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Journal articles on the topic 'Thevenin model Identification of parameters'

Author: Grafiati

Published: 28 June 2021

Last updated: 1 February 2022

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1

Zhang, Liang, Shunli Wang, Daniel-Ioan Stroe, Chuanyun Zou, Carlos Fernandez, and Chunmei Yu. "An Accurate Time Constant Parameter Determination Method for the Varying Condition Equivalent Circuit Model of Lithium Batteries." Energies 13, no. 8 (April 20, 2020): 2057. http://dx.doi.org/10.3390/en13082057.

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An accurate estimation of the state of charge for lithium battery depends on an accurate identification of the battery model parameters. In order to identify the polarization resistance and polarization capacitance in a Thevenin equivalent circuit model of lithium battery, the discharge and shelved states of a Thevenin circuit model were analyzed in this paper, together with the basic reasons for the difference in the resistance capacitance time constant and the accurate characterization of the resistance capacitance time constant in detail. The exact mathematical expression of the working characteristics of the circuit in two states were deduced thereafter. Moreover, based on the data of various working conditions, the parameters of the Thevenin circuit model through hybrid pulse power characterization experiment was identified, the simulation model was built, and a performance analysis was carried out. The experiments showed that the accuracy of the Thevenin circuit model can become 99.14% higher under dynamic test conditions and the new identification method that is based on the resistance capacitance time constant. This verifies that this method is highly accurate in the parameter identification of a lithium battery model.

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2

Khalfi, Jaouad, Najib Boumaaz, Abdallah Soulmani, and El Mehdi Laadissi. "An electric circuit model for a lithium-ion battery cell based on automotive drive cycles measurements." International Journal of Electrical and Computer Engineering (IJECE) 11, no. 4 (August 1, 2021): 2798. http://dx.doi.org/10.11591/ijece.v11i4.pp2798-2810.

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The on-board energy storage system plays a key role in electric vehicles since it directly affects their performance and autonomy. The lithium-ion battery offers satisfactory characteristics that make electric vehicles competitive with conventional ones. This article focuses on modeling and estimating the parameters of the lithium-ion battery cell when used in different electric vehicle drive cycles and styles. The model consists of an equivalent electrical circuit based on a second-order Thevenin model. To identify the parameters of the model, two algorithms were tested: Trust-Region-Reflective and Levenberg-Marquardt. To account for the dynamic behavior of the battery cell in an electric vehicle, this identification is based on measurement data that represents the actual use of the battery in different conditions and driving styles. Finally, the model is validated by comparing simulation results to measurements using the mean square error (MSE) as model performance criteria for the driving cycles (UDDS, LA-92, US06, neural network (NN), and HWFET). The results demonstrate interesting performance mostly for the driving cycles (UDDS and LA-92). This confirms that the model developed is the best solution to be integrated in a battery management system of an electric vehicle.

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3

Han, X., Y.-J. Guo, Y.-E. Zhao, and Z.-Q. Lin. "The application of power-based transfer path analysis to passenger car structure-borne noise." Proceedings of the Institution of Mechanical Engineers, Part D: Journal of Automobile Engineering 222, no. 11 (November 1, 2008): 2011–23. http://dx.doi.org/10.1243/09544070jauto750.

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Structure-borne noise in a passenger car is usually transmitted through multiple and/or multi-dimensional paths. Therefore, identification and control of these transfer paths are effective measures for noise reduction. A power-based transfer path analysis methodology is proposed for this purpose. First, the power flow of each transfer path is estimated with an equivalent-uncoupled-system method based on linear network theory and the Thevenin equivalent theorem. Next, the correlation between the power flow of each transfer path and the sound pressure in the passenger compartment is established; then the contribution of each transfer path is ranked; meanwhile the dominant paths and their key parameters are identified through the equations of power flow calculation. Finally, these key parameters can be analysed and then improved to reduce the structure-borne noise. An illustration of this methodology is given with a passenger car model containing a power plant, three mounts, a compliant car body, and an enclosed acoustic cavity. It is demonstrated that the methodology is effective to analyse and control the structure-borne noise transfer paths.

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4

Zhang, Yuwei, Wenying Liu, Fangyu Wang, Yaoxiang Zhang, and Yalou Li. "Reactive Power Control Method for Enhancing the Transient Stability Total Transfer Capability of Transmission Lines for a System with Large-Scale Renewable Energy Sources." Energies 13, no. 12 (June 17, 2020): 3154. http://dx.doi.org/10.3390/en13123154.

