143 results about "Thermal network model" patented technology

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 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 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 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 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 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 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 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 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.

The application discloses a kind of four-wheel independent electric drive robot chassis driver's elastic power control method, including four-wheel independent electric drive robot receiving remote controller issuing marching instruction;Desired current of four-way drive unit is respectively set according to marching instruction requirement;The junction temperature of power device is calculated in real time through junction-shell thermal network model, output to temperature closed-loop fuzzy controller, and temperature closed-loop control parameter is adaptively adjusted, and the current limit value of each drive unit is dynamically output;Current setting strategy is formulated in combination with finite state machine principle, and the actual current limit value of each drive unit is corrected;The running current of chassis driver is changed by current closed-loop control, and then the power output state of chassis driver is optimized.The application can guarantee the thermal safety of four-wheel independent electric drive chassis driver, and realize the elastic control effect of four-wheel independent electric drive chassis driver power.

The invention discloses a three-phase copper bar temperature estimation method and system of a new energy automobile electric drive system, and relates to the technical field of electric automobile electric drive, and the method comprises the steps: obtaining the vehicle environment temperature and the vehicle time, and recording the time point during dormancy, the three-phase copper bar environment temperature and the three-phase copper bar estimation temperature; reading the time point when the vehicle is awakened after the dormancy, and determining the dormancy time of the vehicle by combining the dormancy time point; according to the current vehicle environment temperature, the vehicle dormancy time, the three-phase copper bar environment temperature and the three-phase copper bar estimated temperature, through a pre-constructed four-dimensional temperature estimation table when the three-phase current is zero, the initial temperature of the three-phase copper bar after waking up is obtained through table look-up; and according to the current environment temperature of the vehicle and the initial temperature of the awakened three-phase copper bar, the current temperature of the three-phase copper bar is calculated through a pre-established thermal network model from the environment temperature to the temperature of the three-phase copper bar. The method can achieve the accurate estimation of the temperature of the three copper bars, and effectively balances the performance and safety of an electric drive system.

This invention discloses a method for IGBT temperature equalization control in parallel DC / DC converters of DC microgrids, belonging to the field of IGBT active temperature equalization control technology. The method includes nine steps: IGBT power loss calculation, IGBT thermal network model establishment, IGBT thermoelectric coupling model construction, IGBT junction temperature calculation in the DC / DC converter, average IGBT junction temperature calculation in the parallel DC / DC converter, calculation of the difference between the IGBT junction temperature and the average junction temperature in the parallel DC / DC converter, calculation of the virtual impedance for IGBT temperature equalization fine-tuning, adding the virtual impedance to the droop control loop, and generating PWM drive signals for the parallel DC / DC converter based on voltage and current dual closed-loop control. This invention adds the virtual impedance for IGBT temperature equalization fine-tuning to the traditional droop control, adjusting the current flowing through the IGBTs in each DC / DC converter, so that the IGBT junction temperatures in each parallel DC / DC converter tend to be uniform, thereby improving the overall system reliability.

The application belongs to the technical field of motor thermal management and computer simulation, and is a motor dynamic thermal network parameter identification and modeling method for full working conditions, comprising the following steps: S1, constructing an equivalent thermal network model of a motor lumped parameter thermal network structure considering key heat dissipation interfaces; S2, establishing a nonlinear thermal resistance and thermal capacity mathematical model that dynamically changes with temperature; S3, based on virtual and real combination, constructing a combined dataset covering the full working condition domain; S4, using a double-layer hybrid optimization mechanism of sparrow search algorithm combined with a local optimization algorithm to jointly identify unknown parameters to be identified, finally determining the polynomial coefficients of multiple dynamic thermal network parameters, and completing the establishment of the dynamic thermal network model. The application significantly improves the motor full-temperature-zone prediction accuracy, conforms to the physical nature of the motor material and fluid heat dissipation changing with temperature, eliminates the prediction deviation of the traditional constant model in a wide temperature range, and ensures high calculation efficiency by using a simplified model.

The application discloses a kind of high-frequency transformer three-dimensional thermal network modeling methods considering heat transfer mechanism, belong to high-frequency transformer technical field, including the following steps: S1, construct high-frequency transformer finite element model and carry out magnetic-thermal coupling simulation;S2, the division of node area is carried out to the core and winding of high-frequency transformer;S3, for different node areas, calculate the thermal resistance of high-frequency transformer 3D geometric model;S4, according to the simulation result of S1, construct heat conduction thermal resistance, convection heat transfer thermal resistance and radiation heat transfer thermal resistance;S5, based on heat flow source, heat conduction thermal resistance, convection heat transfer thermal resistance, radiation thermal resistance, heat capacity and environment node temperature, establish complete high-frequency transformer 3D thermal network model.The application uses the above method, by introducing convection and radiation heat transfer mechanism, and combining refined heat source division and thermal resistance calculation, can improve the precision of high-frequency transformer temperature rise prediction while maintaining high computational efficiency.

