A protection control method and system for energy storage converter test in extreme environment

Abstract

This invention discloses a protection and control method and system for energy storage converter testing under extreme environments. The method includes calibrating the parameters of a multiphysics reduced-order simulation model based on measured performance data to obtain a calibrated multiphysics reduced-order simulation model; embedding a device-level performance degradation proxy model into the calibrated multiphysics reduced-order simulation model to obtain a system-level simulation model; inputting environmental parameter samples into the system-level simulation model for simulation, obtaining risk assessment results based on the performance simulation result set, and selecting at least one high-risk environmental parameter combination; obtaining corresponding physical test cases, driving test equipment to perform physical extreme environment tests on the energy storage converter, comparing the measured results with the prediction results obtained by the system-level simulation model based on the corresponding high-risk environmental parameter combination simulation, and optimizing the parameters of the multiphysics reduced-order simulation model and the device-level performance degradation proxy model to ensure the reliability of the energy storage converter operating under extreme environments.

Description

Technical Field

[0001] This invention belongs to the field of power electronics and energy storage system testing technology, and relates to a protection and control method and system for energy storage converter testing under extreme environments. Background Technology

[0002] With the widespread application of renewable energy and energy storage systems in grid peak shaving, off-grid power supply, and special fields, energy storage converters face challenges from multiple extreme environmental stresses, including high temperature, low temperature, high humidity, corrosion, vibration, and complex power grids. To ensure the reliable operation and long lifespan of energy storage converters under extreme conditions, environmental adaptability testing and protection capability assessment must be conducted during the research and development and verification phases.

[0003] Existing physical testing methods, based on engineer experience or standard specifications, select a limited and fixed combination of extreme operating conditions to conduct long-term physical environment simulation tests on energy storage converter prototypes. The selection of test scenarios is inherently arbitrary and limited. Since extreme environmental parameters are inherently random and interdependent, simply testing a limited number of worst-case scenarios cannot systematically reveal the true failure risk and performance boundaries of energy storage converters under globally uncertain environments. This results in time-consuming, labor-intensive, and costly testing processes, potentially overlooking non-obvious high-risk environmental parameter combinations. Summary of the Invention

[0004] In view of the shortcomings of the existing technology, the purpose of this invention is to provide a method and system for protection and control of energy storage converter testing under extreme environments.

[0005] To achieve the above objectives, the present invention adopts the following technical solution: This invention provides a protection and control method for energy storage converter testing under extreme environments, comprising the following steps: constructing a multiphysics order-reduced simulation model of the energy storage converter; collecting measured performance data of the energy storage converter; calibrating the parameters of the multiphysics order-reduced simulation model based on the measured performance data to obtain a calibrated multiphysics order-reduced simulation model; acquiring accelerated aging test data of preset key components in the energy storage converter; establishing a device-level performance degradation proxy model based on the accelerated aging test data; embedding the device-level performance degradation proxy model into the calibrated multiphysics order-reduced simulation model to obtain a system-level simulation model; generating multiple sets of environmental parameter samples based on the uncertainty probability distribution; and... Environmental parameter samples are sequentially input into the system-level simulation model to obtain a performance simulation result set. The performance simulation result set is analyzed to obtain risk assessment results. Based on the risk assessment results, at least one high-risk environmental parameter combination is selected from the multiple sets of environmental parameter samples. According to the high-risk environmental parameter combination, the corresponding physical test cases are obtained, and the test equipment is driven to perform physical extreme environment tests on the energy storage converter to obtain the measured results. The measured results are compared with the prediction results obtained by the system-level simulation model based on the corresponding high-risk environmental parameter combination. Based on the comparison results, the parameters of the multiphysics order reduction simulation model and the device-level performance degradation proxy model are optimized.

[0006] Furthermore, the construction of the multiphysics order reduction simulation model of the energy storage converter includes: constructing an electrical behavior order reduction model and a thermal behavior order reduction model of the energy storage converter; establishing a coupling relationship between the electrical behavior order reduction model and the thermal behavior order reduction model; and based on the coupling relationship, coupling and combining the electrical behavior order reduction model and the thermal behavior order reduction model to obtain the multiphysics order reduction simulation model.

[0007] Furthermore, the calibration of the parameters of the multiphysics order reduction simulation model based on measured performance data includes: applying multiple preset test stimuli to the energy storage converter, simultaneously collecting electrical and thermal response data of the energy storage converter under each test stimuli to obtain the measured performance data; inputting the input conditions of the multiple test stimuli into the multiphysics order reduction simulation model to obtain corresponding simulation response data; comparing the simulation response data with the corresponding measured performance data to calculate the difference between the simulation response data and the measured performance data; and adjusting the internal parameters of the multiphysics order reduction simulation model using a parameter optimization algorithm based on the difference until the difference meets a preset convergence condition, thereby calibrating the parameters of the multiphysics order reduction simulation model.

[0008] Furthermore, the step of acquiring accelerated aging test data of preset key components in the energy storage converter and establishing a device-level performance degradation proxy model based on the accelerated aging test data includes applying accelerated aging stress to the preset key components, periodically interrupting the test to measure key performance parameters during the stress application process, recording the degradation trajectory data of the key performance parameters with stress time or stress cycle number, and obtaining the accelerated aging test data; extracting stress characteristic parameters characterizing the stress on the key components and the corresponding key performance parameter degradation amount from the accelerated aging test data; and training the device-level performance degradation proxy model based on machine learning methods using the stress characteristic parameters as input and the key performance parameter degradation amount as output.

[0009] Furthermore, embedding the device-level performance degradation proxy model into the calibrated multiphysics reduced-order simulation model to obtain a system-level simulation model includes: using the calibrated multiphysics reduced-order simulation model to simulate the real-time operating state of the energy storage converter; calculating the real-time electrothermal stress parameters borne by the preset key components under the current simulation state based on the real-time operating state; inputting the real-time electrothermal stress parameters into the device-level performance degradation proxy model to obtain the performance parameter degradation amount of the preset key components under the current state; feeding back the performance parameter degradation amount to the calibrated multiphysics reduced-order simulation model to dynamically correct the model parameters of the preset key components; advancing the simulation time step of the multiphysics reduced-order simulation model; and coupling and linking the multiphysics reduced-order simulation model and the device-level performance degradation proxy model during the simulation process based on the corrected model parameters to obtain a system-level simulation model.

[0010] Furthermore, the step of generating multiple sets of environmental parameter samples based on the uncertainty probability distribution and sequentially inputting these environmental parameter samples into the system-level simulation model to obtain a performance simulation result set includes: generating multiple independent combinations of environmental parameter samples from the uncertainty probability distribution, each combination containing a specific value for each key environmental parameter; sequentially replacing the simulation environment configuration parameters of the system-level simulation model with each of the aforementioned environmental parameter sample combinations; running the system-level simulation model under each of the aforementioned environmental parameter sample combinations to simulate the operation of the energy storage converter in the corresponding environment; recording at least one performance index result from each simulation run; and summarizing all the performance index results recorded in the simulation runs to obtain the performance simulation result set.