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With the increased proportion of intermittent renewable energy sources (RES) integrated into the sending-end, the total transfer capability of transmission lines is not sufficient during the peak periods of renewable primary energy (e.g., the wind force), causing severe RES power curtailment. The total transfer capability of transmission lines is generally restricted by the transient stability total transfer capability (TSTTC). This paper presents a reactive power control method to enhance the TSTTC of transmission lines. The key is to obtain the sensitivity between TSTTC and reactive power, while the Thevenin equivalent voltage is the link connecting TSTTC and reactive power. The Thevenin theorem states that an active circuit between two load terminals can be considered as an individual voltage source. The voltage of this source would be open-circuit voltage across the terminals, and the internal impedance of the source is the equivalent impedance of the circuit across the terminals. The Thevenin voltage used in Thevenin’s theorem is an ideal voltage source equal to the open-circuit voltage at the terminals. Thus, the sensitivities between TSTTC and the Thevenin equivalent voltages of the sending-end and receiving-end were firstly derived using the equal area criterion. Secondly, the sensitivity between the Thevenin equivalent voltage and reactive power was derived using the total differentiation method. By connecting the above sensitivities together with the relevant parameters calculated from Thevenin equivalent parameter identification and power flow equation, the sensitivity between TSTTC and reactive power was obtained, which was used as the control priority in the proposed reactive power control method. At last, the method was applied to the Gansu Province Power Grid in China to demonstrate its effectiveness, and the accuracy of the sensitivity between TSTTC and reactive power was verified.

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5

Wei, Ke Xin, and Qiao Yan Chen. "Battery SOC Estimation Based on Multi-Model Adaptive Kalman Filter." Advanced Materials Research 403-408 (November 2011): 2211–15. http://dx.doi.org/10.4028/www.scientific.net/amr.403-408.2211.

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This paper introduces multi-model adaptive kalman filter estimation algorithm.Based on the battery thevenin model,the multi-model adaptive kalman filter is applied to the battery SOC(state of charge) estimation, which solute the battery SOC estimation in conditions that the battery model parameters change caused by temperature changing. Simulation results show that compared to the single model kalman filter algorithm, Multi-Model adaptive kalman filter algorithm improves the estimation precision and reliability greatly.

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6

Li, Shaowu. "Circuit Parameter Range of Photovoltaic System to Correctly Use the MPP Linear Model of Photovoltaic Cell." Energies 14, no. 13 (July 2, 2021): 3997. http://dx.doi.org/10.3390/en14133997.

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The real-time linearization of a photovoltaic (PV) cell has been implemented well by the proposition of two maximum power point (MPP) linear models (MPP Thevenin cell model and MPP Norton cell model). However, there is no work to specially analyze the circuit parameter range (CPR) to correctly use them, which seriously impedes the development of the linear control theory involving them. To deal with this problem, in this paper, PV systems with three usual outputs are analyzed and the expressions of their CPR are proposed under ideal conditions. Meanwhile, these expressions are improved to match the practical application. They disclose the relationships between load (or bus voltage) and model parameters of the MPP Thevenin cell model (MPP-TCM) when the MPP of PV system always exists. They also reveal the constraints of load (or bus voltage) when the MPP-TCM is always available. Finally, by some simulation experiments, the accuracy of the expressions of the CPR is verified, the regular patterns of the CPR changing with weather are disclosed, and the comparison of the CPR for different PV systems are made. In this work, the relationships between MPP-TCM and circuit parameters are successfully found, disclosing the constraints among parameters when the MPP-TCM is used to implement the overall linearization of a PV system.

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7

Xiong, Rui, Hongwen He, and Kai Zhao. "Research on an Online Identification Algorithm for a Thevenin Battery Model by an Experimental Approach." International Journal of Green Energy 12, no. 3 (October 22, 2014): 272–78. http://dx.doi.org/10.1080/15435075.2014.891512.

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8

Liu, Xintian, Xuhui Deng, Yao He, Xinxin Zheng, and Guojian Zeng. "A Dynamic State-of-Charge Estimation Method for Electric Vehicle Lithium-Ion Batteries." Energies 13, no. 1 (December 25, 2019): 121. http://dx.doi.org/10.3390/en13010121.

