Thematische Bibliographien / Thevenin model Identification of parameters

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Auswahl der wissenschaftlichen Literatur zum Thema „Thevenin model Identification of parameters“

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Veröffentlicht am 28. Juni 2021

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Zeitschriftenartikel zum Thema "Thevenin model Identification of parameters"

Zhang, Liang, Shunli Wang, Daniel-Ioan Stroe, Chuanyun Zou, Carlos Fernandez und Chunmei Yu. „An Accurate Time Constant Parameter Determination Method for the Varying Condition Equivalent Circuit Model of Lithium Batteries“. Energies 13, Nr. 8 (20.04.2020): 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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Khalfi, Jaouad, Najib Boumaaz, Abdallah Soulmani und 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, Nr. 4 (01.08.2021): 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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Han, X., Y.-J. Guo, Y.-E. Zhao und 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, Nr. 11 (01.11.2008): 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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Zhang, Yuwei, Wenying Liu, Fangyu Wang, Yaoxiang Zhang und 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, Nr. 12 (17.06.2020): 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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Wei, Ke Xin, und 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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Li, Shaowu. „Circuit Parameter Range of Photovoltaic System to Correctly Use the MPP Linear Model of Photovoltaic Cell“. Energies 14, Nr. 13 (02.07.2021): 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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Xiong, Rui, Hongwen He und Kai Zhao. „Research on an Online Identification Algorithm for a Thevenin Battery Model by an Experimental Approach“. International Journal of Green Energy 12, Nr. 3 (22.10.2014): 272–78. http://dx.doi.org/10.1080/15435075.2014.891512.

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Liu, Xintian, Xuhui Deng, Yao He, Xinxin Zheng und Guojian Zeng. „A Dynamic State-of-Charge Estimation Method for Electric Vehicle Lithium-Ion Batteries“. Energies 13, Nr. 1 (25.12.2019): 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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Bao, Hui, Wei Jiang und Dan Wei. „Electric Vehicle Battery SOC Estimation Based on EKF“. Advanced Materials Research 926-930 (Mai 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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Wang, Hao, Yanping Zheng und Yang Yu. „Lithium-Ion Battery SOC Estimation Based on Adaptive Forgetting Factor Least Squares Online Identification and Unscented Kalman Filter“. Mathematics 9, Nr. 15 (22.07.2021): 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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Dissertationen zum Thema "Thevenin model Identification of parameters"

Loucký, Vojtěch. „Model Li-ion akumulátoru“. Master's thesis, Vysoké učení technické v Brně. Fakulta elektrotechniky a komunikačních technologií, 2021. http://www.nusl.cz/ntk/nusl-442783.

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This diploma thesis deals with description of the principle of Li-ion cells, literature search on the topic of mathematical models of Li-ion cells and the creation of a selected mathematical model in MATLAB, which is able to simulate the course of voltage and state of charge as a function of time for different ambient conditions, such as various aging of battery .The creation of both the model and the procedure of identification of parameters necessary for the creation of the model are described here as well as different options of identification of parameters. The selected Thevenin model is then compared with the real course and the accuracy of the model is evaluated with respect to the measured course.

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Bornitz, Matthias, Thomas Zahnert, Hans-Jürgen Hardtke und Karl-Bernd Hüttenbrink. „Identification of Parameters for the Middle Ear Model“. Saechsische Landesbibliothek- Staats- und Universitaetsbibliothek Dresden, 2014. http://nbn-resolving.de/urn:nbn:de:bsz:14-qucosa-135790.

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This paper presents a method of parameter identification for a finite-element model of the human middle ear. The parameter values are estimated using a characterization of the difference in natural frequencies and mode shapes of the tympanic membrane between the model and the specimens. Experimental results were obtained from temporal bone specimens under sound excitation (300–3,000 Hz). The first 3 modes of the tympanic membrane could be observed with a laser scanning vibrometer and were used to estimate the stiffness parameters for the orthotropic finite-element model of the eardrum. A further point of discussion is the parameter sensitivity and its implication for the identification process
Dieser Beitrag ist mit Zustimmung des Rechteinhabers aufgrund einer (DFG-geförderten) Allianz- bzw. Nationallizenz frei zugänglich

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Bornitz, Matthias, Thomas Zahnert, Hans-Jürgen Hardtke und Karl-Bernd Hüttenbrink. „Identification of Parameters for the Middle Ear Model“. Karger, 1999. https://tud.qucosa.de/id/qucosa%3A27677.

