189 results about "Thermal network model" patented technology

The invention discloses an IGBT junction temperature real-time decoupling modeling method and system based on dynamic electric heating compensation, and relates to the technical field of state monitoring of power electronic devices. At present, junction temperature estimation is not accurate due to IGBT on-state voltage drop measurement distortion and chip internal thermal gradient in a high-voltage environment. The method comprises the following steps: in an offline stage, constructing an ideal electrothermal characteristic database of an IGBT, a dynamic temperature gradient compensation database in a chip and Foster thermal network model parameters; in the on-line stage, synchronous sampling and preprocessing are carried out on IGBT module operation parameters; combining online real-time parameters with an offline database by using an adaptive Kalman filter, and iteratively and cooperatively estimating the real-time junction temperature and loss of the IGBT; and finally, outputting and applying the estimated accurate junction temperature and loss data. According to the technical scheme, the hot spot effect in the chip is accurately decoupled, the junction temperature estimation precision and the robustness of online monitoring are improved, and core data support is provided for fine thermal management and early fault early warning of the flexible direct current converter valve.

The invention provides an IGBT junction temperature prediction method and system based on dynamic thermal impedance correction. The method comprises the following steps: acquiring temperature data in real time, reversely deducing IGBT loss in combination with a pre-constructed thermal network model, and storing the IGBT loss in historical data; obtaining an IGBT loss average value under the same working condition, and calculating a difference value between the IGBT loss average value and the IGBT loss and a specific value of the IGBT loss average value to obtain a loss offset coefficient; judging whether the loss deviation coefficient is continuously greater than a preset value in a continuous period, if so, judging that the heat-conducting silicone grease is aged, calculating an aging coefficient through loss data and temperature data in the continuous period, and correcting the thermal network model by adopting the aging coefficient; and obtaining the corrected internal transient thermal impedance of the IGBT and the loss of the IGBT, and further predicting the junction temperature of the IGBT. According to the method, the thermal network model is adopted to reversely deduce the real-time loss power of the IGBT, and electric parameter drift errors are avoided; and a dynamic thermal impedance correction mechanism is introduced to ensure long-term accuracy of junction temperature prediction.

The invention discloses a permanent magnet synchronous motor temperature monitoring method based on digital-analog hybrid drive, and the method specifically comprises the steps: dividing a motor into a plurality of heat source nodes, including a stator winding, a stator tooth part, a rotor core and the like, connecting the nodes through a thermal resistance and thermal capacity network, building a thermal network model of the motor, and carrying out the thermal network model; calculating the initial values of thermal resistance and thermal capacity of each component of the motor, and optimizing parameters in a thermal network model by using a differential evolution algorithm based on the lumped parameter thermal network model and in combination with a data-driven strategy. Furthermore, temperature rise prediction is dynamically adjusted according to motor state data (such as stator current, rotating speed and the like) collected in real time so as to realize real-time temperature estimation. The method is high in temperature estimation precision, does not affect the performance of the motor, is simple in calculation, is good in real-time performance, and has physical interpretability. The temperature rise of the motor can be predicted in real time under different working conditions, early warning is given out, motor faults caused by too high temperature rise are effectively avoided, and the reliability and safety of the motor are improved.

The invention discloses a transformer noise suppression method, system, equipment and medium, and the method comprises the steps: collecting the winding temperature, iron core temperature, cooling system working condition, load current and other operation parameters of a transformer in real time, constructing a multi-physical field coupling model covering electromagnetic, thermal and mechanical characteristics, and providing basic data for noise analysis; based on a heat conduction equation and a vibration acoustics theory, a mapping relation between a heat dissipation state and a transformer noise source is established, and dynamic influences of heat dissipation on noise amplitude and frequency are quantified; through combination of finite element simulation and a thermal network model, winding / iron core deformation caused by temperature change is analyzed, then a modulation rule of deformation on noise radiation characteristics is derived, and a heat dissipation state-physical deformation-noise characteristic transfer function is constructed; and based on the transfer function, dynamically adjusting operation parameters of the transformer by adopting a model predictive control and adaptive filtering technology, and minimizing noise output on the premise of ensuring heat dissipation efficiency.

