601 results about "Circuit models" patented technology

The invention belongs to the technical field of battery management, and relates to a battery equivalent circuit model parameter identification method and system based on particle swarm optimization, and the method comprises the steps: obtaining the dynamic test data of a battery; an OCV-SOC relation is obtained; calculating a real-time SOC sequence and performing interpolation to obtain a search OCV sequence; defining a second-order RC equivalent circuit model; constructing an objective function based on an error between prediction of the OCV sequence and search of the OCV sequence; and searching an optimal model parameter by adopting a particle swarm optimization algorithm and taking minimization of the target function as a target. By optimizing the matching degree of predicting the OCV and searching the OCV instead of directly fitting the terminal voltage, the sensitivity and accuracy of parameter identification on the dynamic characteristics of the battery are improved, and efficient and accurate identification on the parameters of the second-order RC model is realized in combination with the global optimization capacity and robustness design of PSO (Particle Swarm Optimization).

The invention provides an electromagnetic compatibility test method and system for PCB design, a PCB comprises a schematic circuit diagram and a structural model, and the method comprises the following steps: extracting high-frequency signal characteristics in the schematic circuit diagram to obtain an electromagnetic simulation parameter set; performing multi-physical field coupling modeling according to the electromagnetic simulation parameter set and the structure model to obtain an electromagnetic field-circuit model; performing frequency-time domain hybrid excitation loading on the electromagnetic field-circuit model to obtain an electromagnetic noise distribution diagram; performing gradient descent optimization on layout parameters of the structure model according to the electromagnetic noise distribution diagram to obtain an anti-interference layout scheme; and performing multi-working-condition Monte Carlo simulation and residual error calibration on the anti-interference layout scheme to generate a correction guidance library.

A method for estimating current-dependent non-linear equivalent circuit model (ECM) parameters of a battery may include measuring a battery voltage across terminals of the battery and a battery current drawn from the battery, modelling behavior of the battery with an impedance model having impedance model parameters, dynamically analyzing the battery current drawn from the battery by a load, dynamically determining safe operating limits for an augmented current for augmenting the current drawn by the load, based on analysis of the battery current and the safe operating limits, determining the augmented current, generating the augmented current to be drawn from the battery, and estimating the impedance model parameters when the augmented current is drawn from the battery.

The invention discloses a power transformer winding insulation state monitoring method, a power transformer winding insulation state monitoring device, power transformer winding insulation state monitoring equipment and a medium, and relates to the technical field of power system equipment state monitoring. And an improved particle swarm optimization algorithm is adopted to inversely calculate the insulation parameters of a plurality of windings of the transformer to the ground, so that the insulation state of each winding can be accurately evaluated. Compared with a traditional monitoring method, the method has the advantages that rich information contained in the common-mode leakage current can be fully utilized, the insulation degradation conditions of different windings can be accurately distinguished, more accurate insulation state evaluation is provided, and the safe operation level of the transformer is effectively improved. Moreover, multi-stage evaluation is carried out on the insulation state through a fuzzy comprehensive evaluation method, the insulation state can be comprehensively reflected, different severity degrees of the insulation state can be distinguished, the current state can be evaluated, the degradation trend can be predicted through dynamic indexes, and a scientific basis is provided for operation and maintenance decision making.

The invention provides a high-frequency circulating current calculation method for a motor simulator cascade coupling parallel structure, and the method comprises the following steps: building an equivalent decoupling circuit model of each branch in the cascade coupling parallel structure based on the voltage relation of each branch in the motor simulator cascade coupling parallel structure, wherein the equivalent decoupling circuit of each branch comprises an equivalent voltage source and an equivalent leakage inductor which are connected in series, and the equivalent leakage inductor is determined based on self-inductance and mutual inductance of a choking coil of each branch; a plurality of duty ratio intervals are divided between 0 and 1, and the number of the duty ratio intervals is equal to the number of the branches in the cascade coupling parallel structure; and using the equivalent decoupling circuit model to calculate the high-frequency circulation of each corresponding branch when the duty ratio of the modulation signal of the motor simulator is in each duty ratio interval. According to the method provided by the invention, the high-frequency circulating current of the cascade coupling parallel structure of the motor simulator can be accurately calculated, and effective support is provided for targeted design of a high-frequency circulating current suppression scheme.