[0011] Further, based on the risk assessment results, at least one high-risk environmental parameter combination is selected from the multiple sets of environmental parameter samples, including: performing statistical calculations on the performance simulation result set to obtain the statistical distribution of at least one performance index; calculating the failure probability of the performance index exceeding the performance safety boundary based on the preset performance safety boundary and the statistical distribution of the performance index; assessing the performance risk level of the energy storage converter under environmental parameter uncertainty according to the failure probability; and identifying and selecting the environmental parameter sample combination that causes the performance index to be closest to or exceed the safety boundary from the multiple sets of environmental parameter samples, and marking it as the high-risk environmental parameter combination.

[0012] Furthermore, the step of obtaining corresponding physical test cases based on high-risk environmental parameter combinations and driving the test equipment to perform physical extreme environment testing on the energy storage converter includes: converting the high-risk environmental parameter combinations into a sequence of physical environment control commands that can be executed by the environmental simulation test equipment; determining the electrical load command sequence that needs to be applied to the energy storage converter in the physical test based on the simulation operating conditions corresponding to the high-risk environmental parameter combinations; synchronizing and timing-aligning the physical environment control command sequence with the electrical load command sequence to generate physical test cases; and driving the environmental simulation test equipment and the electrical load equipment to perform physical extreme environment testing on the energy storage converter based on the physical test cases.

[0013] Furthermore, the measured results are compared with the predicted results obtained from the system-level simulation model based on the corresponding high-risk environmental parameter combinations. Based on the comparison results, the parameters of the multiphysics order reduction simulation model and the device-level performance degradation proxy model are optimized, including: during the physical extreme environment test, the measured performance data of the energy storage converter are collected synchronously; the predicted performance data corresponding to the measured performance data in the performance simulation result set is extracted; the deviation between the measured performance data and the predicted performance data is calculated; it is determined whether the deviation exceeds the preset model accuracy tolerance; if the deviation exceeds the model accuracy tolerance, the internal parameters of the calibrated multiphysics order reduction simulation model and the device-level performance degradation proxy model are adjusted with the goal of reducing the deviation.

[0014] This invention also provides a protection and control system for energy storage converter testing under extreme environments, comprising: a construction module for constructing a multiphysics reduced-order simulation model of the energy storage converter, collecting measured performance data of the energy storage converter, and calibrating the parameters of the multiphysics reduced-order simulation model based on the measured performance data to obtain a calibrated multiphysics reduced-order simulation model; an acquisition module for acquiring accelerated aging test data of preset key components in the energy storage converter, and establishing a device-level performance degradation proxy model based on the accelerated aging test data; an embedding module for embedding the device-level performance degradation proxy model into the calibrated multiphysics reduced-order simulation model to obtain a system-level simulation model; and an input module for generating multiple sets of environmental parameter samples based on an uncertainty probability distribution. The system sequentially inputs environmental parameter samples into the system-level simulation model to obtain a performance simulation result set. An analysis module analyzes the performance simulation result set to obtain risk assessment results, and based on these results, selects at least one high-risk environmental parameter combination from the multiple sets of environmental parameter samples. A driving module obtains corresponding physical test cases based on the high-risk environmental parameter combinations, drives the test equipment to perform physical extreme environment tests on the energy storage converter, and obtains measured results. An optimization module compares the measured results with the prediction results obtained by the system-level simulation model based on the corresponding high-risk environmental parameter combinations, and optimizes the parameters of the multi-physics order reduction simulation model and the device-level performance degradation proxy model based on the comparison results.

[0015] Compared with the prior art, the present invention has the following beneficial technical effects: This invention discloses a protection and control method for energy storage converter testing under extreme environments. It embeds a device-level performance degradation proxy model constructed based on accelerated aging test data into a multiphysics reduced-order simulation model calibrated with measured data, resulting in a system-level simulation model that provides an accurate simulation basis for risk assessment. Uncertainty probability distributions are configured for environmental parameters, and multiple sets of environmental parameter samples are generated for simulation. Statistical analysis is performed on the performance risk of the energy storage converter under environmental parameter uncertainty, identifying high-risk environmental parameter combinations and improving the relevance and effectiveness of the test. The measured results of the physical extreme environment test are compared with the prediction results of the system-level simulation model, and the simulation model parameters are iteratively optimized based on the comparison results, forming a closed-loop mechanism of simulation-testing-calibration. This continuously corrects the parameters of the calibrated multiphysics reduced-order simulation model and the device-level performance degradation proxy model, ensuring the reliability of the energy storage converter operating under extreme environments.

[0016] By dynamically embedding a data-driven device-level performance degradation proxy model into a system-level multiphysics simulation model, the performance degradation of key components under electrothermal stress and its feedback impact on the overall system performance can be reflected in real time during the simulation. This enables virtual testing not only to evaluate system-level indicators but also to predict the evolution path of microscopic failure mechanisms under extreme environments.

[0017] The results of physical extreme environment tests are used as a standard and compared with the simulation prediction results. Once the deviation exceeds the tolerance, the parameters of the calibrated multiphysics model and device degradation model are automatically iteratively optimized. Attached Figure Description

[0018] Figure 1 This is a flowchart of a protection and control method for energy storage converter testing under extreme environments according to the present invention. Detailed Implementation

[0019] To enable those skilled in the art to better understand the present invention, the technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings of the embodiments of the present invention. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort should fall within the scope of protection of the present invention.

[0020] Example 1 This invention provides a protection and control method for energy storage converter testing under extreme environments, such as... Figure 1As shown, the process includes: constructing a multiphysics-based reduced-order simulation model of an energy storage converter; collecting measured performance data of the energy storage converter; calibrating the parameters of the multiphysics-based reduced-order simulation model based on the measured performance data to obtain a calibrated multiphysics-based reduced-order simulation model; acquiring accelerated aging test data of preset key components in the energy storage converter; establishing a device-level performance degradation proxy model based on the accelerated aging test data; embedding the device-level performance degradation proxy model into the calibrated multiphysics-based reduced-order simulation model to obtain a system-level simulation model; generating multiple sets of environmental parameter samples based on the uncertainty probability distribution; and sequentially inputting the environmental parameter samples into the system-level model. The simulation model is used to obtain a performance simulation result set. The performance simulation result set is analyzed to obtain risk assessment results. Based on the risk assessment results, at least one high-risk environmental parameter combination is selected from the multiple sets of environmental parameter samples. According to the high-risk environmental parameter combination, the corresponding physical test cases are obtained, and the test equipment is driven to conduct physical extreme environment tests on the energy storage converter to obtain the measured results. The measured results are compared with the prediction results obtained by the system-level simulation model based on the corresponding high-risk environmental parameter combination. Based on the comparison results, the parameters of the multiphysics order reduction simulation model and the device-level performance degradation proxy model are optimized.

