69 results about "Circuit models" patented technology

The application discloses a kind of generator stator winding partial discharge quantity's inversion method and system, belong to large-scale rotating electrical machine insulation state diagnostic technical field, method includes: obtaining or estimating the key distributed capacitance parameter in insulation structure multi-capacitance equivalent circuit model, mapping relationship database is established;Partial discharge detection is carried out to the generator stator winding of applied test voltage, and the characteristic information of discharge pulse is collected;Based on the key distributed capacitance parameter in mapping relationship database, pre-generated pulse waveform-defect capacitance calibration curve, the characteristic information of discharge pulse collected is executed nonlinear iterative inversion calculation;Quantitative evaluation, type identification and health diagnosis are carried out to the insulation defect of generator stator winding.The application introduces pulse waveform feature into partial discharge physical quantity inversion, improves the accuracy, quantitative level and defect identification ability of generator stator insulation state evaluation, provides technical support for generator life extension and condition maintenance.

This application discloses a simulation device, decoupling method, equipment, and system for new energy power plants, relating to the field of power grid decoupling technology. Each DC-side circuit model is assigned to its corresponding first acceleration unit for computation. All sub-filter circuit models of the filter circuit model are assigned to a second acceleration unit for computation. This overcomes the limitations of computing power in a single acceleration unit and the number of subsequent I / O hardware interfaces. The first decoupling unit decouples the DC-side and AC-side filter circuit models of each converter, avoiding numerical oscillations. Assigning the filter circuit models to the second acceleration unit avoids filtering out high-frequency information, ensuring the decoupling of high-frequency information. The input of the second decoupling unit is connected to the output of each sub-filter circuit model, and the output of the second decoupling unit is connected to each busbar in the model of the power plant's line equipment. The second decoupling unit decouples the converter group units and the power plant's line equipment, achieving simulation decoupling and improving simulation accuracy.

This invention discloses a nonlinear dynamic modeling method for crack evolution in a helical gear transmission system for vehicles, which includes the following steps: (1) establishing a calculation formula for meshing stiffness of the helical gear pair under cracked conditions using the slice integration method and the potential energy method; (2) establishing an equivalent circuit model of the motor by combining the principles of circuit theory and magnetic circuit theory with coordinate transformation relationship; (3) building an electromechanical coupling model of the motor-gear system based on the mechanical relationship between the motor and the gear transmission system; (4) establishing a model of the electromechanical coupling helical gear transmission system for vehicles and solving the influence mechanism of crack evolution of helical gears on the dynamic characteristics of the electromechanical coupling helical gear transmission system for vehicles; the beneficial effect is that this invention establishes a nonlinear fault dynamic model of the electromechanical coupling transmission system for new energy vehicles, which is of great significance for the design, optimization and life prediction of the helical gear transmission system for new energy vehicles.

The application discloses a transformer operation state self-diagnosis method and system, relates to the technical field of state diagnosis, and comprises the following steps: acquiring and analyzing relevant data in the working state according to test conditions; judging the primary side circuit element state according to the primary side data and an RLC circuit step response time constant formula; calculating the voltage stability according to the secondary side data; calculating the delay duration of the tap switch and the damage degree of the tap switch according to the primary side data; dividing and outputting the state grade; and predicting the first fault time point according to the delay duration and a preset delay duration. The application realizes predictive maintenance by predicting the first fault time point, avoids power failure accidents caused by sudden failures, comprehensively integrates various data of the primary side and the secondary side, combines specific analysis of a circuit model and key components, forms a multi-dimensional and systematic diagnosis system, and realizes comprehensive, intelligent and fine management of the transformer operation state.

This application provides a method, device, and medium for identifying parameters of a second-order battery equivalent circuit model. The method includes: randomly initializing the positions of an osprey population within a defined parameter feasible solution space to obtain the initial position of each osprey. Each osprey's position represents a set of candidate parameter feasible solutions, with parameters including the ohmic internal resistance, two polarization resistors, and two polarization capacitors in the second-order battery equivalent circuit model; executing an iterative loop of the osprey optimization algorithm based on the initial position of each osprey to obtain the optimal position; and determining the parameter feasible solution corresponding to the optimal position as the optimal solution for the parameters of the second-order battery equivalent circuit model. During the iterative loop of the osprey optimization algorithm, the update of the osprey positions is controlled by dynamic coefficients, which change non-linearly with the number of iterations. This improves the identification accuracy and reliability of the second-order battery equivalent circuit model parameters.