The invention discloses a field self-adaptive retired battery health state estimation method based on an EIS. The method comprises the following steps: firstly, extracting electrochemical impedance spectroscopy data of a retired aged battery; and then a fractional order battery equivalent circuit model is constructed, and the characterization capability of dynamic degradation of an electrode interface is enhanced through a constant phase element. Fractional order battery equivalent circuit model parameters are extracted from electrochemical impedance spectroscopy data based on nonlinear least square fitting, Pearson correlation analysis is adopted to screen parameters having a significant relationship with SOH, then a recursive feature elimination method is used for further screening, and an optimal parameter subset strongly associated with the state of health is extracted from a high-dimensional parameter set. And finally, constructing a decommissioned battery state-of-health estimation method of a field adaptive method, modeling the influence of different operation conditions on battery aging through knowledge migration, and realizing cross-working-condition decommissioned battery state-of-health estimation with both physical interpretability and working condition robustness.

The invention relates to the related technical field of distribution transformer insulation aging, in particular to a frequency domain diagnosis method, system and device for distribution transformer insulation aging and a medium, and the method comprises the steps: carrying out the frequency response test of a to-be-tested transformer, and obtaining a first frequency response curve; performing a frequency response simulation test on a transformer equivalent circuit model pre-constructed based on the to-be-tested transformer to obtain a second frequency response curve; constructing a target function based on the deviation of the first frequency response curve and the second frequency response curve; taking element parameters in the equivalent circuit model of the transformer as position vectors, taking the objective function as a fitness function, and adopting a multi-objective particle swarm algorithm to solve the element parameters to obtain optimal values of the element parameters; respectively comparing the optimal values of the element parameters with corresponding normal values to obtain insulation aging degrees; according to the method, the insulation aging degree can be obtained by comparing the element parameters obtained through optimization with the normal unaged element parameters.

The invention provides a lithium iron phosphate battery thermal runaway trend prediction method and system. The method comprises the steps that real-time operation data of a to-be-predicted lithium iron phosphate battery, initial estimated values of all parameters of an equivalent circuit model, a basic process noise covariance matrix and a basic measurement noise covariance matrix within a preset duration are acquired; determining an adjustment factor, and determining an adjusted process noise covariance matrix and an adjusted basic measurement noise covariance matrix based on the adjustment factor; optimizing the initial estimated value of each parameter of the equivalent circuit model corresponding to the lithium iron phosphate battery by using an extended Kalman filtering algorithm to obtain optimized ohm internal resistance, first-stage polarization internal resistance and second-stage polarization internal resistance; determining the temperature rise rate of the to-be-predicted lithium iron phosphate battery at the current moment t and the predicted temperature of the to-be-predicted lithium iron phosphate battery at the moment t + 1; and performing thermal runaway trend prediction on the lithium iron phosphate battery. According to the technical scheme provided by the invention, the reliability, the sensitivity and the timeliness of the prediction result are improved.

The invention discloses a battery energy storage health state detection and evaluation method and system, and relates to the technical field of battery energy storage state monitoring and evaluation, and the method comprises the steps: employing an SOC estimation method, combining an unscented Kalman filter and a Thevenin model to calculate the state of charge of a battery in real time, integrating the multi-dimensional feature data of the battery, inputting the data into an isolation forest algorithm, and carrying out the detection and evaluation of the energy storage health state of the battery. And carrying out deep anomaly detection on the battery sample with the abnormal frequency, and carrying out battery health state evaluation through a multi-stage anomaly detection result. SOC estimation is combined with an unscented Kalman filter, a Thevenin circuit model and an isolation forest algorithm to construct a self-adaptive and high-robustness multi-stage health assessment and anomaly detection system, the unscented Kalman filter performs high-precision prediction on nonlinear state transition, and the robustness of the health assessment and anomaly detection system is improved. The method effectively improves the estimation precision of the SOC under dynamic charging and discharging conditions and the accuracy of anomaly detection, and is suitable for monitoring and safety management of a battery system under complex environments and working conditions.