[0021] A multiphysics-based reduced-order simulation model of an energy storage converter is constructed. Measured performance data of the energy storage converter is collected, and the parameters of the multiphysics-based reduced-order simulation model are calibrated based on the measured performance data to obtain a calibrated multiphysics-based reduced-order simulation model. This process includes applying multiple preset test stimuli to the energy storage converter in a controlled laboratory environment and simultaneously collecting its electrical and thermal response data under each test stimuli to form measured performance data; inputting the input conditions of the multiple test stimuli into the multiphysics-based reduced-order simulation model to obtain corresponding simulation response data; comparing the simulation response data with the corresponding measured performance data and calculating their data differences; and adjusting the internal parameters of the multiphysics-based reduced-order simulation model based on the data differences using a parameter optimization algorithm to ensure that the data differences meet preset convergence conditions, thus calibrating the parameters of the multiphysics-based reduced-order simulation model to obtain the calibrated multiphysics-based reduced-order simulation model.

[0022] Specifically, firstly, reduced-order models of the electrical and thermal behavior of the energy storage converter are constructed respectively.

[0023] A reduced-order electrical behavior model is used to describe the dynamic characteristics of the converter main circuit. It can be established using the state-space averaging method or the switching device averaging method. The inputs to the reduced-order electrical behavior model are the DC-side voltage, the AC-side grid voltage, and the control signal. The outputs are the AC-side current, the DC-side current, and the instantaneous losses of key power devices. The reduced-order electrical behavior model includes parasitic parameters to be determined, such as line inductance, resistance, and the conduction loss coefficients of the switching devices.

[0024] A reduced-order thermal behavior model is used to describe the thermal dynamics of key heat-generating components and the heat dissipation system in a converter. It is established using the lumped-parameter thermal network method. The model consists of multiple thermal resistance and thermal capacity nodes. The inputs to the reduced-order thermal behavior model are the power loss values ​​of each power device, and the outputs are the temperature rise curves of key components such as the junction temperature of insulated-gate bipolar transistors and the temperature of the heat sink substrate. The structural parameters of the thermal network, such as thermal resistance and thermal capacity values, also need to be determined subsequently. Next, the coupling relationship between the reduced-order electrical behavior model and the reduced-order thermal behavior model is established. Specifically, a data exchange path is established: the power device loss value calculated by the reduced-order electrical behavior model is used as a heat source input to the corresponding node of the thermal network of the reduced-order thermal behavior model; the junction temperature of the power device calculated by the reduced-order thermal behavior model is fed back to the reduced-order electrical behavior model in real time to correct temperature-related electrical parameters such as the on-state voltage drop or on-state resistance of the power device. Finally, based on this coupling relationship, the reduced-order electrical behavior model and the reduced-order thermal behavior model are coupled and combined. Specifically, in the simulation platform, the input and output interfaces of the two models are connected according to the defined coupling relationship to form a unified executable simulation module, which is the multiphysics reduced-order simulation model; in a controllable laboratory environment, the model parameters are accurately calibrated using measured data from the energy storage converter prototype. First, in a controlled laboratory environment, a series of preset test stimuli were applied to the energy storage converter prototype. These test stimuli covered its typical operating range, including steady-state conditions at different load rates and transient conditions with step load changes. Simultaneously, a data acquisition system was used to synchronously collect electrical and thermal response data of the energy storage converter under each test stimuli. The electrical response data included at least AC-side voltage and current, and DC-side voltage and current. The thermal response data included at least the case temperature of key power devices and the temperature of key points on the heat sink. These data together constituted the measured performance data. Then, the input conditions of the above multiple test stimuli were input into a multiphysics reduced-order simulation model, and the simulation was run to obtain the corresponding simulation response data. Next, the simulated response data is compared with the corresponding measured performance data, and the data differences are calculated point by point. This difference can be quantified as the sum of squares of the errors of the corresponding physical quantities at each acquisition time point, forming a scalarized objective function. Subsequently, based on this data difference, the internal parameters of the multiphysics reduced-order simulation model are adjusted using a parameter optimization algorithm. These internal parameters include parasitic resistance, inductance, and loss coefficient in the electrical model, and thermal resistance and heat capacity in the thermal model. The parameter optimization algorithm used can be gradient descent, genetic algorithm, or particle swarm optimization. The optimization objective is to minimize the calculated data difference objective function value until a preset convergence condition is met, such as the objective function value being less than a threshold or the number of iterations reaching an upper limit. When the optimization process meets the convergence condition, it is considered that the internal parameters have been calibrated to the optimal, and finally the calibrated multiphysics reduced-order simulation model is obtained. The calibrated multiphysics reduced-order simulation model can reproduce the electrothermal behavior of the tested energy storage converter in actual operation with high accuracy.

[0025] Accelerated aging test data of preset key components in the energy storage converter are obtained, and a device-level performance degradation proxy model is established based on the accelerated aging test data.

[0026] Accelerated aging stress is applied to preset key components, and the test is periodically interrupted during the stress application process to measure their key performance parameters. The degradation trajectory data of key performance parameters with stress time or stress cycle number is recorded to form accelerated aging test data. From the accelerated aging test data, stress characteristic parameters characterizing the stress on the key components and the corresponding degradation amount of key performance parameters are extracted. Using the stress characteristic parameters as input and the degradation amount of key performance parameters as output, a device-level performance degradation proxy model is trained based on machine learning methods.

[0027] Specifically, first, select pre-defined key components in the energy storage converter. These pre-defined key components are power semiconductor modules or DC support capacitors that significantly impact system reliability. Examples include insulated-gate bipolar transistor (IGBT) modules, metal-oxide-semiconductor (MOSFET) field-effect transistor (MOSFET) modules, aluminum electrolytic capacitors, and film capacitors. Next, apply accelerated aging stress to the selected key components. For power semiconductor modules, accelerated aging stress is typically active power cycling stress or passive temperature cycling stress. For DC support capacitors, accelerated aging stress is typically high-temperature voltage stress. During stress application, the test needs to be periodically interrupted to measure the critical performance parameters of the key components. For power semiconductor modules, critical performance parameters include saturation voltage drop, threshold voltage, and thermal resistance. For DC support capacitors, critical performance parameters include equivalent series resistance and capacitance. Then, record the changes in critical performance parameters over stress time or stress cycle count to form degradation trajectory data. This data should record the complete performance degradation process until device failure or performance parameters exceeding technical specifications. All recorded degradation trajectory data constitute accelerated aging test data.