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With the increasing environmental concerns, plug-in electric vehicles will eventually become the main transportation tools in future smart cities. As a key component and the main power source, lithium-ion batteries have been an important object of research studies. In order to efficiently control electric vehicle powertrains, the state of charge (SOC) of lithium-ion batteries must be accurately estimated by the battery management system. This paper aims to provide a more accurate dynamic SOC estimation method for lithium-ion batteries. A dynamic Thevenin model with variable parameters affected by the temperature and SOC is established to model the battery. An unscented Kalman particle filter (UPF) algorithm is proposed based on the unscented Kalman filter (UKF) algorithm and the particle filter (PF) algorithm to generate nonlinear particle filter according to the advantages and disadvantages of various commonly used filtering algorithms. The simulation results show that the unscented Kalman particle filter algorithm based on the dynamic Thevenin model can predict the SOC in real time and it also has strong robustness against noises.

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9

Bao, Hui, Wei Jiang, and Dan Wei. "Electric Vehicle Battery SOC Estimation Based on EKF." Advanced Materials Research 926-930 (May 2014): 927–31. http://dx.doi.org/10.4028/www.scientific.net/amr.926-930.927.

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In order to estimate the battery state of charge (SOC) accurately, an improved Thevenin model of a battery is established, its mathematical relation is very simple, and also it is easy to realize. In addition, we identify the model parameters, and then use extended Calman filter algorithm to estimate the battery state of charge. The simulation results show that, this model can well reflect the dynamic and static characteristics of a battery, and the Calman algorithm can keep good accuracy in the estimation process.

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10

Wang, Hao, Yanping Zheng, and Yang Yu. "Lithium-Ion Battery SOC Estimation Based on Adaptive Forgetting Factor Least Squares Online Identification and Unscented Kalman Filter." Mathematics 9, no. 15 (July 22, 2021): 1733. http://dx.doi.org/10.3390/math9151733.

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In order to improve the estimation accuracy of the battery state of charge (SOC) based on the equivalent circuit model, a lithium-ion battery SOC estimation method based on adaptive forgetting factor least squares and unscented Kalman filtering is proposed. The Thevenin equivalent circuit model of the battery is established. Through the simulated annealing optimization algorithm, the forgetting factor is adaptively changed in real-time according to the model demand, and the SOC estimation is realized by combining the least-squares online identification of the adaptive forgetting factor and the unscented Kalman filter. The results show that the terminal voltage error identified by the adaptive forgetting factor least-squares online identification is extremely small; that is, the model parameter identification accuracy is high, and the joint algorithm with the unscented Kalman filter can also achieve a high-precision estimation of SOC.

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11

Reński, Andrzej. "Identification of Driver Model Parameters." International Journal of Occupational Safety and Ergonomics 7, no. 1 (January 2001): 79–92. http://dx.doi.org/10.1080/10803548.2001.11076478.

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12

Jeff B. Blau, D. A. Woolhiser, and L. J. Lane. "Identification of Erosion Model Parameters." Transactions of the ASAE 31, no. 3 (1988): 0839–45. http://dx.doi.org/10.13031/2013.30789.

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13

Guo, Xiangwei, Xiaozhuo Xu, Jiahao Geng, Xian Hua, Yan Gao, and Zhen Liu. "SOC Estimation with an Adaptive Unscented Kalman Filter Based on Model Parameter Optimization." Applied Sciences 9, no. 19 (October 6, 2019): 4177. http://dx.doi.org/10.3390/app9194177.

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State of charge (SOC) estimation is generally acknowledged to be one of the most important functions of the battery management system (BMS) and is thus widely studied in academia and industry. Based on an accurate SOC estimation, the BMS can optimize energy efficiency and protect the battery from being over-charged or over-discharged. The accurate online estimation of the SOC is studied in this paper. First, it is proved that the second-order resistance capacitance (RC) model is the most suitable equivalent circuit model compared with the Thevenin and multi-order models. The second-order RC equivalent circuit model is established, and the model parameters are identified. Second, the reasonable optimization of model parameters is studied, and a reasonable optimization method is proposed to improve the accuracy of SOC estimation. Finally, the SOC is estimated online based on the adaptive unscented Kalman filter (AUKF) with optimized model parameters, and the results are compared with the results of an estimation based on pre-optimization model parameters. Simulation experiments show that, without affecting the convergence of the initial error of the AUKF, the model after parameter optimization has a higher online SOC estimation accuracy.