The invention provides an accelerated simulation solving method and system for a controllable self-recovery energy dissipation device, and the method comprises the steps: S1, enabling an inductance element between a nonlinear network and a linear network in a hybrid cascade DC power transmission system to be equivalent to a short transmission line, and building circuit models for the linear network and the nonlinear network respectively; s2, obtaining the value of the current controlled voltage source based on the node voltage of the previous simulation time step of the linear and nonlinear networks, the value of the controlled voltage source and the information interaction format; s3, node voltage solving is carried out based on the value of the controlled voltage source of the current simulation time step to obtain the node voltage of the current simulation time step, if the number of iterations is met, the operation is stopped, and otherwise, the step S2 is executed; the nonlinear network comprises a controllable self-recovery energy dissipation device; according to the invention, the energy dissipation device and the linear main circuit in the circuit system are decoupled in a sub-network manner, the calculation amount is reduced, and the iterative algorithm is used for solving, so that the high-efficiency and high-precision simulation of a nonlinear network and a linear network is ensured.

The invention discloses a relaxation time distribution-based hydrogen fuel cell equivalent circuit model establishment method, and belongs to the technical field of hydrogen fuel cells. The method comprises the following steps: acquiring full-band impedance data of the hydrogen fuel cell; performing relaxation time distribution analysis on the full-band impedance data to obtain a relaxation time relation spectrogram; identifying and constructing an initial equivalent circuit model based on characteristic peaks of different frequency bands in the relation spectrogram; the characteristic peaks at different frequency bands are used for representing different electrochemical characteristic processes of the hydrogen fuel cell; and according to the full-band impedance data and the initial equivalent circuit model, performing model parameter adjustment, and based on the adjusted model parameters, obtaining an equivalent circuit model constructed for the hydrogen fuel cell. According to the method, the high-precision and reasonable equivalent circuit model can be constructed.

Embodiments of the present application provide a verification system, method, device, medium and program, the verification system comprising: a test excitation generation module for generating a test excitation, the test excitation at least comprising serial data with clock jitter; the CDR circuit model is used for carrying out recovery processing on the input serial data and outputting a recovery result to a next-stage circuit model; the CDR jitter compensation device is used for detecting clock jitter deviation between input and output of the CDR circuit model; determining a control parameter according to the clock jitter deviation; the control parameters are fed back to the test excitation generation module; wherein the control parameter is used for adjusting the clock jitter size of the serial data generated by the test excitation generation module, so as to correct the clock jitter introduced by the CDR circuit model during the recovery processing of the serial data. According to the verification system provided by the embodiment of the invention, the clock jitter can be corrected.

The invention provides an integrated circuit model verification method and system, computer equipment and a storage medium. The method comprises the steps that parameters in a model library are analyzed through a script, and model parameters needed by verification are automatically extracted; a standard configuration file format is obtained, the model parameters are loaded into a standard configuration file, and a simulation configuration file is generated; obtaining netlist file formats required by different simulators, and generating netlist files which comprise output instructions and are adaptive to different simulators in batches according to the simulation configuration file; calling each simulator to execute the corresponding netlist file for simulation, and outputting an intermediate file; analyzing the numerical data in the intermediate file, and generating a standard format file; and comparing the standard format files generated by different simulators to obtain a verification result. According to the scheme, batch parameter configuration, netlist generation, simulation execution and result processing of the integrated circuit model can be realized, so that the model verification efficiency is improved.