The invention provides a self-adaptive optimization method and system for motor multi-working-condition temperature rise prediction, and relates to the technical field of motor temperature calculation and prediction. According to the method, the motor thermal network model is constructed through refined node division, the correction coefficient and thermal resistance sensitivity analysis are introduced, and the thermal resistance is scientifically graded, so that the model accuracy is improved. Furthermore, according to the method, the adaptive genetic algorithm is adopted to carry out sectional optimization on the thermal resistance parameters, and rapid and accurate calculation of the temperatures of the motor under various working conditions is realized. In addition, the invention further provides a system based on a computer readable storage medium, and the system is used for achieving the construction and optimization process of the model. According to the method, the genetic algorithm and thermal resistance sensitivity analysis are combined, rapid parameter optimization of the thermal network model is achieved, the temperature calculation precision of the model under different motor working conditions is remarkably improved, and the method has wide application prospects.

The invention relates to an IGBT module junction temperature estimation method and system, and the method comprises the following steps: obtaining a performance curve set of an IGBT module under different working conditions, and carrying out the fitting optimization, and obtaining an optimal performance curve set; constructing a transient thermal resistance curve model according to a transient thermal resistance principle, and constructing a second-order Foster thermal network model based on the transient thermal resistance curve model; performing Laplace transformation and discretization processing based on a backward difference method on the second-order Foster thermal network model to obtain a junction temperature estimation model; collecting a phase current instantaneous value, and calculating the total power loss of the IGBT module in real time in combination with the optimal performance curve set; and finally, the junction temperature of the IGBT module is obtained in real time by using the total power loss of the IGBT module obtained through real-time calculation and the junction temperature estimation model, and junction temperature estimation is completed. According to the method, the effect of improving the power loss precision of the IGBT module is achieved by optimizing the performance curve, and then the junction temperature estimation precision is improved.

The invention belongs to the field of crimping IGBT junction temperature estimation, and particularly discloses a crimping IGBT half-bridge module junction temperature online estimation method and system, and the method comprises the steps: carrying out the decoupling of a double-side thermal network model based on a multi-heat-flow path of a crimping IGBT half-bridge module, and achieving the junction temperature estimation of the crimping IGBT half-bridge module; wherein the establishment method of the double-sided thermal network model comprises the step of constructing the double-sided thermal network model based on the double-sided junction-to-case thermal resistance, the water-cooled radiator thermal resistance, the circulating water equivalent thermal resistance and the coupling thermal resistance of the crimping IGBT half-bridge module by taking the water inlet temperature of the water-cooled radiator as the reference temperature and combining the power loss of each crimping IGBT in the crimping IGBT half-bridge module. The method has research value and application prospect for junction temperature estimation and reliability evaluation of the converter valve crimping IGBT.

The invention discloses a lithium ion battery multi-state collaborative online estimation and reliability evaluation method and system, belongs to the technical field of battery management and state estimation, and aims to solve the problems that battery state collaborative online estimation is difficult and the confidence coefficient of an estimation result is unknown. The method comprises the following steps: establishing an improved equivalent circuit model fusing solid-phase and liquid-phase diffusion mechanisms, a multi-heat-source thermal network model and a degradation evolution model; model parameters are cooperatively identified through an off-line experiment and an on-line double-filter frame, the state of charge SOC, the internal temperature SOT and the state of health SOH of the battery are estimated in real time, a first filter updates external variable thermal resistance, and a second filter fuses voltage and single surface temperature measuring point data to cooperatively estimate the SOC and the SOT; after the model is calibrated through experimental data, the core temperature of the battery is estimated online through an observation filtering method based on a single surface temperature measuring point; a self-adaptive uncertainty quantization module based on filter covariance and measurement residual error is introduced, and a confidence interval of a state estimation value is calculated.

