existing Gillijns and De Moor filtering algorithm for linear discrete-time stochastic systems with unknown exogenous inputs. Using the theoretical results obtained, we demonstrate how this modified algorithm can be applied to solve the problem of parameter identification. The results of our numerical experiments conducted in MATLAB confirm the effectiveness of the SVD-based parameter identification method that was developed, under conditions of unknown exogenous inputs, compared to maximum likelihood parameter identification when exogenous inputs are known.

AC loss study of high-temperature superconducting stacked conductors based on parameter identification method

Fusion magnets generate significant AC loss during the processes of excitation and demagnetization. The electrical measurement was predominantly employed as the method for estimating the AC loss. However, the phase-locked amplifier method and integral method (IM) can access the average power across one or multiple current cycles but leave the instantaneous power value unrecorded. In this study, the AC loss of two types of high-temperature superconducting (HTS) stacked conductors composed of REBCO and Bi-2223 were measured. An AC loss analysis method based on parameter identification method is employed to study AC loss in REBCO and Bi-2223 stacked conductors by identifying their instantaneous inductance and instantaneous resistance. Unlike the integral method, the AC loss analysis method based on parameter identification method can analyze the average power, and also the instantaneous power at any given time using a fixed forgetting factor which can affect the accuracy of the calculation. The experimental data suggest that, under identical frequency and current, the AC loss of the REBCO stacked conductor is approximately three times that of the Bi-2223 stacked conductor.

Research on the Simulation Method for Equivalent Stiffness of Bolted Connection Thin Plate Structures

Bolted connections are widely used in assembly structures, and their dynamic characteristics are often affected by stiffness, damping, excitation, and other factors. In order to solve the problems of low computational efficiency of fine modeling and large computational error of linearized equivalent modeling of bolted structures, this paper proposes a dynamic characteristic parameter identification method for bolted structures based on the multiscale method and considering the influence of nonlinear factors. In this method, the bolted connection characteristics are simulated in the form of a combination of shear stiffness, torsional stiffness, nonlinear stiffness, and viscous damping coefficient and identified according to the test measurement frequency and frequency response function. At the same time, by establishing the nonlinear dynamic model of bolted structure, the influence of different bolt preloads and excitation forces on the dynamic characteristics of bolted structure is studied.

Electrochemical aging model of lithium-ion battery with impedance output and its parameter sensitivity analysis and identification

The electrochemical model of the battery has gained widespread recognition due to its clear physical interpretability. However, achieving an accurate electrochemical model heavily relies on precise parameter settings. To address this challenge, various model-based parameter optimization methods have been developed. Currently, most research relies on model reduction techniques and heuristic random search methods for parameter identification. These approaches often lead to time-consuming processes and a loss of physical interpretability in the electrochemical model. To overcome these limitations, we propose a novel technical framework that directly parametrizes the electrochemical model. First, we establish a battery electrochemical model that accounts for lithium plating and SEI film thickening, serving as the main focus of this study. Secondly, in order to expedite parameter identification without compromising the model's physical interpretability, we innovate by performing comprehensive sensitivity analysis using voltage and impedance characteristics. This allows us to identify the most influential parameters, which become the key for subsequent research. Finally, to ensure reasonable initialization of finite element computations and to avoid convergence issues in the identification algorithm, we combine machine learning and optimization algorithms into a two-step parameter identification method. By utilizing the proposed method to identify the model parameters, the simulations exhibit high accuracy.

Parameter Identification of Multispan Rigid Frames Using a Stiffness Separation Method.

Identifying the parameters of multispan rigid frames is challenging because of their complex structures and large computational workloads. This paper presents a stiffness separation method for the static response parameter identification of multispan rigid frames. The stiffness separation method segments the global stiffness matrix of the overall structure into the stiffness matrices of its substructures, which are to be computed, thereby reducing the computational workload and improving the efficiency of parameter identification. Loads can be applied individually to each separate substructure, thereby guaranteeing obvious local static responses. The veracity and efficacy of the proposed methodology are substantiated by applying it to three- and eight-span continuous rigid frame structures. The findings indicate that the proposed approach significantly enhances the efficiency of parameter identification for multispan rigid frames.