The application relates to a lithium ion battery equivalent circuit model construction method, system, device and medium, and belongs to the lithium ion battery equivalent circuit model construction field. The method comprises the following steps: constructing an initial lithium ion battery equivalent circuit model of a lithium ion battery to be modeled; determining the structure of a second lithium ion battery equivalent circuit model by using the population chromosome coding of a genetic algorithm based on the initial lithium ion battery equivalent circuit model, so as to obtain a plurality of different second lithium ion battery equivalent circuit models; optimizing the second lithium ion battery equivalent circuit model by using a particle swarm algorithm, so as to determine the optimal model parameters of a third lithium ion battery equivalent circuit model; optimizing the structures of the plurality of third lithium ion battery equivalent circuit models by using a genetic algorithm, so as to determine the optimal structure of the lithium ion battery equivalent circuit model, and obtaining the lithium ion battery equivalent circuit model. The application improves the accuracy of model construction.

The application discloses a kind of GaN HEMT equivalent circuit model parameter self-adapting extraction method and system, comprising: extracting parasitic parameter value by small signal equivalent circuit parasitic parameter extraction method, establishes initial population;Stripping parasitic parameter in initial population obtains each bias eigenvalue by network analysis, and simulation equivalent circuit S parameter data is obtained by calculation;Judgment is calculated according to S parameter data individual fitness;New individual is generated to replace singular individual in population until population is non-singular;According to the search range of adjustment algorithm of non-singular population;According to the individual selection, crossover, variation rule set to the individual in initial population is carried out genetic calculation, and new genetic individual is obtained;Whether the preset target is satisfied is judged, and the element parameter corresponding to optimal individual is output as the final parameter of small signal model;I-V model is optimized by genetic algorithm, and the weight matrix of fitness function is updated according to model error, and optimization is carried out again to meet the preset target, and the small signal model parameter is combined to establish large signal equivalent circuit model.The application greatly improves the modeling speed of small signal model and the accuracy of large signal model without sacrificing accuracy, which provides key technical support for reliable design of high-power, high-frequency microwave circuit.

This invention belongs to the field of integrated circuit design, specifically relating to a method for predicting S-parameters of chip interconnects based on hierarchical parasitic modeling. It aims to address the problems of weak generalization ability of machine learning methods in chip interconnect signal integrity analysis and the neglect of electromagnetic coupling effects caused by the switching of reference plane return current between vertical interconnects and horizontal traces, resulting in insufficient accuracy and poor adaptability of S-parameter prediction. The solution includes: normalizing the layout geometry description of the chip high-frequency interconnect structure, decomposing and mapping it into a standardized parallel bus substructure; constructing an initial equivalent circuit model based on a parasitic parameter extraction model; optimizing and correcting the initial parasitic parameters using a neural network parasitic parameter adapter, wherein the transmission line adapter corrects the parameters through high-order nonlinear transformation and projection function, and the vertical interconnect adapter jointly models the electromagnetic coupling effect between vertical interconnects and transmission line segments; injecting the corrected high-fidelity parasitic parameters into the circuit model, and generating S-parameters through circuit simulation.

This application discloses a method, device, terminal, and storage medium for simulating the electrical characteristics of integrated circuits. The method includes acquiring a model of the integrated circuit to be simulated; meshing the model to obtain an integrated circuit mesh dataset composed of multiple mesh cells; constructing corresponding charge basis functions and current basis functions based on the mesh cells; using the charge basis functions as the fusion object, progressively merging and updating the charge basis functions; simultaneously, merging and updating the current basis functions through geometric mapping; after all fusion and updates are completed, acquiring the integrated circuit basis function dataset; and performing electrical characteristic simulation on the integrated circuit to be simulated based on the integrated circuit basis function dataset. This achieves rapid updating and low-order representation of complex coupling capacitance matrices, reducing the theoretical complexity from O(n^2) to O(n^2) of traditional back-end order reduction methods. 3 The time complexity is reduced to O(n), while the model accuracy and order reduction effect are maintained, and the efficiency of model order reduction is greatly improved.