The invention provides a thermal management system energy grade and energy conversion difference measurement method based on # imgabs0 # analysis, and the method comprises the steps: obtaining key parameters of all parts of a system in real time, carrying out # imgabs1 # analysis, and obtaining an energy quality coefficient; performing grade difference measurement and heat flow distribution to obtain grade grading and flow direction configuration of each component; constructing a heat energy network model, and optimizing a network loop to form a high and low temperature multi-loop heat energy network; measuring component energy conversion efficiency difference to obtain a difference analysis result; analyzing the corresponding speed of energy conversion and performing control medium classification to obtain corresponding speed classification and medium distribution suggestions; and constructing a heat flow prediction model, designing an algorithm reward function, and obtaining an optimization control strategy based on response difference measurement. The heat flow can be reasonably distributed and utilized step by step according to the grade, the energy utilization efficiency of the total system is improved, the optimal energy utilization terminal combination scheme can be designed, energy loss is reduced, and the overall performance of the system is improved.

The invention provides an IGBT junction temperature prediction model construction method and system, and relates to the technical field of power electronic device thermal management, and the method comprises the steps: obtaining an initial data set representing the transient thermal response relation between an IGBT chip and an NTC temperature measurement point in an IGBT power module where the IGBT chip is located; training a neural network model based on the initial data set, and learning and representing a nonlinear function relationship between time and thermal impedance; inputting a preset new time sequence into the trained neural network model, and generating a time-continuous and noise-suppressed optimized thermal impedance data set; and based on the data set, performing parameter fitting on a preset lumped parameter thermal network model to identify thermal resistance and thermal capacity parameters of the model so as to complete construction of the junction temperature prediction model. According to the method, the neural network is used as a data optimization tool in a physical model parameter identification process, and accurate and convenient online prediction of the IGBT junction temperature is realized only by using the temperature signal of the NTC temperature measurement point of the module.

The invention discloses a variable switching frequency-based power enhancement method for a network-constructing type converter under a dynamic state of a power grid, and relates to the field of network-constructing type converter control. According to the method, the power module loss and thermal network model is constructed, the junction temperature of the power device of the network-forming type converter is estimated to keep the junction temperature unchanged, the relation model of the power reference value and the switching frequency of the converter is established, and the power-switching frequency relation model is looked up after the power reference value is obtained through the network-forming algorithm during the dynamic period of the power grid. And obtaining the switching frequency corresponding to the current power reference value for underclocking operation, thereby reducing the switching loss of the power module, and ensuring that the maximum power output capability of the converter is maintained under the dynamic state of the power grid until the switching frequency is reduced to the lowest amplitude limit. The method for enhancing the power of the constructed network type current converter effectively improves the voltage and frequency supporting capability under the dynamic state of the power grid.

The invention discloses a water pump motor temperature estimation method based on data and model fusion. The method comprises the following steps: obtaining multi-source operation data of a water pump motor under different operation conditions; constructing a multi-node thermal network model of the motor for the water pump, and calculating and outputting multi-node temperature estimation values under different operation conditions based on the multi-source operation data; constructing an LSTM neural network model, and introducing physical prior constraints to obtain a composite loss function fused with physical prior; training an LSTM neural network model, and optimizing and adjusting the LSTM neural network model by using a composite loss function to obtain a temperature prediction model; when the temperature of the motor for the water pump is estimated, motor operation data under the corresponding operation working condition are input into the temperature prediction model, and the temperature of each hot node of the motor for the water pump is output. According to the method, the prior structure information provided by the thermal network model can be utilized, the capability of self-adaptive modeling for different working conditions is achieved, and efficient and accurate estimation of the internal temperature of the motor for the water pump is achieved.