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14

Huang, Jian Long, Ying Nan Wang, Zhong Feng Wang, Feng Li Han, and Li Gang Li. "The Experiments of Dual Kalman Filter in Lithium Battery SOC Estimation." Applied Mechanics and Materials 494-495 (February 2014): 1509–12. http://dx.doi.org/10.4028/www.scientific.net/amm.494-495.1509.

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We use Thevenin battery model and Kalman filter algorithm to online estimate lithium-ion battery pack state of charge (SOC) in this paper. In order to improve the accuracy of the model we use least square method and Dual Kalman filter (DEKF) algorithms to identify the parameters of model. The battery model can reflect the true state of internal battery well. The principle of Kalman filter algorithm is introduced. The relevant battery testing laboratory is designed. The algorithm has better accuracy when online estimate SOC and adapt to the environment well from experimental results. Finally, the convergence and robustness of DEKF algorithm are verified. It solves the problems that initial estimates are not accuracy and cumulative error.

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15

Huang, Jian Long, Zhong Feng Wang, Kun Ya Guo, Xiao Tian Wang, Li Gang Li, and Zhe Zhu Huang. "The Analysis of Modeling of Dual Kalman Filter in Lithium Battery SOC Estimates." Applied Mechanics and Materials 513-517 (February 2014): 4294–97. http://dx.doi.org/10.4028/www.scientific.net/amm.513-517.4294.

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In order to online estimate the state of charge (SOC) of lithium-ion battery pack in this paper. This article establishes a dual Kalman filter (DEKF) algorithms for it. We establish a battery model of state space expression based on Thevenin battery model and Kalman filter algorithms. We use least square method and DEKF algorithms to identify the parameters of battery model in order to improve the accuracy of battery model. It can make battery model reflect true state of internal battery well. It introduces the principle which is dual Kalman filter algorithm online estimate the state of charge. Finally, it verifies DEKF algorithm has better convergence and robustness which can effectively resolve the problems of initial estimates error and cumulative error issue.

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16

TANAKA, Ikuo, Isamu TSUJI, Hirofumi HORATA, and Kazuhide ITO. "WALL SURFACE DECOMPOSITION MODEL AND IDENTIFICATION OF MODEL PARAMETERS." Journal of Environmental Engineering (Transactions of AIJ) 79, no. 702 (2014): 671–80. http://dx.doi.org/10.3130/aije.79.671.

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17

Sun, Bing Xiang, Jiu Chun Jiang, and Zhan Guo Wang. "SOC Estimation of Ni-MH Battery Pack Based on Approved HPPC Test and EKF Algorithm for HEV." Advanced Materials Research 403-408 (November 2011): 4398–402. http://dx.doi.org/10.4028/www.scientific.net/amr.403-408.4398.

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In this paper, Methods of SOC estimation of Extended Kalman Filter (EKF) is studied based on the characteristics of Nickel-Metal Hydride (Ni-MH) battery pack with 120 cells in series and 8Ah capacity for HEV. In the study of EKF-based SOC estimation, the improved Thevenin circuit model is adopted, and a new hybrid pulse power characterization （HPPC） test is designed to identify the model parameters by using piecewise linear regression method. In this way, the precision of the circuit model is improved. In addition, The Kalman gain matrix is optimized for EKF iterative algorithm by two ways: a constant gain is increased taking into account the entire process; a dynamic gain which increases at the beginning of abrupt change and decreases rapidly after abrupt change is set up. The improvement achieves a good tracing prediction.

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18

Januszkiewicz, Ƚukasz, Paolo Di Barba, and Sƚawomir Hausman. "Automated identification of human-body model parameters." International Journal of Applied Electromagnetics and Mechanics 51, s1 (April 7, 2016): S41—S47. http://dx.doi.org/10.3233/jae-2009.

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19

Pink, Václav. "Identification of a predator-prey model parameters." IOSR Journal of Mathematics 10, no. 1 (2014): 89–94. http://dx.doi.org/10.9790/5728-10148994.

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20

Keyhani, A., H. Tsai, S. Pillutla, and A. Abur. "Identification of high frequency transformer model parameters." Electric Power Systems Research 42, no. 2 (August 1997): 127–33. http://dx.doi.org/10.1016/s0378-7796(96)01197-2.