This invention discloses a modeling method for a new energy power plant simulator, relating to the field of power system simulation technology. Specifically, it includes the following steps: S1, developing and debugging a digital encapsulation model using ADPSS software on both the Windows and Linux platforms; S2, based on the established digital encapsulation model, connecting the new energy controller hardware to the simulator equipment within a simulation environment, and conducting relevant simulation modeling work; S3, obtaining the primary circuit topology and parameters of the new energy unit or SVG, and establishing a primary circuit model based on the primary circuit topology and parameters using the ADPSS simulation platform. The new energy power plant simulator modeling method provided by this invention only requires modeling, simulation, and debugging on one ADPSS simulation platform. Theoretically, all other platforms can be directly accessed by the platform user using the ADPSS-tested control encapsulation model, without requiring participation in model development, greatly reducing workload and complexity.

This invention proposes a method for enhancing the stability of power hardware-in-the-loop systems based on DDPG-RP. First, an equivalent circuit model of the system containing grid fault disturbances is established, its dynamic interaction characteristics are analyzed, and the impedance matching conditions for stable operation are determined. Based on this, a phase-compensated and enhanced repetitive-voltage feedforward composite controller is designed. An upper-level intelligent controller is constructed by combining this with a deep deterministic policy gradient algorithm. These two controllers are deployed in the bottom and upper-level control units, respectively, forming a hierarchical collaborative control architecture for grid fault simulation. A test platform is built based on this architecture. After offline training and online deployment of the deep reinforcement learning controller, a three-phase short-circuit fault condition is simulated using a high-power grid-connected interface device to verify its dynamic response and stability. This invention, relying on hierarchical collaboration and intelligent optimization, effectively solves the problem of traditional control systems struggling to balance stability and performance under complex faults, significantly improving system adaptability and robustness.

The present application relates to the technical field of dynamic wireless power transfer (DWPT), and especially relates to a DWPT system electromagnetic exposure evaluation method and system considering human activity, aiming at the problem that the existing static evaluation method is difficult to take into account the time-varying characteristics of dynamic system and the difference of human activity distribution, the present application derives the time domain expression of the receiving coil current through establishing a circuit model, quantifies the influence of speed and load on the dynamic change of current; the electromagnetic region is classified into alert zone and waiting zone, and multiple height monitoring planes are set to correspond to the main organs of the human body; the magnetic field data set is obtained by laying measuring points, the regional average magnetic induction intensity, the maximum magnetic induction intensity and the magnetic field energy are calculated as the evaluation target; based on the rank correlation coefficient and the mutual information value, the redundant measuring points are removed, the core measuring point combination is screened by combining the NSGA-II multi-objective optimization, and the multi-objective high-precision synchronous prediction is realized by using the second-order ridge regression, so that the accurate characterization and low-cost measurement of dynamic electromagnetic exposure are realized.

The invention discloses a mobile self-adaptive charging control and thermal control method and system, and belongs to the technical field of thermoelectric control systems.The method comprises the steps that a charging state sensing matrix is constructed by collecting multi-dimensional data such as electric quantity, battery health, environment temperature and load in real time; a neural network model fusing a space-time attention mechanism and a bidirectional LSTM is adopted to accurately identify a charging demand, and a dynamic strategy library is matched to generate an initial charging parameter; in the charging process, the state of a loop is monitored through double-end synchronous sampling larger than 1 kHz, the current and voltage are dynamically optimized in combination with a battery equivalent circuit model of the self-adaptive order, and efficient power matching and ripple suppression are achieved. And when it is detected that the temperature is abnormal, a multi-mode thermal management mechanism is triggered, and a charging-calculation-heat dissipation cooperative control closed loop is formed. The system also supports security OTA strategy updating based on federated learning and differential privacy; according to the invention, the charging efficiency, the safety and the service life of the battery are remarkably improved, and the environment adaptability in a complex mobile scene is enhanced.