The present invention provides a battery cooling method based on phase change materials, comprising: step S1, designating multiple equivalent thermal nodes on a lithium-ion battery structure coated with a phase change material layer at both ends of the battery cell, and modeling a three-dimensional thermal network model by combining air temperature nodes and cooling medium temperature nodes; step S2, establishing heat conduction paths between nodes within the three-dimensional thermal network model to simulate battery heat dissipation; step S3, collecting temperature material data during the battery heat dissipation simulation; step S4, obtaining multiple phase change material thickness optimization schemes as individuals to form a population cluster; step S5, extracting a preset number of individuals from the population cluster, and based on the temperature material data, continuously performing population iterative optimization on each individual using a pre-established weighted objective function to obtain the optimal individual, and then using the optimal individual to optimize the thickness of the phase change material layer. The present invention has the beneficial effect of improving temperature control accuracy and reducing material costs.

The invention discloses a hot spot and average temperature estimation method for a permanent magnet synchronous motor based on a thermal neural network, and belongs to the field of motor state monitoring. Predicting and outputting a thermal parameter correction coefficient of the current time step by using the trained thermal parameter correction neural network; based on the reference thermal parameter, the thermal parameter correction coefficient of the current time step, the motor state data of the current time step and the node average temperature prediction value of the previous time step, predicting and outputting the average temperature of each node of the permanent magnet synchronous motor of the current time step by using a lumped parameter thermal network model; and based on the motor state data of the current time step and the average temperature prediction value of each node of the permanent magnet synchronous motor, predicting and outputting the hot-spot temperature of the permanent magnet synchronous motor of the current time step by using the trained hot-spot temperature prediction neural network. The method is supported by the Zhejiang Scientific and Technology Project (2025C01199 (SD2)) of Zhejiang Province 'Scientific and Technical Project' of 'Scientific and Technical Project

The invention discloses a temperature prediction method and device, and a storage medium. The temperature prediction method comprises the following steps: constructing a two-node thermal network model of a motor; wherein the two-node thermal network model comprises a stator node and a rotor node, and the two-node thermal network model takes the temperature of a cooling medium in a motor cooling loop as a temperature reference point; predicting a first temperature of the target node at the next moment based on the temperature of the target node at the current moment by using a two-node thermal network model; wherein the target node comprises at least one of a stator node and a rotor node. In this way, the efficiency of predicting the temperature of the target node can be improved.

The invention discloses a temperature field estimation method for a liquid cooling-phase change hybrid cooling structure of a permanent magnet motor. The method comprises the steps that a motor cooling structure is divided into independent areas, and a three-dimensional heat conduction model is constructed under overload working condition operation; setting temperature nodes in the independent areas of the stator and the phase change material along the circumferential direction, taking the other independent areas as the temperature nodes, and taking motor loss as a heat source, so as to construct a thermal network topological structure; a convective heat transfer coefficient is obtained, the heat capacity of cooling oil in the circumferential direction and the equivalent specific heat capacity of a phase change material are applied to temperature nodes, motor loss is applied to a heat source, and an equivalent transient heat network model is constructed; and obtaining the temperature rise of each temperature node until the overload condition is finished, and obtaining an estimated temperature field. The phase-change material effectively inhibits the instantaneous large temperature rise of the permanent magnet under the overload working condition; the model can completely represent the circumferential temperature gradient distribution, reflect the temperature space distribution difference of the stator winding under the overload condition, and improve the estimation speed of the temperature field.

The invention discloses a silicon carbide power device junction temperature online accurate extraction method based on a double-sensor Kalman filtering weighted average algorithm, and the method comprises the following steps: 1, enabling a loss model of a power device to receive the phase current, the DC bus voltage, the duty ratio, the phase current and the DC bus voltage from an inverter; the on resistance of the SIC MOSFET, the reference direct current bus voltage, the on loss energy and the off loss energy under the on current and the on threshold voltage of the Schottky diode are obtained from a data manual of the chip, and the gate driving resistor is used for extracting the junction temperature and monitoring operation in real time; and step 2, obtaining an estimated junction temperature through loss calculation of a power device and a thermal network model, and weighting the junction temperatures estimated at different measurement points through a Kalman filtering weighted average algorithm to obtain a more accurate junction temperature for next iterative calculation. According to the method, the limitation of a traditional single sensor in a dynamic working environment can be effectively compensated, and the accuracy and the real-time performance of junction temperature monitoring are improved.