The invention discloses a time-varying thermal failure probability estimation method and system for a deep groove ball bearing, and belongs to the field of bearing design. The method comprises the following steps: constructing a basic parameter model containing a mixed random parameter vector and a limit state function; establishing a transient thermal network model to calculate time-varying temperature response; constructing an agent model to replace high-time-consumption heat generation rate calculation; a self-adaptive time node sampling strategy is adopted, a proxy model and a thermal network model are combined to process large-scale samples, and the time-varying thermal failure probability is estimated. The system comprises a basic parameter modeling module, a transient thermal network simulation module, an agent model construction module and a self-adaptive probability evaluation module which correspond to the system. According to the method, the time-varying and random dual uncertainty is fused, and the proxy model and the adaptive sampling strategy are utilized, so that the defects of conservative property of a traditional static evaluation method and low efficiency of a traditional probability simulation method are effectively overcome, and accurate evaluation of the thermal behavior of the bearing under the dynamic working condition and remarkable improvement of probability calculation efficiency are realized.

This application relates to a method and system for IGBT hotspot identification based on transient thermal resistance curves, belonging to the field of IGBT thermal monitoring technology. The method includes: loading the junction-shell transient thermal resistance curve from the datasheet to construct a fractional-order thermal network model containing equivalent thermal resistance, thermal capacity, and fractional order; obtaining model parameters through parameter identification; real-time acquisition of electrical quantities and temperature to calculate power loss; substituting the power loss into the fractional-order differential equation to solve for the junction-shell temperature difference, and combining this with the operating temperature to obtain a real-time estimated junction temperature; and identifying hotspots based on the estimated junction temperature to obtain hotspot status information. This method achieves a complete closed loop from offline modeling and parameter identification to real-time junction temperature monitoring and hotspot identification. This application solves the problems of low reliability of a single junction temperature criterion and the inability to distinguish the location of thermal aging in the chip layer, solder layer, and substrate layer in IGBT thermal monitoring.

The present application relates to a kind of life prediction method and system considering IGBT module bonding wire failure.The method includes determining the health state of IGBT module according to the online monitoring data of IGBT module;Online monitoring data includes: the number of broken bonding wire;When IGBT module is healthy module, directly obtain transient impedance curve, and determine RC thermal network model according to transient impedance curve, and then carry out power cycle experiment, realize life prediction;When IGBT module is aging module, according to the transient thermal impedance measurement test measured IGBT module current transient thermal impedance curve, and determine the current RC thermal network model using current transient thermal impedance curve, and then carry out power cycle experiment, realize life prediction.The present application can improve the accuracy of life prediction result of IGBT module.

A kind of thermal-electric comprehensive energy system failure influence quantitative evaluation method and system, comprising: constructing power grid, heat grid diagram structure and power grid, heat grid model;The power transmission efficiency and power supply proportion of power grid node are calculated, the heat transmission efficiency and heat supply proportion of heat grid node;And based on this, the power importance index of power grid node, the power importance index of power grid branch and the heat importance index of heat grid node are calculated;Each fault characteristic data of power grid and each fault characteristic data of heat grid are calculated;Thermal-electric coupling strength and failure strength are calculated;The importance weight of power grid and heat grid is calculated based on thermal-electric coupling strength and failure strength, and the importance weight is weighted and summed to each fault characteristic data of power grid and heat grid respectively, to obtain final failure risk index.This method considers the topological characteristics of different networks and the priority of each fault characteristic in the process of quantitative risk level calculation, and provides strong support for the safety analysis of thermal-electric comprehensive energy system.

The invention relates to the technical field of building thermal engineering, solves the problems of inaccurate and incomplete evaluation of the heat storage performance of a building envelope and poor environmental adaptability in the prior art, and provides a method, a device and equipment for evaluating the heat storage efficiency of the building envelope and a storage medium to prepare a target test piece containing a functional material; carrying out a cross-characteristic change temperature interval experiment by utilizing the dynamic thermal and humid climate wind tunnel to obtain nonlinear thermal response data; constructing an enthalpy method discrete numerical model, and performing parameter inversion by taking the minimum root-mean-square error of theoretical surface heat flow and actually measured heat flow as a target to obtain thermophysical parameters and an effective specific heat capacity curve; and constructing a thermal network model based on the inversion parameters, and simulating hourly heat flow of each node of the test piece, so as to calculate a multi-dimensional heat storage efficiency evaluation index of the enclosure structure. According to the method, the high-precision wind tunnel experiment, the nonlinear heat transfer model and the inversion algorithm are coupled, the dynamic heat storage performance of the enclosure structure is accurately quantified, and an accurate and practical solution is provided for evaluating the heat storage efficiency of the functional enclosure structure.