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21

Sedira, D., Y. Gabi, A. Kedous-Lebouc, K. Jacob, B. Wolter, and B. Straß. "ABC method for hysteresis model parameters identification." Journal of Magnetism and Magnetic Materials 505 (July 2020): 166724. http://dx.doi.org/10.1016/j.jmmm.2020.166724.

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22

Oti, John, Ferenc Vajda, and Edward Della Torre. "Identification of parameters in a moving model." Journal of Applied Physics 69, no. 8 (April 15, 1991): 4826–28. http://dx.doi.org/10.1063/1.348245.

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23

Leite, J. V., N. Sadowski, P. Kuo-Peng, N. J. Batistela, and J. P. A. Bastos. "The inverse jiles-atherton model parameters identification." IEEE Transactions on Magnetics 39, no. 3 (May 2003): 1397–400. http://dx.doi.org/10.1109/tmag.2003.810216.

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24

Vajda, F., and E. D. Torre. "Identification of parameters in an accommodation model." IEEE Transactions on Magnetics 30, no. 6 (1994): 4371–73. http://dx.doi.org/10.1109/20.334092.

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25

Franulović, Marina, Robert Basan, Robert Kunc, and Ivan Prebil. "Automation of LCF material model parameters’ identification." Computational Materials Science 48, no. 3 (May 2010): 529–36. http://dx.doi.org/10.1016/j.commatsci.2010.02.019.

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26

Fan, Yongcun, Haotian Shi, Shunli Wang, Carlos Fernandez, Wen Cao, and Junhan Huang. "A Novel Adaptive Function—Dual Kalman Filtering Strategy for Online Battery Model Parameters and State of Charge Co-Estimation." Energies 14, no. 8 (April 17, 2021): 2268. http://dx.doi.org/10.3390/en14082268.

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This paper aims to improve the stability and robustness of the state-of-charge estimation algorithm for lithium-ion batteries. A new internal resistance-polarization circuit model is constructed on the basis of the Thevenin equivalent circuit to characterize the difference in internal resistance between charge and discharge. The extended Kalman filter is improved through adding an adaptive noise tracking algorithm and the Kalman gain in the unscented Kalman filter algorithm is improved by introducing a dynamic equation. In addition, for benignization of outliers of the two above-mentioned algorithms, a new dual Kalman algorithm is proposed in this paper by adding a transfer function and through weighted mutation. The model and algorithm accuracy is verified through working condition experiments. The result shows that: the errors of the three algorithms are all maintained within 0.8% during the initial period and middle stages of the discharge; the maximum error of the improved extension of Kalman algorithm is over 1.5%, that of improved unscented Kalman increases to 5%, and the error of the new dual Kalman algorithm is still within 0.4% during the latter period of the discharge. This indicates that the accuracy and robustness of the new dual Kalman algorithm is better than those of traditional algorithm.

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27

Xia, Bizhong, Guanghao Chen, Jie Zhou, Yadi Yang, Rui Huang, Wei Wang, Yongzhi Lai, Mingwang Wang, and Huawen Wang. "Online Parameter Identification and Joint Estimation of the State of Charge and the State of Health of Lithium-Ion Batteries Considering the Degree of Polarization." Energies 12, no. 15 (July 31, 2019): 2939. http://dx.doi.org/10.3390/en12152939.

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The state of charge (SOC) and the state of health (SOH) are the two most important indexes of batteries. However, they are not measurable with transducers and must be estimated with mathematical algorithms. A precise model and accurate available battery capacity are crucial to the estimation results. An improved speed adaptive velocity particle swarm optimization algorithm (SAVPSO) based on the Thevenin model is used for online parameter identification, which is used with an unscented Kalman filter (UKF) to estimate the SOC. In order to achieve the cyclic update of the SOH, the concept of degree of polarization (DOP) is proposed. The cyclic update of available capacity is thus obtainable to conversely promote the estimation accuracy of the SOC. The estimation experiments in the whole aging process of batteries show that the proposed method can enhance the SOC estimation accuracy in the full battery life cycle with the cyclic update of the SOH, even in cases of operating aged batteries and under complex operating conditions.