The invention discloses a temperature control method and device for an IGBT module, and the method comprises the steps: carrying out the initial estimation of the junction temperature of key parameters, such as current, voltage, switching state, frequency, shell temperature and environment temperature, which are collected in real time based on a standard physical thermal network model, and introducing a pre-stored IGBT loss characteristic to improve the basic prediction accuracy; further, a machine learning model is used for mining a complex dynamic nonlinear relation between input variables from historical operation data, and system errors caused by simplification or hypothesis of a physical model are effectively corrected; and finally, taking the fused and corrected junction temperature result as an adjusting basis of a cooling device, and dynamically adjusting the cooling intensity through control algorithms such as PID and the like, thereby ensuring that the IGBT module is always in a safe and efficient operation interval. Therefore, not only are theoretical preciseness and practical applicability considered, but also the robustness and the intelligent self-adaption level of the system under factors such as working condition change, self-aging and external disturbance are remarkably improved.

The invention discloses a bearing transient temperature measurement method based on multi-source data fusion, and the method comprises the specific steps: monitoring a vibration signal of a bearing system and the temperature of a bearing outer ring in real time through an acceleration sensor and a temperature sensor, and dividing dynamic temperature nodes on the section of the bearing system according to the structure and heat transfer characteristics of the bearing system, establishing a one-dimensional thermal network model on the section, and calculating, analyzing and determining the relative sliding speed of the roller and the inner and outer rings, the revolution frequency of the roller, the spinning frequency of the roller, the movement frequency of the retainer, the contact force of the roller and the inner and outer rings and the contact force of the roller and the retainer based on the vibration signal frequency and a bearing dynamical model; and the vibration signal amplitude is coupled into an additional contact load, transient heat generation is calculated by using a local heat generation method, and a two-dimensional transient temperature field of the bearing system is generated. According to the method, the limitation of a traditional static node model is broken through, the vibration data of the bearing system and the thermal network model are fused, and real-time calculation of the transient temperature field of the bearing system is achieved.

The application discloses a driving motor temperature estimation method based on machine learning and double Kalman filtering and relates to the technical field of motor temperature monitoring and control. The application comprises the following steps: constructing a finite element simulation model, collecting motor loss data and temperature response data under multiple working conditions through finite element simulation, and constructing an offline parameter database through a physical information machine learning method; based on the offline parameter database, a second-order lumped parameter thermal network model is constructed, and the second-order lumped parameter thermal network model is converted into a linear parameter variation state space model. The application switches parameters in real time through an LPV model, dynamically corrects model errors in combination with a double Kalman filter, overcomes parameter drift caused by temperature and rotating speed, and solves the problem that the precision of an offline modeling open-loop estimator is insufficient and accurate real-time estimation under all working conditions cannot be realized due to modeling errors, time-varying thermal parameters and other reasons in the traditional rotor temperature estimation method under wide-range complex operating conditions of an electric vehicle.

The invention discloses a compensation thermal resistance regulation and control method applied to relay thermal network modeling, and belongs to the technical field of thermal modeling in an electromagnetic and thermal coupling system. The method is used for solving the problems that a traditional thermal network model is inaccurate in local temperature field prediction and difficult in hot spot recognition in relay thermal analysis. Comprising the steps of S1, establishing a thermal network model of a relay structure; s2, a compensation thermal resistance unit is inserted, and a T-type network is established; s3, solving a two-dimensional Poisson equation to obtain an average temperature; s4, comparing the average temperature with the node temperature in the thermal resistance network; iteratively adjusting the compensation thermal resistance unit until the temperature error of the compensation thermal resistance unit and the compensation thermal resistance unit is within an acceptable range; and S5, the calibrated model is operated, and the steady-state temperature is output. The introduction of the compensation thermal resistance is mathematically equivalent to the addition of a reverse heat flux regulator, so that the node temperature can be approximate to the two-dimensional integral average temperature, the distortion of the thermal network in a local two-dimensional region is effectively corrected, and the reflection capability of the model to local hot spots and temperature gradients is remarkably improved.