The invention discloses a spacecraft heat balance dynamic simulation evaluation method, and particularly relates to the technical field of spacecraft heat balance. Constructing a spacecraft kinematics model with an expandable structure and a thermal network model; establishing a thermal-structure-motion multi-field coupling interface, and realizing dynamic driving of a structure deployment state on thermal connection parameters; in the joint simulation process, the thermal coupling matrix is updated in real time, so that the thermal connection state continuously changes along with structure deployment; key thermal node temperature sequence data are recorded, thermal response characteristics of the key thermal node temperature sequence data in the whole deployment process are evaluated, and the problems of temperature anomaly and thermal instability are identified; reversely positioning a potential thermal control defect area and a deployment strategy risk point by combining the deployment state and the thermal connection state change, and outputting a thermal response map and a design optimization suggestion; according to the method, the accuracy and responsiveness of thermal simulation in the structure deployment process are improved, thermal connection dynamic modeling and risk diagnosis in the deployment process are achieved, and the method is suitable for thermal control design and thermal safety evaluation of the spacecraft with the deployable structure.

The invention discloses a half-bridge power module packaging optimization method, and belongs to the technical field of power module packaging, and the method comprises the steps: S1, analyzing the thermodynamic performance of a power module; s2, establishing a two-dimensional power module finite element model; s3, based on finite element simulation, researching the influence of temperature on the service life and stress strain of the module; s4, based on finite element simulation, obtaining the thermal resistance value of each layer of material and the ratio of the thermal resistance value to the total thermal resistance value; s5, analyzing the relationship between the packaging parameters and the thermal impedance, and giving an optimization direction from the packaging level by integrating the temperature and the stress; and S6, establishing a thermal network model, extracting junction temperature through electrothermal coupling, and verifying life increase. By the adoption of the half-bridge power module packaging optimization method, the size of the module can be reduced, temperature resistance and stability can be enhanced, the problem of multi-physics field coupling can be solved, internal thermal impedance and stress strain can be remarkably reduced, thermal performance can be improved, and the service life of the power module can be effectively prolonged through verification of the working condition of a photovoltaic inverter.

The application relates to the technical field of new energy vehicles, in particular to a torque control method and device, a vehicle and a storage medium, wherein the method comprises the following steps: acquiring a current voltage of a high-voltage battery and a current temperature of a motor stator; calculating a current temperature of a motor rotor according to the current temperature of the motor stator and an equivalent thermal network model of the motor rotor; taking the current voltage and the current temperature of the motor rotor as indexes, querying an efficiency table of a motor drive system corresponding to the indexes, and determining a current efficiency of the motor drive system; calculating a motor drive request torque of the motor drive system according to the current efficiency, and controlling the motor drive system to execute the motor drive request torque so as to provide power for the whole vehicle or realize energy recovery. Therefore, the problems that the actual efficiency of the motor drive system is greatly different from the calibrated efficiency, the actual power consumption of the battery in the motor mode exceeds the maximum allowable discharging power of the battery, and the actual charging power in the energy recovery mode exceeds the maximum allowable charging power of the high-voltage battery are solved.

The invention discloses an energy room natural ventilation intelligent control method and system for storing temperature based on day and night temperature difference, and relates to the technical field of building energy saving and intelligent control, and the method comprises the steps: collecting indoor and outdoor air temperature, air comfort and meteorological environment data through deploying indoor and outdoor multi-source sensors of an energy room; deducing an indoor and outdoor temperature difference change trend in a future day and night period by adopting a grey box heat network model, and identifying a candidate temperature storage window; calculating a temperature storage potential score based on the thermal network model and the building structure thermal capacity parameter; the method comprises the following steps: performing screening in combination with air comfort constraints, determining partition temperature storage priorities, constructing an energy house ventilation topological graph, estimating effective ventilation rates under different windowing combinations by utilizing a simplified aerodynamic model, constructing a multi-scene scene tree, and solving an optimal ventilation strategy under multiple scenes by adopting a multi-element heuristic algorithm of adaptive learning. According to the method, the temperature storage potential is quantified, and comfort and air quality are both considered; and the temperature storage efficiency and the intelligent level of the energy room are obviously improved.