A single-phase PWM inverter circuit non-intrusive parameter identification method, in which a PWM inverter circuit model is established, a space state equation set of the PWM inverter circuit model is written, and a 4th order Runge-Kutta method is used to calculate the value of the space state equation set; an evaluation function is constructed, an optimization particle swarm optimization algorithm is used for optimization, the coordinate dimension of a particle is 4, the minimum value of the evaluation function is found, the particle coordinate corresponding to the minimum value of the evaluation function is found, and the coordinate is updated as the identified circuit parameter under the state. f rmse A single-phase PWM inverter circuit non-intrusive parameter identification method, in which a PWM inverter circuit model is established, a space state equation set of the PWM inverter circuit model is written, and a 4th order Runge-Kutta method is used to calculate the value of the space state equation set; an evaluation function is constructed, an optimization particle swarm optimization algorithm is used for optimization, the coordinate dimension of a particle is 4, the minimum value of the evaluation function is found, the particle coordinate corresponding to the minimum value of the evaluation function is found, and the coordinate is updated as the identified circuit parameter under the state.

The present disclosure provides a system (210) and a method (400) for predicting geometric conflicts between vehicle components. The method (400) includes collecting (410), by a controller (208) associated with the system (210), data associated with at least two components of the vehicle from a data system. The method (400) includes extracting (420), by the controller (208), one or more features of the at least two components from the collected data. The method (400) includes training (450), by the controller (208), a quantum circuit model based on the one or more features of the at least two components. The method (400) includes predicting (460), by the controller (208), the geometric conflicts between the at least two components via the quantum circuit model.

This invention discloses a method and system for estimating the State of Charge (SOC) of an electric aircraft battery. The method first constructs a second-order RC equivalent circuit model and sets up a two-dimensional lookup table module. By inputting the real-time operating temperature and the estimated SOC value into the lookup table module, the corresponding open-circuit voltage value is obtained and assigned to the equivalent circuit model. Then, based on this model, an extended Kalman filter algorithm is applied to estimate the SOC value in real time. During algorithm execution, the real-time current and temperature of the battery are monitored, and the process noise covariance matrix and observation noise covariance matrix are dynamically adjusted according to the current change rate and temperature change rate to optimize the SOC estimation result. This invention effectively overcomes the technical defects of existing second-order RC models, such as significant polarization effects and insufficient dynamic response under extreme temperatures, through a temperature-compensated two-dimensional lookup table module. Combined with an adaptively adjusted extended Kalman filter algorithm, it significantly improves the estimation accuracy and reliability of the electric aircraft battery management system under complex operating conditions.

The application discloses a modeling method and device of a traction power supply system. The method comprises the following steps: using energy macro-expression modules to represent each component in the traction power supply system, so as to build a main circuit model, wherein each energy macro-expression module has an input port, an output port and a causal relationship; determining an adjustment path in the main circuit model according to a control target; and performing reverse step-by-step deduction according to the control target and the adjustment path, so as to deduce a control instruction required for realizing the control target. The application can accurately clarify the interaction relationship between each component, clearly represent the energy flow path of the system, and improve the simulation speed.

This application provides a method, apparatus, and electronic device for estimating the state of charge (SOC) of a battery pack, relating to the field of battery technology. The method includes: first, acquiring the dynamic current and dynamic terminal voltage of the battery pack when it is under dynamic operating conditions; then, estimating the basic SOC of the battery pack using an equivalent circuit model based on the dynamic current and dynamic terminal voltage, where the equivalent circuit model represents the dynamic mapping relationship between the dynamic current, dynamic terminal voltage, and SOC; further, determining the SOC deviation of the battery pack using a first deviation prediction model based on the basic SOC, dynamic current, and dynamic terminal voltage, where the first deviation prediction model is used to compensate for the estimation error generated by the equivalent circuit model under dynamic operating conditions; finally, determining the estimated SOC value of the battery pack based on the basic SOC and the SOC deviation. This application can adapt to the dynamic operating conditions of the battery pack, compensate for model estimation errors, and improve the accuracy of SOC estimation.