Disclosed in the present invention is a method for implementing temperature monitoring on power devices by means of a thermal observer. The method comprises: mounting sensors on the upper surface of a radiator and at an NTC in a power module, so as to construct a sensor network to implement information collection with respect to heating situations of different power devices; fitting a thermal network on the basis of a thermal response curve, and extracting thermal resistance and thermal capacitance from a model to establish a thermal impedance matrix, and forming a state-space equation for a thermal sensor network; establishing a thermal observer by using a Gillijn De Moor filter algorithm, so as to estimate losses of the power devices, and realize thermal information observation with respect to operations of the devices; and fitting a thermal network model of the devices and the environment by means of the thermal network, so as to realize real-time tracking of junction temperatures of the power devices. The present invention can be flexibly expanded on the basis of the number of devices and a module structure, thereby realizing device temperature monitoring with high safety, good real-time performance and high accuracy.

The invention relates to the technical field of compressed air energy storage, in particular to a thermal power coupling compressed air energy storage system robust scheduling method, device and equipment, and the method comprises the steps: constructing an operation model of each subsystem in a topological structure of the thermal power coupling compressed air energy storage system; a comprehensive operation model of the whole thermal power coupling compressed air energy storage system is established based on the operation model of each subsystem, thermal power resources can be utilized to the maximum extent, the energy storage efficiency is improved, the comprehensive model serves as a thermoelectric system hub, a power flow model on the power grid side and a heat supply network model on the heat supply network side are combined, an optimal scheduling model is established, and the energy storage efficiency is improved. A new energy output fuzzy set is obtained to cope with uncertain factors in actual operation, fuzzy set parameters are set according to actual data of a power grid in a target region, and the parameters are input into an optimal scheduling model to output an optimal scheduling strategy, so that the system operation cost is effectively reduced, and the system performance is improved. And the adaptability to new energy fluctuation and the risk management capability are enhanced.

The application provides a kind of IGBT junction temperature prediction model construction method and system, it is related to power electronic device thermal management technical field, method includes: obtaining the initial data set of the transient thermal response relationship between the IGBT chip and the NTC temperature measuring point in the IGBT power module;Based on the initial data set, the neural network model is trained, and the nonlinear function relationship between time and thermal impedance is learned and characterized;The preset new time series is input into the trained neural network model, and the optimized thermal impedance data set with continuous time and noise suppression is generated;Based on the data set, the lumped parameter thermal network model is parameter fitted, to identify its thermal resistance and heat capacity parameters, complete the construction of junction temperature prediction model.The application uses neural network as a data optimization tool in the parameter identification process of physical model, only uses the temperature signal of NTC temperature measuring point provided by the module, realizes the accurate, convenient online prediction of IGBT junction temperature.

The invention provides a variable reluctance motor temperature distribution prediction method and prediction system. The method comprises the following steps: dividing and modeling network nodes based on the structure and network precision requirements of a variable reluctance motor; calculating conduction thermal resistance and thermal capacity according to geometric parameters and material parameters of the motor; calculating air gap convection thermal resistance according to the motor working condition and the air gap viscosity coefficient; considering the relative position relationship between the armature winding and the excitation winding and establishing a complete variable reluctance motor thermal network model; calculating iron loss and copper loss by using an empirical formula based on state variables of actual operation of the motor, and taking the iron loss and the copper loss as input sources of the variable reluctance motor thermal network model; and iteratively solving the thermal network model of the variable reluctance motor by using a thermal transient equation to obtain predicted temperature distribution of the variable reluctance motor. The invention provides a complete modeling and prediction method suitable for temperature distribution of the variable reluctance motor, the calculation efficiency and precision are both considered, and powerful support is provided for multi-physics field joint optimization.