The invention discloses a new energy automobile battery monitoring method and system, and relates to the technical field of new energy automobile battery monitoring, and the method comprises the steps: collecting battery operation data, initializing a population through a Halton sequence, executing preliminary VMD decomposition, calculating a Hilbert envelope, calculating an envelope entropy, and initializing a sparrow search algorithm HLSSA parameter and population classification. Updating the three populations, integrating and updating the populations, selecting cross individual pairs, executing Laplacian cross perturbation, outputting an optimal parameter group, executing final VMD decomposition, outputting an iteratively updated modal component, outputting a statistical feature vector, generating an Nyquist graph, fitting a Randles circuit model, performing feature screening, outputting an EIS feature set, and integrating the feature vector; according to the method, an SVR model is constructed to predict SOH and SOC values, VMD decomposition and envelope entropy feature analysis are used, the accuracy of feature extraction is improved, and through combination of Halton sequence initialization and sparrow search algorithm HLSSA optimization, the precision of battery health state prediction is improved.

The invention discloses a common-mode electromagnetic interference modeling and suppression method for a matrix converter, and the method comprehensively considers high-frequency parameters and topological characteristics, builds a conduction CM EMI equivalent circuit model of the matrix converter, completes the impedance network fitting of key elements and the extraction of high-frequency parasitic parameters, and achieves the modeling and suppression of the common-mode electromagnetic interference of the matrix converter. The precision of the CM EMI model of the matrix converter is improved, and the analysis of the CM EMI characteristics of the matrix converter is simplified. According to the common-mode electromagnetic interference suppression method for the matrix converter, CM EMI characteristics of the matrix converter are analyzed, a low-attenuation loop in the converter is constructed by adopting a method of combining a neutral line leading-back type structure and increasing output filter capacitor neutral-point-to-ground parasitic capacitance, the propagation proportion of noise in a linear impedance stabilization network measurement branch is reduced, and the measurement accuracy of the common-mode electromagnetic interference suppression method for the matrix converter is improved. And effective suppression of low-cost CM EMI noise is realized on the premise of not depending on an input EMI filter.

The invention discloses a modeling method and system for a vehicle network field circuit coupling model under a contact line short-circuit working condition, and relates to the technical field of modeling, and the method comprises the following steps: constructing a short-circuit arc equivalent model under the contact line short-circuit working condition based on an isolation switch equivalent model, one end of the short-circuit arc equivalent model is connected with a contact line in sliding connection with a pantograph in the chain type lumped tau-shaped network model through a switch element, the other end of the short-circuit arc equivalent model is connected with a steel rail in the chain type lumped tau-shaped network model, and a vehicle network circuit model under the contact line short-circuit working condition is obtained; and taking the short-circuit arc current at any short-circuit phase obtained through simulation of the vehicle network circuit model as a current excitation source of the three-dimensional model, and obtaining a vehicle network field model under the contact line short-circuit working condition. The rise rate, peak value and attenuation process of the short-circuit current can be accurately simulated by adding the short-circuit arc, the arc voltage drop is reflected, and the authenticity and practicability of simulation are remarkably improved.

The invention provides a lithium battery parameter online identification method based on dynamic weight particle swarm optimization, and relates to the field of power lithium battery model parameter identification. According to the method, a lithium battery equivalent circuit model based on a second-order RC network is established, and a state-space equation is derived according to the Kirchhoff's current law and the voltage law; a discrete output model is obtained through Laplace transformation and discretization processing; constructing a recursive least square parameter updater with a forgetting factor, and jointly optimizing an initial parameter vector of the forgetting factor recursive least square and the forgetting factor by adopting a dynamic weight particle swarm optimization algorithm; and based on the optimized initial parameter vector and the forgetting factor, performing recursive least square updating with the forgetting factor, performing online identification on model parameters, and reversely deducing an equivalent parameter value in an original circuit. According to the method, the parameter identification precision can be remarkably improved, and the problem that data saturation parameter convergence is slow in a traditional recursive least square method is solved.