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28

Hinz, Hartmut. "Comparison of Lithium-Ion Battery Models for Simulating Storage Systems in Distributed Power Generation." Inventions 4, no. 3 (August 6, 2019): 41. http://dx.doi.org/10.3390/inventions4030041.

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Lithium-ion batteries are well known in numerous commercial applications. Using accurate and efficient models, system designers can predict the behavior of batteries and optimize the associated performance management. Model-based development comprises the investigation of electrical, electro-chemical, thermal, and aging characteristics. This paper focuses on the analysis of models describing the electrical behavior. In particular, it investigates how cell voltage and state of charge can be determined with sufficient accuracy for a given load profile. For this purpose, the Thevenin-based, the Rint, and the Shepherd’s models, as well as a generic library model of an electronic circuit simulation software package, are compared. The procedure for determining model parameters is discussed in detail. All models are evaluated for the application in the analysis of distributed power generation. The validation is carried out by comparing simulation and measurement results with the help of a case study.

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29

Kuczmann, Miklós, Attila Szücs, and Gergely Kovács. "Transformer Model Identification by Ārtap." Periodica Polytechnica Electrical Engineering and Computer Science 65, no. 2 (April 6, 2021): 123–30. http://dx.doi.org/10.3311/ppee.17606.

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The paper presents how Ārtap can be used for determining the equivalent circuit parameters of a one phase transformer as a benchmark problem. The following unknown parameters of the equivalent circuit are identified: primary resistance and primary leakage reactance, secondary resistance and secondary leakage reactance, finally magnetizing resistance, and magnetizing reactance. The known quantities from measurement are the primary voltage, primary current, power factor, secondary voltage, and the load resistance. Algorithms implemented in Ārtap are used for determining the transformer parameters and the results are compared with the analytical solution.

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30

Muratoglu, Y., and A. Alkaya. "Unscented Kalman Filter based State of Charge Estimation for the Equalization of Lithium-ion Batteries on Electrical Vehicles." Engineering, Technology & Applied Science Research 9, no. 6 (December 1, 2019): 4876–82. http://dx.doi.org/10.48084/etasr.3111.

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Accurate state of charge estimation and robust cell equalization are vital in optimizing the battery management system and improving energy management in electric vehicles. In this paper, the passive balance control based equalization scheme is proposed using a combined dynamic battery model and the unscented Kalman filter based state of charge estimation. The lithium-ion battery is modeled with a 2nd order Thevenin equivalent circuit. The combined dynamic model of the lithium-ion battery, where the model parameters are estimated depending on the state of charge, and the unscented Kalman filter based state of charge, are used to improve the performance of the passive balance control based equalization. The experimental results verified the superiority of the combined dynamic battery model and the unscented Kalman filter algorithm with very tight error bounds. Furthermore, these results showed that the presented passive balance control based equalization scheme is suitable for the equalization of series-connected lithium-ion batteries.

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31

Cui, Zhen Ping, Yong Xin Qin, and Hao Li. "Prediction Method of Lithium Battery's State of Charge Based on No Trace of Calman Filter." Advanced Materials Research 912-914 (April 2014): 1888–91. http://dx.doi.org/10.4028/www.scientific.net/amr.912-914.1888.

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The Thevenin equivalent circuit model is established for single lithium battery,current and voltage data to identify the parameters of the equivalent circuit is obtained by the discharge experiment, and the open circuit voltage and charge state relationship curve was obtained by curve fitting.On this basis, design the extended Kalman filter algorithm and unscented Kalman filter algorithm on the lithium battery state of charge, then use Matlab/Simulik simulation, the results of the state prediction of the two different algorithms are compared. The analysis results show that two kinds of algorithm are effective for single lithium battery state of charge estimation, and no trace of Calman filter algorithm can effectively solve the the problem of accuracy is not high of the extended Calman filter ,which due to the linear approximation.

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32

Meng, Qing Bo, Yi Xin Yin, and Gui Ling Qiao. "PID Controller Parameters Identification Based on Data Model." Advanced Materials Research 433-440 (January 2012): 4254–61. http://dx.doi.org/10.4028/www.scientific.net/amr.433-440.4254.

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To tune PID controller’s parameters, the algorithm based on the Data-Model of Controlled plant is presented. With input and output data, Data Model as well as the feature data of controlled plant is identified by the Convolution Equation, and then the fastest control signal referenced to ideal fastest response is calculated according to the constraint condition of control signal. Finally, the closed loop control system of the controlled plant is designed, and the PID parameters are identified by the Least-Squares-Method (LSM) with the data of the fastest control signal.