[0028] Features for modeling are extracted from accelerated aging test data. These extracted features include stress characteristic parameters characterizing the stress experienced by critical components and the corresponding degradation of key performance parameters. Stress characteristic parameters are determined based on the stress type. For power cycling stress, stress characteristic parameters include junction temperature fluctuation amplitude, average junction temperature, and cycling frequency. For temperature cycling stress, stress characteristic parameters include ambient temperature fluctuation amplitude and cycling frequency. For high-temperature voltage stress, stress characteristic parameters include ambient temperature and applied voltage. The degradation of key performance parameters is defined as the relative change between the current measured value and the initial measured value. For example, the degradation of saturation voltage drop can be expressed as the increment of the current saturation voltage drop relative to the initial saturation voltage drop. Next, a training dataset is constructed using stress characteristic parameters as input and degradation amount as the key performance parameter as output. Then, based on this training dataset, a device-level performance degradation surrogate model is trained using machine learning methods. This device-level performance degradation surrogate model aims to establish a mapping relationship from stress state to performance degradation amount. A feasible model form is a multiple linear regression model. The mathematical expression of the multiple linear regression model is:

[0029] in, The degradation amount of the key performance parameter is a scalar output. , , These are stress characteristic parameters, forming the input vector; For constant terms; , , The regression coefficients represent the weights of each stress characteristic parameter on the degradation of key performance parameters, and are determined by fitting the training data using the least squares method.

[0030] The least squares method is used to fit a multiple linear regression model. The goal is to find a set of model coefficients that minimizes the mean squared error between the model's predicted value ΔP and the actual degradation in the training data. Mean squared error The calculation formula is:

[0031] Where m is the number of training samples; This represents the actual amount of degradation. The amount of degradation predicted by the model; The model coefficients can be determined by solving the least squares optimization problem. , , Thus, a device-level performance degradation proxy model is obtained.

[0032] Embedding the device-level performance degradation proxy model into the calibrated multiphysics reduced-order simulation model to obtain a system-level simulation model includes: simulating the real-time operating state of the energy storage converter using the calibrated multiphysics reduced-order simulation model; calculating the real-time electrothermal stress parameters of the preset key components under the current simulation state based on the real-time operating state; inputting the real-time electrothermal stress parameters into the device-level performance degradation proxy model to obtain the performance parameter degradation amount of the preset key components under the current state; feeding back the performance parameter degradation amount to the calibrated multiphysics reduced-order simulation model to dynamically correct the model parameters of the preset key components; advancing the simulation time step of the multiphysics reduced-order simulation model; and coupling and linking the multiphysics reduced-order simulation model and the device-level performance degradation proxy model during the simulation process based on the corrected model parameters to obtain a system-level simulation model.

[0033] Specifically, the multiphysics reduced-order simulation model with calibrated parameters is run. This model starts with the set input conditions and begins simulating the real-time operating state of the energy storage converter. The real-time operating state includes DC-side voltage, AC-side current, the switching state of power devices, and the temperature of each node in the thermal network. Then, based on this real-time operating state, the real-time electrothermal stress parameters borne by preset key components under the current simulation state are calculated. For the power semiconductor module, the real-time electrothermal stress parameters include its junction temperature Tj and the effective current value Irms through the device. The junction temperature Tj is directly output from the reduced-order thermal behavior model, and the effective current value Irms is calculated from the current waveform output by the reduced-order electrical behavior model. The calculation formula is as follows:

[0034] Where T is the calculation period and i(t) is the instantaneous current; Then, the calculated real-time electrothermal stress parameters are used as input to the device-level performance degradation surrogate model. The stress parameters are substituted into the surrogate model:

[0035] in, This refers to the degradation of key performance parameters. For constant terms, for The regression coefficients, For real-time junction temperature of key components, for The regression coefficients, This is the effective value of the current.

[0036] The degradation of the performance parameters of key components in the current state can then be calculated. . It characterizes the degree of device performance degradation caused by the current stress, such as the increment of saturation voltage drop.

[0037] Subsequently, the performance parameter degradation amount Feedback is fed back to the running multiphysics order reduction simulation model to dynamically correct the model parameters of preset key components. The specific correction method is as follows: based on the performance parameter degradation... Adjust the parameters used to describe the electrical characteristics of this key device in the reduced-order electrical behavior model. For example, if To represent the saturation voltage drop increment in the degradation of key performance parameters, the equivalent on-resistance of the device in the electrical model is then used. Revised to: *(1+k ΔP) in, Let be the initial on-resistance, and k be a scaling factor that maps performance degradation to resistance change. This factor can be obtained from the device datasheet or through experimental calibration. The corrected on-resistance will directly affect the loss calculation in subsequent simulation steps.

[0038] After completing the model parameter correction at the current simulation moment, advance the simulation time step of the multiphysics reduced-order simulation model by one step. At the new simulation moment, based on the corrected model parameters, the multiphysics reduced-order simulation model is run again to simulate the new real-time operating state of the energy storage converter. Then, based on this new state, the real-time electrothermal stress parameters of the preset key components are recalculated. Next, the new stress parameters are input into the device-level performance degradation proxy model to obtain the new performance parameter degradation. Finally, the new degradation is fed back into the multiphysics reduced-order simulation model to perform a new round of dynamic correction on the model parameters of the key components.

[0039] The process of simulating real-time operation, calculating real-time electrothermal stress parameters, obtaining performance parameter degradation, and dynamically correcting model parameters is repeated iteratively. Each iteration represents a simulation step, and the device performance state and the system operation state evolve synchronously and influence each other on the simulation timeline. Through this continuous data exchange and parameter update on the simulation time step, the multiphysics reduced-order simulation model and the device-level performance degradation proxy model achieve tight coupling and linkage. This coupled and integrated executable simulation, which incorporates electrical, thermal, and device degradation behaviors, is the system-level simulation model. This model can simulate the overall performance degradation process of the energy storage converter due to the gradual degradation of key device performance during long-term operation or under extreme stress.

[0040] Identify the key environmental parameters affecting the operation of the energy storage converter and configure corresponding uncertainty probability distributions for these parameters. Identify external environmental factors that directly affect the electrical performance, thermal performance, or mechanical structure of the energy storage converter when it operates in extreme environments. From external environmental factors, parameters with random fluctuation characteristics and whose fluctuations can be quantified are selected as key environmental parameters; based on historical environmental observation data, geographical and climatic information, or engineering experience, the statistical distribution characteristics of each key environmental parameter are defined; according to the statistical distribution characteristics, a corresponding uncertainty probability distribution is configured for each key environmental parameter.