This application relates to the field of lithium battery testing technology, and discloses a method, apparatus, electronic device, and storage medium for detecting the internal resistance of lithium batteries. The method for detecting the internal resistance of lithium batteries includes: performing pulse charge-discharge tests on the battery under test to obtain voltage response data of the battery under test under different states of charge; substituting the voltage response data into a pre-established equivalent circuit model of the lithium battery, and obtaining the internal resistance parameters in the equivalent circuit model of the lithium battery through parameter identification processing; and establishing a predictive model of the internal resistance with respect to the state of charge based on the internal resistance parameters obtained under different states of charge. This application uses software to process test data to replace the direct measurement of signals by physical hardware, thereby greatly simplifying the complexity of system wiring, integration, and maintenance at the system architecture level.

The application provides a feature-driven method and system for estimating the health state of a retired power battery, and relates to the field of battery health state estimation.The method comprises the following steps: constructing an algorithm library and presetting the capability vector of each algorithm in the algorithm library; acquiring the voltage and current of the retired power battery at the current time, combining a preset battery equivalent circuit model to acquire the feature vector at the current time; based on the feature vector, calculating the matching score of each algorithm in the algorithm library through a dynamic weighted scoring mechanism, and determining the current execution algorithm according to the matching score; using the current execution algorithm to perform parameter identification on the battery equivalent circuit model, obtaining the optimal model parameters, and calculating the current health state of the retired power battery according to the optimal model parameters; and calculating the convergence performance of the current execution algorithm to update the historical credibility of the algorithm in the algorithm library, so as to be used for the matching score calculation at the next time.The application can adapt to various working conditions, and improve the estimation accuracy and convergence speed.

This invention belongs to the technical field of high-field magnet structure design, specifically involving an adaptive selection method for simulation type in pulse magnet structure design. The method includes: establishing a model of the pulse magnet; using a circuit model to calculate the actual current waveform of the magnet at the required power supply voltage; importing this waveform into an electromagnetic finite element model to calculate the peak electromagnetic load of the magnet; applying this load as a single step load to the structural finite element model; extracting the stress-time curve of each target component; and extracting the fundamental component of this curve using Fourier transform to determine the natural vibration frequency of the component. ω m Perform a Fourier transform on the actual current waveform to determine its fundamental component. ω i If all target components ω m All are greater than N × ω i If a steady-state simulation evaluation is selected, then this invention can solve the problem of the lack of a standardized operating system in the existing pulse magnet structure design that can reasonably use static analysis results to approximate transient analysis results.

The present application relates to a kind of lithium battery multi-time scale model parameter identification method based on time domain separation in new energy automobile battery management system technical field, based on the analysis voltage recovery curve, the corresponding relationship between macroscopic voltage response characteristic and microcosmic multi-time scale electrochemical mechanism is established, the separation basis of fast dynamic and slow dynamic is proposed;With bipolarization equivalent circuit model as object, introduce fast dynamic and slow dynamic separation point information, construct its prior relationship with state of charge, and introduce it into parameter identification process;Under the prior constraint condition, establish time domain separation recursive least square model parameter identification framework, realize the low-dimensional, weakly coupled separation estimation of fast dynamic and slow dynamic parameters, to improve model precision.The present application fully considers the significant multi-time scale effect inside battery, has the advantages of high model precision, good stability and fast online calculation speed, provides reliable model basis for high-precision estimation and optimization management control of battery state.