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This paper presents a method of parameter identification for a finite-element model of the human middle ear. The parameter values are estimated using a characterization of the difference in natural frequencies and mode shapes of the tympanic membrane between the model and the specimens. Experimental results were obtained from temporal bone specimens under sound excitation (300–3,000 Hz). The first 3 modes of the tympanic membrane could be observed with a laser scanning vibrometer and were used to estimate the stiffness parameters for the orthotropic finite-element model of the eardrum. A further point of discussion is the parameter sensitivity and its implication for the identification process.
Dieser Beitrag ist mit Zustimmung des Rechteinhabers aufgrund einer (DFG-geförderten) Allianz- bzw. Nationallizenz frei zugänglich.

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Kučerová, Anna. „Identification of nonlinear mechanical model parameters based on softcomputing methods“. Cachan, Ecole normale supérieure, 2007. http://tel.archives-ouvertes.fr/tel-00256025/fr/.

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Le problème d'identification des paramètres apparaît dans beaucoup de problèmes en génie civil sous formes différentes et il peut être résolu par beaucoup de méthodes distinctes. Cette thèse présente deux philosophies principales d'identification avec orientation vers les méthodes basées sur intelligence artificielle. Les aspects pratiques sont montrés sur plusieurs problèmes d'identification, où les paramètres des modèles mécaniques non linéaires sont à déterminer
The problem of parameters identification occurs in many engineering tasks and, as such, attains several différent forms and can bc solved by many very distinct methods. An overview of two basic philosophies of thé identification is presented in this thesis with an emphasis put on thé area of sort computing methods. Practical aspects are shown on several identification tasks, where parameters of highly non linear mechanical models are to be determined

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Temeltas, H. „Real-time identification of robot dynamic model parameters using parallel processing“. Thesis, University of Nottingham, 1993. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.357973.

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Karameh, Fadi Nabih. „On-line identification and control algorithm for system model with jump parameters using wavelets“. Thesis, Massachusetts Institute of Technology, 1996. http://hdl.handle.net/1721.1/11019.

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Thesis (M.S.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 1996.
Includes bibliographical references (leaves 73-75).
by Fadi Nabih Karameh.
M.S.

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Zhou, Haiyan. „Stochastic Inverse Methods to Identify non-Gaussian Model Parameters in Heterogeneous Aquifers“. Doctoral thesis, Universitat Politècnica de València, 2011. http://hdl.handle.net/10251/12267.

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La modelación numérica del flujo de agua subterránea y del transporte de masa se está convirtiendo en un criterio de referencia en la actualidad para la evaluación de recursos hídricos y la protección del medio ambiente. Para que las predicciones de los modelos sean fiables, estos deben de estar lo más próximo a la realidad que sea posible. Esta proximidad se adquiere con los métodos inversos, que persiguen la integración de los parámetros medidos y de los estados del sistema observados en la caracterización del acuífero. Se han propuesto varios métodos para resolver el problema inverso en las últimas décadas que se discuten en la tesis. El punto principal de esta tesis es proponer dos métodos inversos estocásticos para la estimación de los parámetros del modelo, cuando estos no se puede describir con una distribución gausiana, por ejemplo, las conductividades hidráulicas mediante la integración de observaciones del estado del sistema, que, en general, tendrán una relación no lineal con los parámetros, por ejemplo, las alturas piezométricas. El primer método es el filtro de Kalman de conjuntos con transformación normal (NS-EnKF) construido sobre la base del filtro de Kalman de conjuntos estándar (EnKF). El EnKF es muy utilizado como una técnica de asimilación de datos en tiempo real debido a sus ventajas, como son la eficiencia y la capacidad de cómputo para evaluar la incertidumbre del modelo. Sin embargo, se sabe que este filtro sólo trabaja de manera óptima cuándo los parámetros del modelo y las variables de estado siguen distribuciones multigausianas. Para ampliar la aplicación del EnKF a vectores de estado no gausianos, tales como los de los acuíferos en formaciones fluvio-deltaicas, el NSEnKF propone aplicar una transformación gausiana univariada. El vector de estado aumentado formado por los parámetros del modelo y las variables de estado se transforman en variables con una distribución marginal gausiana.
Zhou ., H. (2011). Stochastic Inverse Methods to Identify non-Gaussian Model Parameters in Heterogeneous Aquifers [Tesis doctoral no publicada]. Universitat Politècnica de València. https://doi.org/10.4995/Thesis/10251/12267
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Benedetti, Lorenzo. „Substructuring approache in state space models for dynamic system parameters identification“. Master's thesis, Alma Mater Studiorum - Università di Bologna, 2011. http://amslaurea.unibo.it/2325/.