The invention discloses an all-vanadium redox flow battery electrolyte flow control method, which comprises the following steps: step 1, establishing a hybrid model of an all-vanadium redox flow battery system and an all-vanadium redox flow battery system efficiency calculation model, the hybrid model comprising an electrochemical model, a fluid mechanics model and an equivalent loss circuit model of the all-vanadium redox flow battery system, the system efficiency calculation model calculates the system efficiency according to the battery rated power and the pump loss power; step 2, performing a simulation experiment on the hybrid model and the system efficiency calculation model established in the step 1 to obtain the electrolyte flow under different SOC states when the system efficiency is the highest, namely the optimal flow; and step 3, controlling a variable frequency pump of the all-vanadium redox flow battery system to work according to the optimal flow under different SOC states obtained in the step 2. According to the method, the working characteristics of the battery can be intuitively reflected, the system efficiency can be accurately calculated, rapid switching among the optimal flow intervals can be realized, and the working stability is improved.

The invention discloses a method for constructing a simplified equivalent circuit model of a battery energy storage system. The method comprises the following steps: acquiring data information of a target battery energy storage system; the polarization capacitance in the target battery energy storage system is not considered, and a neglected capacitance equivalent circuit model of the target battery energy storage system is constructed; simplifying the target battery energy storage system into a circuit model comprising a resistor and a voltage source by adopting the Thevenin theorem; calculating the series current and the charge state of the model; and completing the construction of the simplified equivalent circuit model of the battery energy storage system according to the obtained result. The invention also discloses a system for realizing the construction method of the simplified equivalent circuit model of the battery energy storage system. According to the method, the capacitor-neglected equivalent circuit model of the target battery energy storage system is constructed, the circuit model is constructed, and the parameters are calculated, so that the simplified equivalent circuit model of the battery energy storage system is constructed, and the method is higher in reliability, better in accuracy and higher in modeling effect.

The invention relates to the technical field of data processing, and particularly discloses a matrix pre-sequencing method and system based on reinforcement learning and a storage medium, which are used for modeling a matrix pre-sequencing problem into a sequence decision process, and learning an optimal sequencing strategy by utilizing a reinforcement learning agent through interaction with a matrix environment. The state of the agent is defined by the structural features of the matrix, and the action corresponds to the selection of nodes that have not been eliminated. The reward function aims at minimizing the ratio of the number of filling elements to the matrix dimension in the calculation process. The performance of the method is compared with that of a traditional heuristic algorithm in an experiment, and the result shows that in specific test cases such as a large-scale circuit model, the pre-sequencing method based on reinforcement learning has the advantages in the aspects of reducing the number of filling elements and shortening the calculation time.

A converter station AC filter group circuit breaker abnormal arcing modeling analysis method comprises the following steps: firstly, carrying out time mark alignment, amplitude calibration and filtering de-noising on wave recording signals such as AC bus voltage and filter branch current to obtain high-quality input data; then, combining a primary wiring diagram, enabling an alternating current system to be equivalent to an ideal power supply and series impedance, enabling circuit breaker arcing to be equivalent to arc resistance changing along with time, and constructing an equivalent circuit model comprising the power supply, the impedance, a capacitor, an electric reactor and dynamic arc resistance; establishing a state space equation according to a voltage and current constraint relation, and performing time domain solution by adopting a two-step two-order or three-order diagonal implicit Runge-Kutta method to obtain voltage and current responses of the whole process of the fault; on this basis, the arc resistance change is inversed according to the ratio of arc voltage to current, the current zero passage is identified through the resistance symbol change at adjacent moments, and the zero passage section is smoothed; and finally, the inversion result is verified again by means of the simplified equivalent model.

The embodiment of the invention provides a parameter updating method for a battery circuit model, a vehicle and a storage medium, and the method comprises the steps: obtaining a current state parameter and a current operation condition of a target battery, the current operation condition being used for representing the operation stability degree of the target battery; a battery circuit model corresponding to the target battery is used for predicting and obtaining predicted state parameters of the target battery, and the battery circuit model is used for simulating the electrochemical process of the target battery in the charging and discharging process; based on the current operation condition, determining a parameter adjustment factor of the battery circuit model, the parameter adjustment factor being used for quantifying an influence of the current operation condition on a model parameter corresponding to the battery circuit model; and based on the parameter adjustment factor, the current state parameter and the predicted state parameter, updating the current model parameter of the battery circuit model to obtain a target model parameter of the battery circuit model. According to the invention, the technical problem of low safety of the power battery in the prior art is solved.