[0041] Specifically, first, identify the external environmental factors that directly affect the electrical, thermal, or mechanical performance of the energy storage converter when operating in extreme environments. These factors are determined based on the application scenario. Taking a high-altitude desert photovoltaic energy storage scenario as an example, directly influencing factors include ambient temperature, solar irradiance, ambient wind speed and direction, and dust concentration in the air. Ambient temperature directly affects heat dissipation efficiency and component ratings. Solar irradiance affects the output of photovoltaic modules, which in turn affects the DC input power of the converter. Ambient wind speed and direction affect the heat dissipation efficiency of forced air-cooled radiators. Dust concentration may affect airflow blockage and insulation performance.

[0042] Next, from the aforementioned external environmental factors, parameters exhibiting random fluctuation characteristics and whose fluctuations are quantifiable are selected as key environmental parameters. The selection principle is that the parameter values ​​change randomly over time and can be measured or estimated numerically. Based on this principle, ambient temperature, ambient wind speed, and solar irradiance are typically selected as key environmental parameters. Dust concentration, due to its complexity in instantaneous measurement and quantitative modeling, is treated as a fixed background condition in this embodiment and is not considered a random variable. Define the statistical distribution characteristics for each key environmental parameter. These characteristics include distribution type, mean, and standard deviation. The definitions are based on historical environmental observation data, geographic climate information, or engineering experience of the target deployment site. For ambient temperature, its diurnal variation approximately follows a sinusoidal law, but the daily maximum and minimum temperatures exhibit randomness. Therefore, the daily maximum ambient temperature can be used as the basis for this definition. The model is based on a random variable. Based on historical meteorological data, it is assumed that the daily maximum temperature in the region during summer follows a mean of [missing value]. Standard deviation is The normal distribution is denoted as: ( ),in This is the average of the highest daily temperatures recorded in historical summer days, for example, 45°C; Its standard deviation, for example, 5°C; For ambient wind speed v, its statistical properties are typically described by the Weibull distribution. Based on historical wind speed data, the shape parameter k and scale parameter c of the Weibull distribution are fitted. Its probability density function... for:

[0043] in, For shape parameters, For scale parameters, This refers to wind speed.

[0044] Solar irradiance G can be accurately calculated based on a solar position model under clear sky conditions, but it exhibits random fluctuations due to cloud cover. The total solar irradiance at local noon can be used as a reference. As a random variable, based on historical data, it can be modeled as the irradiance under ideal clear sky conditions. A truncated normal distribution of nearby fluctuations, for example:

[0045] in, The average irradiance, Standard deviation; Then, based on the environmental temperature distribution characteristics, environmental wind speed distribution characteristics, and solar irradiance distribution characteristics defined above, a corresponding uncertainty probability distribution is configured for each key environmental parameter. That is, a normal distribution is configured for the daily maximum environmental temperature. We assign a Weibull distribution (Weibull(k,c)) to the ambient wind speed and a truncated normal distribution to the midday solar irradiance. .

[0046] Multiple sets of environmental parameter samples are generated based on the uncertainty probability distribution. These environmental parameter samples are then sequentially input into a system-level simulation model to obtain a performance simulation result set. This process includes generating multiple independent combinations of environmental parameter samples from the uncertainty probability distribution, with each combination containing a specific value for each key environmental parameter; sequentially replacing the simulation environment configuration parameters of the system-level simulation model with each of these environmental parameter sample combinations; running the system-level simulation model under each of these environmental parameter sample combinations to simulate the operation of the energy storage converter in the corresponding environment; recording at least one performance index result from each simulation run; and summarizing all the performance index results recorded in the simulation runs to obtain the performance simulation result set.

[0047] First, based on the uncertainty probability distribution of each key environmental parameter, a large number of independent environmental parameter sample combinations are generated using the Monte Carlo sampling method. The total number of samplings is set to N, for example, N=10000. Each sampling generates a set of environmental parameter sample combinations. The sample combination generated by the i-th sampling is:

[0048] in, From normal distribution A randomly selected daily maximum temperature value; It is a wind speed value randomly selected from the Weibull distribution Weibull(k,c); From the truncated normal distribution (TruncatedNormal) , ,0, A random midday solar irradiance value is selected from the sample. Through N independent samplings, N sets of environmental parameter sample combinations are generated, forming an N-row, 3-column sample matrix; Next, the simulation environment configuration parameters of the system-level simulation model are sequentially replaced with the environmental parameter sample combinations generated above. System-level simulation models typically have a preset set of baseline environmental configuration parameters, including baseline ambient temperature, baseline wind speed, and baseline irradiance. For the i-th sample combination... Set the ambient temperature parameter to Set the wind speed parameter to Set the solar irradiance parameter to Meanwhile, based on solar irradiance The corresponding DC-side input power is calculated using a photovoltaic array model, and this power value is used as one of the DC input conditions for the system-level simulation model. Then, the system-level simulation model is run under each set of configured environmental parameter samples. The total simulation duration is 24 hours. The complete operation process of the energy storage converter under the random environment of the day will be simulated, including charge-discharge switching, power regulation, and the resulting electrothermal dynamics.

[0049] During each simulation run, record at least one performance metric output from the system-level simulation model. The performance metric should include at least the maximum junction temperature of the preset key components. For the i-th simulation run, record the maximum junction temperature of the key components as output. After completing all N simulation runs, summarize the performance index results recorded from all simulation runs. Include the N recorded maximum junction temperature values. The parameters are arranged in the order of simulation execution, forming an N-row, 1-column performance index vector. Together with the corresponding N sets of environmental parameter sample combination matrices, they constitute the performance simulation result set.

[0050] The performance simulation result set is analyzed to obtain risk assessment results. Based on the risk assessment results, at least one high-risk combination of environmental parameters is selected from the multiple sets of environmental parameter samples.

[0051] Statistical calculations are performed on the performance simulation result set to obtain the statistical distribution of at least one performance index; based on the preset performance safety boundary and the statistical distribution of the performance index, the failure probability of the performance index exceeding the performance safety boundary is calculated; the performance risk level of the energy storage converter under environmental parameter uncertainty is assessed according to the failure probability; from the multiple sets of environmental parameter samples, the combination of environmental parameter samples that causes the performance index to be closest to or exceed the safety boundary is identified and screened as the high-risk environmental parameter combination.