Adaptive PI Controller for Speed Control of Electric Drives Based on Model Reference Adaptive Identification

In this paper, to achieve auto-setting of PI controller gains when mechanical parameters are unknown, two adaptive PI controllers for speed control of electric drives are developed based on model reference adaptive identification. The adaptive linear neuron (ADALINE) neural network is used to interpret the proposed adaptive PI controller. The effect of the low-pass filter used for the feedback speed and the Coulomb friction torque on parameter identification is analysed, and a new motion equation using filtered speed is given. Additionally, a parameter identification method based on unipolar speed reference is provided. The two proposed adaptive PI controllers and the conventional PI controller are compared based on the high-precision digital simulation using MATLAB/Simulink (version R2023a). The simulation results show that both of the two proposed adaptive PI controllers are able to identify mechanical parameters, but the adaptive PI-1 controller outperforms the adaptive PI-2 controller due to its better noise attenuation performance.

A novel method for reference parameters identification and electrical property estimation of PV modules under varying operating conditions

The parameters identification of the diode model under reference condition are crucial and have significant impact on output characteristics estimation of photovoltaic (PV) module under varying operating conditions. The traditional methods of extracting model parameters have always focused on solving the parameter optimization problem only under some certain condition, which neglects their effect of performance estimation under other operating condition. In this paper, a novel method is proposed to identify the reference physical parameters and enhance the accuracy of electrical property estimation for PV modules under varying operating conditions. New objective function with penalty function is constructed by considering the estimated performance not only under reference condition but also under multiple real operating conditions. The maximum permissible error (MPE) is proposed to constrain the accuracy for key electrical output indicators and added in penalty function to ensure the accuracy under reference condition. Taking into account the inequality constraints, the guaranteed convergence particle swarm optimization with penalty function (P-GCPSO) is introduced for parameter identification. A set of comparisons between the measured and calculated results of PV modules indicate that the proposed method substantially more accurate than other documented methods for both single and double diode model. For the six different materials tested, the root mean square error (RMSE) was compared. The RMSE of the single-diode model is reduced by at least 57.98% compared to the compared method, with a maximum reduction of 86.57%. Similarly, the RMSE of the double -diode model was reduced by at least 47.24% with a maximum reduction of 88.71% compared to the compared methods. Additionally, the tests with six different types of PV modules at varying environmental conditions for over one year prove that the proposed method is reliable and practical for real-world applications. The relative error of power generation reached a minimum of 2.20%. This proves the reliability and practicability of the proposed method in practical applications. The proposed method is envisaged to be valuable for both offline analysis and online monitoring applications where an accurate, fast and consistent performance estimation tool is required.

A bootstrap-based stochastic subspace method for modal parameter identification and uncertainty quantification of high-rise buildings

This paper proposes a bootstrap-based stochastic subspace method for modal parameter identification and uncertainty quantification of high-rise buildings. Firstly, the stochastic subspace method in combination with the bootstrap technique enables the estimation of multiple sets of modal parameters from raw data series. Then, a bootstrap-based stabilization diagram is used to extract the physical modes. Finally, the modal identification and associated uncertainty quantification results are determined via statistical analysis. Through a numerical study of high-rise buildings, the performance of the proposed method is validated, demonstrating that it can provide reliable modal parameter identification and uncertainty quantification as well as has good noise immunity. Furthermore, the developed approach is employed to identify modal parameters of a 600-m-tall skyscraper during a typhoon, proving its applicability to field measurements and structural health monitoring of high-rise buildings. This paper aims to present a novel tool for modal parameter identification and associated uncertainty quantification of high-rise buildings.

Joint estimation of state-of-charge and state-of-power for hybrid supercapacitors using fractional-order adaptive unscented Kalman filter

As a new type of energy storage device, hybrid supercapacitors have the advantages of both lithium-ion batteries and supercapacitors. State of charge and state of power estimation are crucial for system operation and energy management. This work proposes a joint estimation method for the state-of-charge (SoC) and state-of-power (SoP) of hybrid supercapacitors based on a fractional-order model and unscented Kalman filter algorithm. Firstly, a parameter identification method for second-order fractional-order models is proposed using a competitive learning-based particle swarm optimization algorithm. On this basis, a SoC estimation method is designed based on the fractional-order adaptive unscented Kalman filter. Then, a SoP estimation method considering multiple constraint conditions is proposed. Finally, the proposed parameter identification and state estimation algorithms are validated under different operating dynamic conditions and environmental temperatures. The experimental results show that the error of model voltage is lower than 100 mV and the SoC estimation error is lower than 2% in the vast majority of cases, which proves the proposed algorithms have good accuracy and robustness in different environments.