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In the recent decade, the request for structural health monitoring expertise increased exponentially in the United States. The aging issues that most of the transportation structures are experiencing can put in serious jeopardy the economic system of a region as well as of a country. At the same time, the monitoring of structures is a central topic of discussion in Europe, where the preservation of historical buildings has been addressed over the last four centuries. More recently, various concerns arose about security performance of civil structures after tragic events such the 9/11 or the 2011 Japan earthquake: engineers looks for a design able to resist exceptional loadings due to earthquakes, hurricanes and terrorist attacks. After events of such a kind, the assessment of the remaining life of the structure is at least as important as the initial performance design. Consequently, it appears very clear that the introduction of reliable and accessible damage assessment techniques is crucial for the localization of issues and for a correct and immediate rehabilitation. The System Identification is a branch of the more general Control Theory. In Civil Engineering, this field addresses the techniques needed to find mechanical characteristics as the stiffness or the mass starting from the signals captured by sensors. The objective of the Dynamic Structural Identification (DSI) is to define, starting from experimental measurements, the modal fundamental parameters of a generic structure in order to characterize, via a mathematical model, the dynamic behavior. The knowledge of these parameters is helpful in the Model Updating procedure, that permits to define corrected theoretical models through experimental validation. The main aim of this technique is to minimize the differences between the theoretical model results and in situ measurements of dynamic data. Therefore, the new model becomes a very effective control practice when it comes to rehabilitation of structures or damage assessment. The instrumentation of a whole structure is an unfeasible procedure sometimes because of the high cost involved or, sometimes, because it’s not possible to physically reach each point of the structure. Therefore, numerous scholars have been trying to address this problem. In general two are the main involved methods. Since the limited number of sensors, in a first case, it’s possible to gather time histories only for some locations, then to move the instruments to another location and replay the procedure. Otherwise, if the number of sensors is enough and the structure does not present a complicate geometry, it’s usually sufficient to detect only the principal first modes. This two problems are well presented in the works of Balsamo [1] for the application to a simple system and Jun [2] for the analysis of system with a limited number of sensors. Once the system identification has been carried, it is possible to access the actual system characteristics. A frequent practice is to create an updated FEM model and assess whether the structure fulfills or not the requested functions. Once again the objective of this work is to present a general methodology to analyze big structure using a limited number of instrumentation and at the same time, obtaining the most information about an identified structure without recalling methodologies of difficult interpretation. A general framework of the state space identification procedure via OKID/ERA algorithm is developed and implemented in Matlab. Then, some simple examples are proposed to highlight the principal characteristics and advantage of this methodology. A new algebraic manipulation for a prolific use of substructuring results is developed and implemented.

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Huang, Changwu. „Kriging-assisted evolution strategy for optimization and application in material parameters identification“. Thesis, Normandie, 2017. http://www.theses.fr/2017NORMIR05.

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Afin de réduire le coût de calcul pour des problèmes d'optimisation coûteuse, cette thèse a été consacrée à la Stratégie d'Evolution avec Adaptation de Matrice de Covariance assistée par modèle de Krigeage (KA-CMA-ES). Plusieurs algorithmes de KA-CMA-ES ont été développés et étudiés. Une application de ces algorithmes KA-CMA-ES développés est réalisée par l'identification des paramètres matériels avec un modèle constitutif d'endommagement élastoplastique. Les résultats expérimentaux démontrent que les algorithmes KA-CMA-ES développés sont plus efficaces que le CMA-ES standard. Ils justifient autant que le KA-CMA-ES couplé avec ARP-EI est le plus performant par rapport aux autres algorithmes étudiés dans ce travail. Les résultats obtenus par l'algorithme ARP-EI dans l'identification des paramètres matériels montrent que le modèle d'endommagement élastoplastique utilisé est suffisant pour décrire le comportement d'endommage plastique et ductile. Ils prouvent également que la KA-CMA-ES proposée améliore l'efficace de la CMA-ES. Par conséquent, le KA-CMA-ES est plus puissant et efficace que CMA-ES pour des problèmes d'optimisation coûteuse
In order to reduce the cost of solving expensive optimization problems, this thesis devoted to Kriging-Assisted Covariance Matrix Adaptation Evolution Strategy (KA-CMA-ES). Several algorithms of KA-CMA-ES were developed and a comprehensive investigation on KA-CMA-ES was performed. Then applications of the developed KA-CMA-ES algorithm were carried out in material parameter identification of an elastic-plastic damage constitutive model. The results of experimental studies demonstrated that the developed KA-CMA-ES algorithms generally are more efficient than the standard CMA-ES and that the KA-CMA-ES using ARP-EI has the best performance among all the investigated KA-CMA-ES algorithms in this work. The results of engineering applications of the algorithm ARP-EI in material parameter identification show that the presented elastic-plastic damage model is adequate to describe the plastic and ductile damage behavior and also prove that the proposed KA-CMA-ES algorithm apparently improve the efficiency of the standard CMA-ES. Therefore, the KA-CMA-ES is more powerful and efficient than CMA-ES for expensive optimization problems