[0052] First, statistical calculations are performed on the performance simulation result set to obtain the statistical distribution of key performance indicators. Taking the aforementioned maximum junction temperature as an example... For example, for a set containing N samples { Perform statistical analysis. Calculate its empirical cumulative distribution function. Under random conditions, the maximum junction temperature Less than or equal to a specific value The estimated probability. The calculation formula is: = (Quantity) , where i ranges from 1 to N; Next, based on the preset performance safety boundary and the statistical distribution of performance indicators, the failure probability of performance indicators exceeding the safety boundary is calculated. The preset junction temperature safety boundary value for key components is... This value is specified by the device manufacturer, for example, 150°C. A performance failure event is defined as the maximum junction temperature exceeding the safety boundary, i.e. Based on the empirical cumulative distribution function Failure probability It can be estimated as follows: It directly quantifies the risk level of key component failure caused by overheating in energy storage converters under given environmental uncertainties; Then, based on the failure probability The magnitude of the risk level is used to assess the performance risk level of the energy storage converter under environmental parameter uncertainties. The risk level classification rules are defined as follows: If... ≤0.001 is assessed as low risk; if 0.001 0.01, assessed as medium risk; if A value of 0.01 is classified as high risk. The risk level of the current design can be determined by comparing the calculated Pf value with this rule. If the risk assessment results show that there are risks that cannot be ignored, for example If the value is greater than 0.001, then it is necessary to identify and filter the environmental parameter sample combinations that cause the performance index to be closest to or exceed the safety boundary from multiple sets of environmental parameter samples. The specific method is to traverse the performance simulation result set and find all the environmental parameter samples that meet the requirements. The index corresponding to the simulation record ,in A threshold coefficient close to 1, such as 0.95, is used to filter out simulation conditions that cause the junction temperature to reach or approach the safety boundary, from all indexes that meet the condition. In the middle, select the one that makes The top M indices with the largest values ​​are selected, where M is the set number of filters, for example, M=5. Then, based on these indices, the corresponding M sets of environmental parameter sample combinations are extracted from the generated N sets of environmental parameter sample combination matrices. For example, the sample combination corresponding to index i is... = These M sets of selected environmental parameter samples represent high-risk environmental parameter combinations. They represent the extreme environmental conditions that are most likely to cause performance failure or have already caused simulation failure in a probabilistic sense, and are the target scenarios that need to be prioritized for verification in subsequent physical testing.

[0053] Based on the combination of high-risk environmental parameters, corresponding physical test cases are obtained, and the test equipment is driven to perform physical extreme environment tests on the energy storage converter to obtain the test results. This includes converting the combination of high-risk environmental parameters into a sequence of physical environment control commands that can be executed by the environmental simulation test equipment; determining the sequence of electrical load commands that need to be applied to the energy storage converter in the physical test based on the simulation operating conditions corresponding to the combination of high-risk environmental parameters; synchronizing and aligning the physical environment control command sequence with the electrical load command sequence to generate physical test cases; and driving the environmental simulation test equipment and electrical load equipment to perform physical extreme environment tests on the energy storage converter based on the physical test cases.

[0054] First, the selected high-risk environmental parameter combinations are transformed into a sequence of physical environment control commands that can be executed by environmental simulation testing equipment. This is done using a set of high-risk environmental parameter combinations. For example, this combination corresponds to a simulated day lasting 24 hours. The conversion process is as follows: based on the solar irradiance variation model for that day, combined with... This generates a 24-hour light intensity variation curve. Simultaneously, based on a meteorological model, [the following is also done / is]... As the daily maximum temperature, a 24-hour ambient temperature variation curve is generated. Wind speed is also considered. The wind speed curves are generated either as constant values ​​or according to typical diurnal variation patterns. Then, the time-series data of these environmental parameters are compiled by the equipment control software into temperature control command sequences for the temperature and humidity test chamber, wind speed control command sequences for the wind tunnel or fan array, and irradiance control command sequences for the solar simulator. These command sequences precisely specify the setpoints for each environmental parameter at every moment within the test cycle. Next, based on the simulation operating conditions corresponding to this high-risk environmental parameter combination, the sequence of electrical load commands that need to be applied to the energy storage converter during physical testing is determined. In the corresponding simulation, the power dispatch commands during the system-level simulation model operation are known. Based on the power curves within the simulation day, the active power commands of the energy storage converter are extracted. and reactive power command A sequence that varies with time t. This sequence defines the charge and discharge power commands that the converter should execute on the test day; Then, the above physical environment control command sequence and electrical load command sequence are synchronized and time-aligned. A unified timeline is established with a time resolution of [missing information]. For example, 1 minute. Make sure at every identical time point... In this context, environmental commands and electrical load commands take effect simultaneously. These two synchronized command sequences are integrated to generate a complete, timestamp-aligned entity test case file. This file can be in tabular or script format, with each row containing a timestamp, temperature setpoint, wind speed setpoint, irradiance setpoint, active power setpoint, and reactive power setpoint. Finally, based on the generated physical test cases, the environmental simulation test equipment and electrical load equipment are driven to perform physical extreme environment testing on the energy storage converter. The energy storage converter prototype is placed in a temperature and humidity test chamber, with its AC side connected to a grid simulator or programmable AC load, and its DC side connected to a battery simulator or actual battery pack. The physical test case file is loaded into the test control host computer. According to the time sequence in the file, the test control host computer simultaneously sends environmental control commands to the temperature and humidity test chamber and the solar simulator, and power commands to the grid simulator and the battery simulator via the communication bus. All equipment operates synchronously according to the commands, exposing the energy storage converter prototype to extreme comprehensive stress under load conditions that are accurately reproduced by a combination of high-risk environmental parameters, completing a full test cycle of physical extreme environment testing. During the test, the data acquisition system synchronously records various actual response data of the prototype as the measured results.

[0055] The measured results are compared with the predicted results obtained by the system-level simulation model based on the corresponding high-risk environmental parameter combination simulation. Based on the comparison results, the parameters of the multiphysics order reduction simulation model and the device-level performance degradation proxy model are optimized. This includes simultaneously collecting the measured performance data of the energy storage converter during the physical extreme environment test; extracting the predicted performance data corresponding to the measured performance data from the performance simulation result set; calculating the deviation between the measured performance data and the predicted performance data; determining whether the deviation exceeds the preset model accuracy tolerance; if the deviation exceeds the model accuracy tolerance, adjusting the internal parameters of the calibrated multiphysics order reduction simulation model and the device-level performance degradation proxy model with the goal of reducing the deviation.