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33

Menshikov, Yuri L. "Identification of Mathematical Model Parameters of Stationary Process." Journal of Applied Mathematics and Physics 02, no. 05 (2014): 189–93. http://dx.doi.org/10.4236/jamp.2014.25023.

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34

Laban, M., and J. A. Mulder. "On-Line Identification of Aircraft Aerodynamic Model Parameters." IFAC Proceedings Volumes 25, no. 15 (July 1992): 199–204. http://dx.doi.org/10.1016/s1474-6670(17)50633-3.

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35

Pelevin, А. Е. "Identification of object model parameters under external disturbances." Giroskopiya i Navigatsiya 22, no. 4 (2014): 111–20. http://dx.doi.org/10.17285/0869-7035.2014.22.4.111-120.

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36

Okubo, Hiroki, and Mont Hubbard. "Identification of basketball parameters for a simulation model." Procedia Engineering 2, no. 2 (June 2010): 3281–86. http://dx.doi.org/10.1016/j.proeng.2010.04.145.

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37

Pelevin, A. E. "Identification of vehicle model parameters under external disturbances." Gyroscopy and Navigation 6, no. 2 (April 2015): 143–48. http://dx.doi.org/10.1134/s2075108715020091.

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38

Rugkwamsook, P., and C. E. Korman. "Identification of magnetic aftereffect model parameters: Temperature dependence." IEEE Transactions on Magnetics 34, no. 4 (July 1998): 1863–65. http://dx.doi.org/10.1109/20.706728.

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39

Calogine, D., H. Boyer, S. Ndoumbe, C. Rivière, and F. Miranville. "Identification of Parameters in Building Concentration Dispersion Model." Indoor and Built Environment 19, no. 2 (March 22, 2010): 250–66. http://dx.doi.org/10.1177/1420326x09349900.

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Bornitz, Matthias, Thomas Zahnert, Hans-Jürgen Hardtke, and Karl-Bernd Hüttenbrink. "Identification of Parameters for the Middle Ear Model." Audiology and Neurotology 4, no. 3-4 (1999): 163–69. http://dx.doi.org/10.1159/000013836.

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Hensen, R. H. A., M. J. G. van de Molengraft, and M. Steinbuch. "Frequency domain identification of dynamic friction model parameters." IEEE Transactions on Control Systems Technology 10, no. 2 (March 2002): 191–96. http://dx.doi.org/10.1109/87.987064.

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Chisari, Corrado, Lorenzo Macorini, Claudio Amadio, and Bassam A. Izzuddin. "Identification of mesoscale model parameters for brick-masonry." International Journal of Solids and Structures 146 (August 2018): 224–40. http://dx.doi.org/10.1016/j.ijsolstr.2018.04.003.

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Imamovic, Ismar, Adnan Ibrahimbegovic, Catherine Knopf-Lenoir, and Esad Mesic. "Plasticity-damage model parameters identification for structural connections." Coupled systems mechanics 4, no. 4 (December 25, 2015): 337–64. http://dx.doi.org/10.12989/csm.2015.4.4.337.

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Cabrera, J. A., Antonio Ortiz, B. Estebanez, F. Nadal, and A. Simon. "A coevolutionary algorithm for tyre model parameters identification." Structural and Multidisciplinary Optimization 41, no. 5 (November 13, 2009): 749–63. http://dx.doi.org/10.1007/s00158-009-0446-5.

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Falco, M., A. Curami, and A. Zasso. "Nonlinear effects in sectional model aeroelastic parameters identification." Journal of Wind Engineering and Industrial Aerodynamics 42, no. 1-3 (October 1992): 1321–32. http://dx.doi.org/10.1016/0167-6105(92)90140-6.

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Al-Khusaibi, T. M., K. A. Ellithy, and M. R. Irving. "State-of-the-Art Methods for Electric Power Systems Voltage Stability Analysis." Sultan Qaboos University Journal for Science [SQUJS] 5 (December 1, 2000): 247. http://dx.doi.org/10.24200/squjs.vol5iss0pp247-263.