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Spohrer, Klaus. „The water regime in a lychee orchard of Northern Thailand : identification of model parameters for water balance modelling /“. Stuttgart : Univ. Hohenheim, Inst. für Bodenkunde und Standortlehre, 2007. http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&doc_number=016421055&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA.

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Bücher zum Thema "Thevenin model Identification of parameters"

McCleary, Richard, David McDowall und Bradley J. Bartos. Noise Modeling. Oxford University Press, 2017. http://dx.doi.org/10.1093/oso/9780190661557.003.0003.

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Chapter 3 introduces the Box-Jenkins AutoRegressive Integrated Moving Average (ARIMA) noise modeling strategy. The strategy begins with a test of the Normality assumption using a Kolomogov-Smirnov (KS) statistic. Non-Normal time series are transformed with a Box-Cox procedure is applied. A tentative ARIMA noise model is then identified from a sample AutoCorrelation function (ACF). If the sample ACF identifies a nonstationary model, the time series is differenced. Integer orders p and q of the underlying autoregressive and moving average structures are then identified from the ACF and partial autocorrelation function (PACF). Parameters of the tentative ARIMA noise model are estimated with maximum likelihood methods. If the estimates lie within the stationary-invertible bounds and are statistically significant, the residuals of the tentative model are diagnosed to determine whether the model’s residuals are not different than white noise. If the tentative model’s residuals satisfy this assumption, the statistically adequate model is accepted. Otherwise, the identification-estimation-diagnosis ARIMA noise model-building strategy continues iteratively until it yields a statistically adequate model. The Box-Jenkins ARIMA noise modeling strategy is illustrated with detailed analyses of twelve time series. The example analyses include non-Normal time series, stationary white noise, autoregressive and moving average time series, nonstationary time series, and seasonal time series. The time series models built in Chapter 3 are re-introduced in later chapters. Chapter 3 concludes with a discussion and demonstration of auxiliary modeling procedures that are not part of the Box-Jenkins strategy. These auxiliary procedures include the use of information criteria to compare models, unit root tests of stationarity, and co-integration.

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Buchteile zum Thema "Thevenin model Identification of parameters"

Gorokhovski, Vikenti. „Model Identification“. In Effective Parameters of Hydrogeological Models, 39–63. Cham: Springer International Publishing, 2014. http://dx.doi.org/10.1007/978-3-319-03569-7_4.

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Kozlowski, Krzysztof. „Identification of robot model parameters“. In Advances in Industrial Control, 101–30. London: Springer London, 1998. http://dx.doi.org/10.1007/978-1-4471-0429-2_4.

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Doyle, F. J., R. K. Pearson und B. A. Ogunnaike. „Determination of Volterra Model Parameters“. In Identification and Control Using Volterra Models, 79–103. London: Springer London, 2002. http://dx.doi.org/10.1007/978-1-4471-0107-9_4.

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Cambraia, Heraldo N., Leonardo M. L. Contini und Paulo R. G. Kurka. „Operational Modal Parameters Identification Using the ARMAV Model“. In Lecture Notes in Mechanical Engineering, 155–67. Cham: Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-91217-2_11.

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Fardmoshiri, M., M. Sasso, E. Mancini, G. Chiappini und M. Rossi. „Identification of Constitutive Model Parameters in Hopkinson Bar Tests“. In Residual Stress, Thermomechanics & Infrared Imaging, Hybrid Techniques and Inverse Problems, Volume 9, 189–98. Cham: Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-42255-8_23.

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Hong, Q. Y., Sam Kwong und H. L. Wang. „Optimization of Gaussian Mixture Model Parameters for Speaker Identification“. In Genetic and Evolutionary Computation – GECCO 2004, 1310–11. Berlin, Heidelberg: Springer Berlin Heidelberg, 2004. http://dx.doi.org/10.1007/978-3-540-24855-2_141.

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Zou, Hang, Wei Zhang, Junyi Zuo, Xiaodan Chen und Yawen Cao. „EM-Based Online Identification Algorithm for Linear Aerodynamic Model Parameters“. In Lecture Notes in Electrical Engineering, 2249–58. Singapore: Springer Singapore, 2019. http://dx.doi.org/10.1007/978-981-13-3305-7_182.