[0056] First, during the physical extreme environment testing, the measured performance data of the energy storage converter are collected simultaneously. The collected data includes at least the measured case temperature of preset key components. This data is obtained through a thermocouple sensor attached to the device housing, and at the same time resolution. Record the data, then extract the predicted performance data from the performance simulation result set that corresponds to the measured performance data. Within the performance simulation result set, locate a set of simulation run records that perfectly correspond to the currently executed entity test case. From this record, extract the predicted junction temperatures of the system-level simulation model, operating under the same high-risk environmental parameter combination, and for the preset key components. Since the simulation model directly outputs the junction temperature, while the actual measurement is the case temperature, it is necessary to establish the relationship between the two. Based on the thermal characteristics of the device, the junction temperature... With shell temperature There is an approximately linear relationship between them: ;in, The thermal resistance of the junction to the shell, This represents device loss. The measured case temperature will be used for measurement. Device loss values ​​at the same time as in the simulation : +

[0057] Then, the deviation between the measured performance data and the predicted performance data is calculated. This deviation is defined as the measured-to-estimated junction temperature sequence. With predicted junction temperature sequence Root mean square value of deviation at each time point within the entire test duration T The calculation formula is:

[0058] in, This represents the total number of sampling points. For the first Each sampling time; Next, determine the calculated root mean square deviation. Does it exceed the preset model accuracy tolerance? Model accuracy tolerance Set according to project requirements, for example =5℃. If RMSE≤ If the prediction accuracy is satisfactory, the current model parameters do not need optimization. If the RMSE is... If the model is found to have a significant bias, the model parameters need to be adjusted to reduce this bias. The adjustment targets are the internal parameters of the calibrated multiphysics reduced-order simulation model and / or the device-level performance degradation proxy model. For multiphysics order-reduced simulation models, the thermal resistance and thermal capacity parameters related to the heat dissipation paths of key components in the thermal sub-model are the key targets for adjustment. Let the vector of thermal resistance parameters to be adjusted be... For the device-level performance degradation surrogate model, its regression coefficient vector It is an object to be adjusted; Parameter optimization is performed using gradient descent. The loss function is defined. The aforementioned root mean square error The goal of optimization is to find a new set of parameters ( , ), making the loss function Minimize. The iterative formula for gradient descent is: ,in This represents a vector consisting of all parameters to be optimized. For learning rate, For the loss function in parameters The gradient at the given point. Through multiple iterations, the parameters are continuously updated until the loss function is reached. Converging to the minimum or below the tolerance ; After completing parameter optimization, the optimized parameters will be... Update the multiphysics order reduction simulation model to Updated to a device-level performance degradation proxy model.

[0059] Example 2 A protection and control system for energy storage converter testing under extreme environments includes: a construction module, an acquisition module, an embedding module, an input module, an analysis module, a drive module, and an optimization module. The construction module is used to construct a multiphysics-based reduced-order simulation model of the energy storage converter, collect measured performance data of the energy storage converter, and calibrate the parameters of the multiphysics-based reduced-order simulation model based on the measured performance data to obtain a calibrated multiphysics-based reduced-order simulation model. The acquisition module is used to acquire accelerated aging test data of preset key components in the energy storage converter and establish a device-level performance degradation proxy model based on the accelerated aging test data. The embedding module is used to embed the device-level performance degradation proxy model into the calibrated multiphysics-based reduced-order simulation model to obtain a system-level simulation model. The input module is used to generate multiple sets of environmental parameter samples based on the uncertainty probability distribution and input the environmental parameter samples sequentially into the system. The system employs a multi-physics simulation model to obtain a performance simulation result set. An analysis module analyzes this result set to obtain risk assessment results and, based on these results, selects at least one high-risk environmental parameter combination from the multiple sets of environmental parameter samples. A driving module acquires corresponding physical test cases based on the high-risk environmental parameter combinations and drives testing equipment to perform physical extreme environment tests on the energy storage converter, obtaining measured results. An optimization module compares the measured results with the prediction results obtained from the system-level simulation model based on the corresponding high-risk environmental parameter combinations, and optimizes the parameters of the multi-physics reduced-order simulation model and the device-level performance degradation proxy model based on the comparison results.

[0060] Example 3 The present invention also provides a computer device, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement the steps of the extreme environment energy storage converter test protection control method described in any of the above embodiments.

[0061] In another embodiment of the present invention, a terminal device is provided, comprising a processor and a memory. The memory stores a computer program, which includes program instructions. The processor executes the program instructions stored in the memory. The processor may be a Central Processing Unit (CPU), or other general-purpose processors, digital signal processors (DSPs), application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. It is the computing and control core of the terminal, suitable for implementing one or more instructions, specifically suitable for loading and executing one or more instructions to achieve corresponding method flows or corresponding functions. The processor described in this embodiment of the present invention can realize the operation of the energy storage converter test protection control method under extreme environments.

[0062] Example 4 The present invention also provides a computer-readable storage medium storing a computer program, wherein when the computer program is executed by a processor, the computer program implements the steps of the extreme environment energy storage converter test protection and control method described in any of the above embodiments.

[0063] In another embodiment of the present invention, a storage medium is provided, specifically a computer-readable storage medium (Memory). This computer-readable storage medium is a memory device in a terminal device used to store programs and data. It is understood that the computer-readable storage medium here can include both the built-in storage medium in the terminal device and extended storage media supported by the terminal device. The computer-readable storage medium provides storage space containing the terminal's operating system. Furthermore, this storage space also contains one or more instructions suitable for loading and execution by a processor. These instructions can be one or more computer programs (including program code). It should be noted that the computer-readable storage medium here can be a high-speed RAM memory or non-volatile memory, such as at least one disk storage device. The processor can load and execute one or more instructions stored in the computer-readable storage medium to implement the corresponding steps of the energy storage converter test protection and control method in the above embodiments related to extreme environments.

[0064] Those skilled in the art will understand that embodiments of this application can be provided as methods, systems, or computer program products. Therefore, this application can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, this application can take the form of a computer program product embodied on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0065] This application is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of this application. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, generate instructions for implementing the flowchart... Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0066] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.

[0067] These computer program instructions may also be loaded onto a computer or other programmable data processing equipment to cause a series of operational steps to be performed on the computer or other programmable equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable equipment for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.

[0068] It should be noted that the terms "first," "second," etc., in the specification, claims, and accompanying drawings of this invention are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that such data can be interchanged where appropriate so that the embodiments of the invention described herein can be implemented in orders other than those illustrated or described herein. Furthermore, the terms "comprising" and "having," and any variations thereof, are intended to cover a non-exclusive inclusion; for example, a process, method, system, product, or apparatus that comprises a series of steps or units is not necessarily limited to those steps or units explicitly listed, but may include other steps or units not explicitly listed or inherent to such processes, methods, products, or apparatus.

Claims

1. A protection and control method for energy storage converter testing under extreme environments, characterized in that, Includes the following steps: A multiphysics order-reduced simulation model of an energy storage converter is constructed. Measured performance data of the energy storage converter is collected. The parameters of the multiphysics order-reduced simulation model are calibrated based on the measured performance data to obtain the calibrated multiphysics order-reduced simulation model. Acquire accelerated aging test data of preset key components in the energy storage converter, and establish a device-level performance degradation proxy model based on the accelerated aging test data; The device-level performance degradation proxy model is embedded into the calibrated multiphysics reduced-order simulation model to obtain a system-level simulation model; Multiple sets of environmental parameter samples are generated based on the uncertainty probability distribution. The environmental parameter samples are then sequentially input into the system-level simulation model to obtain the performance simulation result set. The performance simulation result set is analyzed to obtain risk assessment results. Based on the risk assessment results, at least one high-risk combination of environmental parameters is selected from the multiple sets of environmental parameter samples. Based on the combination of high-risk environmental parameters, obtain the corresponding physical test cases, drive the test equipment to perform physical extreme environment tests on the energy storage converter, and obtain the actual test results; The measured results are compared with the prediction results obtained by the system-level simulation model based on the corresponding high-risk environmental parameter combination simulation. Based on the comparison results, the parameters of the multiphysics order reduction simulation model and the device-level performance degradation proxy model are optimized.