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This paper presents a literature survey on the subject of voltage stability analysis of power systems. The survey describes several published methods and techniques used to determine voltage stability indices. These indices predict proximity to voltage instability and collapse problems. The Q-V and P-V curves; singular value decomposition; modal analysis; test function; reduced determinant; loading margin by multiple power flow solutions; local load margins; thevenin/load impedance; and energy function are the methods which have been decribed in the paper. The methods described are based on the original work that first proposed them. They are based on the power-flow system model, where the variation of real and reactive powers are assumed to be the main parameters driving the system to voltage instability. Some of the described methods were applied on the IEEE 30-bus power system.

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Du, Xin, Min Fang Peng, Hong Mei Zeng, Liang Zhu, Hong Wei Che, and Zheng Yi Liu. "Fault Section Location in Distribution Network with DG Based on Voltage Sag Correlation Coefficient." Advanced Materials Research 787 (September 2013): 902–8. http://dx.doi.org/10.4028/www.scientific.net/amr.787.902.

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A fault location method based on correlation coefficient was proposed in the paper for the distribution system with distributed generation (DG). At first, by acquiring the Thevenin equivalent parameters of each DG to establish simulation model of the actual distribution network. Then voltage sag feature vectors of each bus would be obtained by simulating short-circuit fault at the location of each bus, respectively. Afterwards, by calculating and analyzing the correlation coefficient between each bus and the actual fault node,, suspicious fault sections can be determined. Finally, the fault section was identified by utilizing the associated node of every suspicious fault section .The method could reduce the fault search range obviously, without a massive requirement of measuring devices, and the required data can be obtained easily. Simulation results of a 10kV distribution network with DG showed that the proposed method was accuracy and effective.

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Amir, Mounir, Mourad Zergoug, and Aissa Amrouche. "Identification Parameters with Neural Network for Preisach Hysteresis Model." Applied Mechanics and Materials 541-542 (March 2014): 487–93. http://dx.doi.org/10.4028/www.scientific.net/amm.541-542.487.

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The description of hysteresis is one of the classical problems in magnetic materials. The progress in its solution determines the reliability of modeling and the quality of design of a wide range of devices, the proposed approach has been applied to model the behavior of many samples and the results show the robustness and efficiency of Neural Network to model the phenomenon of hysteresis loop. The goal of this study is to optimize the parameters of hysteresis Loop by Preisach model with the Neural Network, the method developed is based on an analysis of two distribution functions. The modified Lorentzian function and Gaussian function have been analyzed. The implemented software and performances of the distributions are presented.

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Novák, J., P. Chalupa, and V. Bobál. "Identification of Local Model Networks Parameters Using Fuzzy Clustering." IFAC Proceedings Volumes 43, no. 10 (2010): 265–70. http://dx.doi.org/10.3182/20100826-3-tr-4015.00050.

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Marano, G. C., M. Pelliciari, T. Cuoghi, B. Briseghella, D. Lavorato, and A. M. Tarantino. "Degrading Bouc–Wen Model Parameters Identification Under Cyclic Load." International Journal of Geotechnical Earthquake Engineering 8, no. 2 (July 2017): 60–81. http://dx.doi.org/10.4018/ijgee.2017070104.

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The purpose of this article is to describe the Bouc–Wen model of hysteresis for structural engineering which is used to describe a wide range of nonlinear hysteretic systems, as a consequence of its capability to produce a variety of hysteretic patterns. This article focuses on the application of the Bouc–Wen model to predict the hysteretic behaviour of reinforced concrete bridge piers. The purpose is to identify the optimal values of the parameters so that the output of the model matches as well as possible the experimental data. Two repaired, retrofitted and reinforced concrete bridge pier specimens (in a 1:6 scale of a real bridge pier) are tested in a laboratory and used for experiments in this article. An identification of Bouc–Wen model's parameters is performed using the force–displacement experimental data obtained after cyclic loading tests on these two specimens. The original model involves many parameters and complex pinching and degrading functions. This makes the identification solution unmanageable and with numerical problems. Furthermore, from a computational point of view, the identification takes too much time. The novelty of this work is the proposal of a simplification of the model allowed by simpler pinching and degrading functions and the reduction of the number of parameters. The latter innovation is effective in reducing computational efforts and is performed after a deep study of the mechanical effects of each parameter on the pier response. This simplified model is implemented in a MATLAB code and the numerical results are well fit to the experimental results and are reliable in terms of manageability, stability, and computational time.

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