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Van den Hof, Paul M. J., Jorn F. M. Van Doren und Sippe G. Douma. „Identification of Parameters in Large Scale Physical Model Structures, for the Purpose of Model-Based Operations“. In Model-Based Control:, 125–43. Boston, MA: Springer US, 2009. http://dx.doi.org/10.1007/978-1-4419-0895-7_8.

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Yamamoto, Takashi, Shinichi Maruyama, Kazuhiro Izui und Shinji Nishiwaki. „Identification of Material Parameters in Biot’s Model by the Homogenization Method“. In Topics in Modal Analysis II, Volume 6, 43–52. New York, NY: Springer New York, 2012. http://dx.doi.org/10.1007/978-1-4614-2419-2_5.

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Braiek, Sonia, Ated Ben Khalifa, Redouane Zitoune und Mondher Zidi. „Model Parameters Identification of Adhesively Bonded Composites Tubes Under Internal Pressure“. In Lecture Notes in Mechanical Engineering, 603–13. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-27146-6_65.

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Konferenzberichte zum Thema "Thevenin model Identification of parameters"

Peng, Wei, Zhengqiu Yang, Chen Liu, Jiapeng Xiu und Zheng Zhang. „An Improved PSO Algorithm for Battery Parameters Identification Optimization Based on Thevenin Battery Model“. In 2018 5th IEEE International Conference on Cloud Computing and Intelligence Systems (CCIS). IEEE, 2018. http://dx.doi.org/10.1109/ccis.2018.8691341.

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Locorotondo, Edoardo, Luca Pugi, Lorenzo Berzi, Marco Pierini und Giovanni Lutzemberger. „Online Identification of Thevenin Equivalent Circuit Model Parameters and Estimation State of Charge of Lithium-Ion Batteries“. In 2018 IEEE International Conference on Environment and Electrical Engineering and 2018 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I&CPS Europe). IEEE, 2018. http://dx.doi.org/10.1109/eeeic.2018.8493924.

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Huang, Cong-Sheng, und Mo-Yuen Chow. „Accurate Thevenin's circuit-based battery model parameter identification“. In 2016 IEEE 25th International Symposium on Industrial Electronics (ISIE). IEEE, 2016. http://dx.doi.org/10.1109/isie.2016.7744902.

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Chen, Xinnan, Yuan Sun und Rui Yin. „An Improvement Algorithm for Online Identification of Thevenin Equivalent Parameters“. In 2021 3rd Asia Energy and Electrical Engineering Symposium (AEEES). IEEE, 2021. http://dx.doi.org/10.1109/aeees51875.2021.9403072.

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Xinyuan, Meng, Wen Tao und Ma Kaigang. „Application of Python Parallel Computing in Online Identification of Thevenin Equivalent Parameters“. In 2020 IEEE Student Conference on Electric Machines and Systems (SCEMS). IEEE, 2020. http://dx.doi.org/10.1109/scems48876.2020.9352354.

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Liu, Youbo, Zhuoyi Li, Yue Yang und Junyong Liu. „A novel on-line identification for Thevenin equivalent parameters of power system regarding persistent disturbance condition“. In 2016 China International Conference on Electricity Distribution (CICED). IEEE, 2016. http://dx.doi.org/10.1109/ciced.2016.7575909.

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Jianwei Zhao, Zheng Yan, Lu Cao und Jianhua Li. „Study on Thevenin equivalent model and algorithm of AC/DC power systems for voltage instability identification“. In 2014 International Conference on Power System Technology (POWERCON). IEEE, 2014. http://dx.doi.org/10.1109/powercon.2014.6993794.

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ALLEN, JAMES, und DAVID MARTINEZ. „Automating the identification of structural model parameters“. In 30th Structures, Structural Dynamics and Materials Conference. Reston, Virigina: American Institute of Aeronautics and Astronautics, 1989. http://dx.doi.org/10.2514/6.1989-1242.

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Vesely, Ivo, und Lukas Pohl. „Parameters identification of PMSM through Hammerstein model“. In IECON 2013 - 39th Annual Conference of the IEEE Industrial Electronics Society. IEEE, 2013. http://dx.doi.org/10.1109/iecon.2013.6699612.

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Cipin, Radoslav, Marek Toman, Petr Prochazka und Ivo Pazdera. „Identification of Li-ion Battery Model Parameters“. In 2019 International Conference on Electrical Drives & Power Electronics (EDPE). IEEE, 2019. http://dx.doi.org/10.1109/edpe.2019.8883926.

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