This invention provides a joint prediction method for state of charge (SOC) and state of power (SOP) of a battery, comprising: collecting voltage, current, temperature, and capacity data of a lithium-ion battery under different operating conditions and temperatures; constructing multi-temperature SOC-open-circuit voltage relationship curves; extracting health features characterizing the degree of battery aging; establishing battery health status characterization quantities; establishing a second-order resistive-capacitive equivalent circuit model of the lithium-ion battery to achieve battery SOC prediction; co-optimizing the FFRLS forgetting factor and MIAUKF key parameters; comprehensively considering temperature constraints, voltage constraints, and SOC constraints in SOP prediction, and coupling the health status characterization quantities into the power prediction process to construct a two-layer network model to achieve high-precision prediction of battery SOP. This invention can effectively improve the accuracy, convergence, and robustness of SOC prediction under complex operating conditions, noise variations, and temperature fluctuations.

The application discloses a method and system for evaluating the influence of direct current bias voltage on dielectric accumulation effect, and builds a dielectric direct current superimposed impulse test circuit model; the maximum and minimum direct current bias voltages that an insulating dielectric receives during operation of corresponding electrical equipment are obtained; the minimum direct current bias voltage is applied to the insulating dielectric, and a superimposed impulse voltage test of the insulating dielectric is performed through the model to obtain a U-N characteristic curve of the dielectric accumulation effect under the direct current bias voltage; the direct current bias voltage is increased by a step of delta Udc to obtain U-N characteristic curves of the dielectric accumulation effect under different bias voltages; the U-N characteristic curves of the dielectric accumulation effect obtained under different bias voltages are analyzed by fitting to obtain an evaluation standard of the bias voltage on the dielectric accumulation effect; the application has the beneficial effect of increasing the accuracy of the evaluation result of the insulating dielectric accumulation effect under the direct current voltage.

This invention relates to the field of power systems and their automation technology. It provides a method for constructing a traction network simulation model based on current distribution characteristics. The method includes: establishing multi-conductor transmission line equations based on Maxwell's equations, including Gauss's law and Faraday's law; deriving a chain-like network model of the traction network based on the multi-conductor transmission line equations and a chain circuit model with multi-conductor transmission lines as the backbone and randomly connected transverse elements; simplifying the chain-like network model to obtain a simplified series impedance matrix equation and a parallel admittance parameter matrix after conductor merging; and calculating the series impedance and parallel admittance values ​​using the simplified series impedance matrix equation and the parallel admittance parameter matrix after conductor merging. This invention improves the accuracy and applicability of traction network electrical simulation and signal analysis.

This invention discloses a method for interphase battery charge balancing in a three-phase cascaded H-bridge based on model predictive control. A simulation circuit model of a 7-level cascaded H-bridge grid-connected inverter based on model predictive control was built in Matlab / Simulink simulation software, and the simulation results were recorded. The DC-side voltage vo of each H-bridge unit of the inverter is specified. dc The value is 150V, and the mains voltage is v. g The values ​​are set to 220V, the fundamental frequency to 50Hz, the sampling time T for model predictive control to 0.1ms, the grid-connected filter inductor L to 1mH, and the equivalent resistance R of the grid-connected filter inductor to 0.1Ω. The SOC is set... a =70.1%, SOC b =70%, SOC c =69.9%, ε=0.01, the grid-connected filter inductor L is 1mH, the equivalent resistance R of the grid-connected filter inductor is 0.1Ω, the grid-connected current is controlled by model predictive control, and a reward function is introduced to select redundant voltage vectors to balance the phase SoC without affecting the current performance. After the error of the SoC is less than a certain given value, the SoC balancing is stopped to achieve the minimum common mode voltage.

This invention provides a bond portfolio management method, apparatus, storage medium, and electronic device. The method includes: upon receiving a bond portfolio management request from a client, determining the target service category corresponding to the request; identifying a target obfuscated circuit model from among multiple constructed obfuscated circuit models; the target obfuscated circuit model being a model constructed based on a financial engineering model corresponding to the target service category; sending the target obfuscated circuit model to the client, enabling the client to process the bond portfolio data using the target obfuscated circuit model to obtain model output results; receiving the model output results; determining the bond portfolio management result corresponding to the model output results; and sending the bond portfolio management result to the client. By applying this method, no financial engineering model needs to be used during the service process, and the user's bond portfolio data does not need to be exposed to the server, thus protecting model data security and user data privacy.