The invention provides a wind driven generator fault diagnosis method, device and equipment and a storage medium, and relates to the technical field of intelligent operation and maintenance of wind power generation equipment. The method comprises the following steps: constructing a thermal network model of the wind driven generator, and obtaining a transient heat transfer equation and a first thermal conductance matrix and a first heat source intensity vector of the wind driven generator in a healthy state based on the thermal network model; according to the first thermal conductance matrix, the first heat source intensity vector and the current temperature of each temperature measurement point of the wind driven generator, performing inversion based on a transient heat transfer equation to obtain a second thermal conductance matrix and a second heat source intensity vector; obtaining a first fault feature matrix according to the second thermal conductivity matrix and the first thermal conductivity matrix, and obtaining a second fault feature matrix according to the second heat source intensity vector and the first heat source intensity vector; and obtaining a fault diagnosis result of the wind driven generator according to the first fault feature matrix, the second fault feature matrix and a preset fault diagnosis model. According to the invention, high-precision fault diagnosis of the wind driven generator can be realized.

The invention provides an interface thermal resistance testing method and testing device, and belongs to the technical field of semiconductor device testing. The test method comprises the following steps: acquiring an actual cumulative structure function curve of a plurality of contrast samples of the semiconductor device, constructing a thermal structure model of the semiconductor device, and establishing a thermal network model corresponding to the thermal structure model, the thermal network model is used for representing an equivalent thermal structure of a heat flow path of the semiconductor device from a heating junction to an environmental medium; optimizing the parameters of the thermal network model through the actual cumulative structure function of each contrast sample; and obtaining the thermal resistance value of each interface in the semiconductor device according to the optimized thermal network model. The thermal resistance of different interfaces in the semiconductor device can be determined.

The invention discloses an IGBT aging monitoring method and apparatus, and an electronic device. The method comprises the steps of obtaining a pre-established first junction temperature expression, a first shell temperature expression and a model parameter conversion model of a to-be-monitored IGBT; the actually measured junction temperature and the actually measured shell temperature of the IGBT to be monitored and initial values of model parameters of the four-order Cauer thermal network model are obtained; fitting the first junction temperature expression in combination with the actually measured junction temperature to obtain a junction temperature initial value coefficient and a time constant; inputting different values of the junction temperature initial value coefficient, the time constant and the model parameter into a model parameter conversion model to obtain a shell temperature initial value coefficient corresponding to each value; inputting each value of the model parameter and the initial value coefficient of the shell temperature into a first shell temperature expression to obtain the shell temperature corresponding to each value; and evaluating the aging state of the to-be-monitored IGBT according to the model parameter corresponding to the shell temperature closest to the actually measured shell temperature. According to the invention, the aging state can be accurately evaluated in real time in different states.

A transmission system bearing interface modeling method includes the steps of: establishing a three-dimensional (3D) model of a bearing interface, and performing grid generation for a fluid domain of the bearing interface within the 3D model; determining simulation operating conditions, modeling the fluid domain using a volume of fluid (VOF) model, and capturing phase interfaces in multiphase flow within a gear transmission; modeling a solid domain of the bearing interface using a thermal network model, and discretizing the solid domain into a plurality of temperature nodes; calculating frictional heat generation of a gear pair to obtain heat sources under meshing conditions, and applying the heat sources to the thermal network model for simulation; and packaging the modeled bearing interface into an analysis system to calculate a heat source of the bearing interface. Reference can be provided for temperature variation and multiphase flow behavior in gear transmissions, improving research efficiency.

The invention discloses a parameter transformation method of an equivalent thermal network model and a related device, and the method comprises the steps: obtaining the complex frequency domain network impedance of a Foster thermal network model through curve fitting; according to the complex frequency domain network impedance of the Foster thermal network model, all thermal parameters in the Cauer model are solved in an admittance solving mode, and the thermal parameters of the Cauer model can be obtained through thermal parameter transformation of the Foster model by means of the method and the related device.