The invention belongs to the technical field related to operation control of a power system, and provides a transient instability judgment method and system of a network-building type converter in order to solve the problem that an existing transient instability judgment method of the network-building type converter is prone to causing maloperation or missed judgment of a control strategy, and a nonlinear equivalent circuit model of the network-building type converter is established. Calculating transient total energy and critical instability energy of the system in real time, and adaptively switching a control mode according to the magnitude relationship of the transient total energy and the critical instability energy; during normal operation, an original control strategy of the virtual synchronous machine is maintained, and oscillation is suppressed by using the damping characteristic of the virtual synchronous machine, so that the voltage and frequency supporting capacity of a power grid is maximized; and when an instability risk is detected, entering a fault ride-through mode to realize rapid and accurate identification and control of the instability risk.

The invention provides an SOC estimation method and device for a lithium battery, electronic equipment and a storage medium, and belongs to the technical field of battery management, and the method comprises the steps: obtaining a current model parameter of a pre-constructed battery equivalent circuit model and an SOC estimation value at a previous moment; predicting the terminal voltage of the lithium battery at the current moment based on the current model parameter, the current of the lithium battery at the current moment and the SOC estimated value at the previous moment; and estimating the SOC at the current moment based on the SOC estimated value at the previous moment by adopting an SOC estimation algorithm, and correcting the SOC estimated at the current moment based on the deviation between the predicted terminal voltage and the terminal voltage actually measured at the current moment to obtain the optimal SOC estimated value at the current moment. According to the method, the dynamically updated battery equivalent circuit model parameters are introduced, so that the model can reflect the change of the impedance characteristic of the battery in real time, the terminal voltage prediction error is remarkably reduced, and the SOC estimation precision is improved.

The invention discloses a GaN HEMT (High Electron Mobility Transistor) driving method for quantitative suppression of false triggering oscillation. The method comprises the following steps: calculating turn-off transient process time; establishing a false triggering oscillation equivalent circuit model, deducing an oscillation system characteristic equation, performing root locus analysis on the oscillation system characteristic equation, and delimiting a design range of RC parameters with the best oscillation suppression effect; detecting a turn-off signal of the GaN HEMT through a detection circuit, and outputting a trigger signal of which the length is equal to turn-off transient process time; a trigger signal is input into the driving circuit to form a low-impedance grid branch, so that the turn-off process of the GaN HEMT is accelerated; and in response to completion of turn-off of the GaN HEMT, the trigger signal is controlled to drop to a low level, an RC buffer circuit is formed, and false trigger oscillation after the device is turned off is quantitatively suppressed. According to the invention, acceleration of the turn-off process of the GaN HEMT is realized, false trigger oscillation is quantitatively suppressed, the turn-off speed of the device is not influenced, and the gate impedance is reduced in the turn-off process of the device.

The invention discloses a method for estimating the state of charge of a series battery pack, which comprises the following specific steps of: firstly, constructing a battery pack equivalent circuit model based on online acquired battery parameters and series circuit characteristics, carrying out standardization processing on the terminal voltage Uti and the internal resistance R0i of each battery monomer, and carrying out Euclidean distance analysis to obtain the state of charge of the series battery pack; identifying the battery with the maximum difference as a representative battery monomer, then constructing a spatial state equation, and performing SOC estimation by adopting a self-adaptive extended Kalman filter with residual drive, thereby obtaining a battery pack state-of-charge base value SOCb; meanwhile, after abnormality judgment and effective data screening are carried out on battery monomer data collected offline, the voltage difference feature delta Uti and an estimated historical residual sequence eh are fused to form joint input, a CNN-GRU-SE hybrid neural network optimized by a KOA algorithm is used for estimating a battery pack charge state compensation value delta SOCb, and after processing is carried out through a moving average filter, the battery pack charge state compensation value delta SOCb is calculated. And superposing with the SOCb to obtain an estimated value SOCr of the state of charge of the series battery pack.

The application provides a high-frequency circulating current calculation method for a motor simulator cascaded coupling parallel structure, comprising the following steps: establishing an equivalent decoupling circuit model of each branch in the cascaded coupling parallel structure based on the voltage relationship of each branch in the motor simulator cascaded coupling parallel structure, wherein the equivalent decoupling circuit of each branch comprises an equivalent voltage source and an equivalent leakage inductance connected in series, and the equivalent leakage inductance is determined based on the self-inductance and mutual inductance of each branch choke; dividing a plurality of duty cycle intervals between 0 and 1, and the number of duty cycle intervals is equal to the number of branches in the cascaded coupling parallel structure; and using the equivalent decoupling circuit model to calculate the high-frequency circulating current of each branch when the duty cycle of the modulation signal of the motor simulator is in each duty cycle interval. The method provided by the application can accurately calculate the high-frequency circulating current of the motor simulator cascaded coupling parallel structure, and provides effective support for the targeted design of a high-frequency circulating current suppression scheme.

The invention discloses a modeling method of a simulation model, a storage medium and a terminal. The method comprises the following steps: acquiring an initial simulation model of a semiconductor device to be modeled; according to the initial simulation model, fitting a drain current-grid voltage characteristic curve of a sub-threshold region of the semiconductor device to be modeled; judging whether the to-be-modeled semiconductor device has subsurface leakage current in a subthreshold region of the drain current-grid voltage characteristic curve or not; if the secondary surface leakage current exists, establishing a sub-circuit model representing the secondary surface leakage current; integrating the sub-circuit model and the initial simulation model; and according to the integrated initial simulation model, fitting the drain current-grid voltage characteristic curve of the sub-threshold region until the fitting curve of the drain current-grid voltage characteristic curve converges, and obtaining a simulation model of the semiconductor device to be modeled. According to the modeling method, the secondary surface leakage current can be accurately modeled, and the simulation precision and accuracy of an integrated circuit simulation model are improved.

The invention discloses a method for estimating the capacity of a lithium battery based on an equivalent circuit model by using cloud data. The method comprises the following steps: acquiring a plurality of related data of a real vehicle cloud lithium battery in an actual charging and discharging process; screening out a plurality of sections of charging data fragments containing identifiable characteristics according to screening conditions; carrying out simple calculation and preprocessing on the obtained data; establishing a lithium battery equivalent circuit model, and determining input parameters of the model and battery parameters to be estimated; optimizing and identifying the to-be-identified parameters of the established equivalent circuit model by adopting a particle swarm optimization algorithm; carrying out optimization estimation by taking the capacity, the initial SOC and the internal resistance of the lithium battery as to-be-identified parameters; and finally, a root-mean-square error between the simulation voltage of the model and the collected actual voltage is used as a fitness function to judge an optimization result, the estimated capacity of the lithium battery is obtained, multi-section data joint fitting is adopted, contingency is eliminated, and the reliability of capacity estimation is improved.

The invention provides a transformer heavy overload evaluation analysis method and device, and the method comprises the steps: building a thermal circuit model of load-induced heat point temperature rise of a transformer according to the heating power, equivalent heat capacity and equivalent heat resistance of an internal winding of the transformer, and load-induced heat top oil temperature rise of the transformer; converting a heat flow source in the heat circuit model into a hot pressure source based on the Thevenin theorem, constructing an equivalent heat circuit model of the heat circuit model, and calculating load heating hot spot temperature rise of the transformer under different load rates according to the equivalent heat circuit model; according to the historical environment heating temperatures of the transformer under different load rates, obtaining predicted environment heating temperatures of the transformer under different load rates; and obtaining winding hot-spot temperatures of the transformer under different load rates according to the load heating hot-spot temperature rise and the predicted environment heating temperature of the transformer under the same load rate. According to the invention, the thermal state sensing precision of the transformer under the dynamic load is improved.