2. The method for protection and control of energy storage converter testing under extreme environments according to claim 1, characterized in that, The multiphysics-based reduced-order simulation model for the energy storage converter includes: Construct reduced-order models of the electrical and thermal behavior of the energy storage converter; Establish the coupling relationship between the reduced-order electrical behavior model and the reduced-order thermal behavior model; Based on the aforementioned coupling relationship, the reduced-order model of electrical behavior and the reduced-order model of thermal behavior are coupled and combined to obtain a multiphysics reduced-order simulation model.

3. The method for protection and control of energy storage converter testing under extreme environments according to claim 2, characterized in that, The calibration of the parameters of the multiphysics order reduction simulation model based on measured performance data includes: Multiple preset test stimuli are applied to the energy storage converter, and the electrical response data and thermal response data of the energy storage converter under each test stimuli are collected simultaneously to obtain the measured performance data; The input conditions of the multiple test stimuli are respectively input into the multiphysics reduced-order simulation model to obtain the corresponding simulation response data; The simulation response data is compared with the corresponding measured performance data, and the difference between the simulation response data and the measured performance data is calculated. Based on the differences, the internal parameters of the multiphysics reduced-order simulation model are adjusted using a parameter optimization algorithm until the differences meet the preset convergence conditions, thereby calibrating the parameters of the multiphysics reduced-order simulation model.

4. The method for protection and control of energy storage converter testing under extreme environments according to claim 1, characterized in that, The process involves acquiring accelerated aging test data of preset key components in the energy storage converter, and establishing a device-level performance degradation proxy model based on the accelerated aging test data, including... Accelerated aging stress is applied to the preset key components. During the stress application process, the key performance parameters are periodically interrupted for testing and measurement. The degradation trajectory data of the key performance parameters with stress time or stress cycle number is recorded to obtain the accelerated aging test data. From the accelerated aging test data, extract the stress characteristic parameters characterizing the stress on the key components and the corresponding degradation amount of key performance parameters. Using the stress characteristic parameters as input and the degradation amount of the key performance parameters as output, the device-level performance degradation proxy model is trained based on machine learning methods.

5. The method for protection and control of energy storage converter testing under extreme environments according to claim 1, characterized in that, Embedding the device-level performance degradation proxy model into the calibrated multiphysics reduced-order simulation model yields a system-level simulation model including: The real-time operating state of the energy storage converter was simulated using a calibrated multiphysics reduced-order simulation model. Based on the real-time operating status, calculate the real-time electrothermal stress parameters that the preset key components bear under the current simulation state; The real-time electrothermal stress parameters are input into the device-level performance degradation proxy model to obtain the performance parameter degradation amount of the preset key device in the current state; The degradation of the performance parameters is fed back into the calibrated multiphysics reduced-order simulation model to dynamically correct the model parameters of the preset key components; The simulation time step of the multiphysics reduced-order simulation model is advanced, and based on the corrected model parameters, the multiphysics reduced-order simulation model and the device-level performance degradation proxy model are coupled and linked during the simulation process to obtain a system-level simulation model.

6. The method for protection and control of energy storage converter testing under extreme environments according to claim 1, characterized in that, The process involves generating multiple sets of environmental parameter samples based on the uncertainty probability distribution, sequentially inputting these environmental parameter samples into a system-level simulation model, and obtaining a performance simulation result set, including: The uncertainty probability distribution generates multiple independent combinations of environmental parameter samples, each combination of environmental parameter samples containing a specific value of each key environmental parameter. Replace the simulation environment configuration parameters of the system-level simulation model with the environmental parameter sample combinations described in each group in turn; Under each set of environmental parameter sample combinations, the system-level simulation model is run to simulate the operation of the energy storage converter under the corresponding environment; Record at least one performance metric result from each simulation run; The performance index results of all simulation run records are summarized to obtain the performance simulation result set.

7. The method for protection and control of energy storage converter testing under extreme environments according to claim 1, characterized in that, Based on the risk assessment results, at least one high-risk combination of environmental parameters is selected from the multiple sets of environmental parameter samples, including: Statistical calculations are performed on the performance simulation result set to obtain the statistical distribution of at least one performance index; Based on the preset performance safety boundary and the statistical distribution of the performance index, calculate the failure probability of the performance index exceeding the performance safety boundary; The performance risk level of the energy storage converter under environmental parameter uncertainty is assessed based on the failure probability. From the multiple sets of environmental parameter samples, identify and filter the environmental parameter sample combinations that cause the performance index to be closest to or exceed the safety boundary, and mark them as the high-risk environmental parameter combinations.

8. The method for protection and control of energy storage converter testing under extreme environments according to claim 1, characterized in that, The step of obtaining corresponding physical test cases based on high-risk environmental parameter combinations and driving the test equipment to perform physical extreme environment tests on the energy storage converter includes: The high-risk environmental parameters are combined and transformed into a sequence of physical environment control instructions that can be executed by environmental simulation testing equipment; Based on the simulation operating conditions corresponding to the high-risk environmental parameter combination, determine the electrical load command sequence that needs to be applied to the energy storage converter during physical testing; The physical environment control command sequence and the electrical load command sequence are synchronized and time-aligned to generate entity test cases; Based on the physical test cases, the environmental simulation test equipment and electrical load equipment are driven to perform physical extreme environment tests on the energy storage converter.

9. The method for protection and control of energy storage converter testing under extreme environments according to claim 1, characterized in that, The measured results are compared with the prediction results obtained by the system-level simulation model based on the corresponding high-risk environmental parameter combination simulation. Based on the comparison results, the parameters of the multiphysics order reduction simulation model and the device-level performance degradation proxy model are optimized, including: During the physical extreme environment test, the test performance data of the energy storage converter are collected simultaneously; Extract the predicted performance data from the performance simulation result set that corresponds to the measured performance data; Calculate the deviation between the measured performance data and the predicted performance data; Determine whether the deviation exceeds the preset model accuracy tolerance; If the deviation exceeds the model accuracy tolerance, the internal parameters of the calibrated multiphysics reduced-order simulation model and the device-level performance degradation proxy model are adjusted with the goal of reducing the deviation.

10. A protection and control system for energy storage converter testing under extreme environments, characterized in that: The construction module is used to build a multiphysics order-reduced simulation model of the energy storage converter, collect the measured performance data of the energy storage converter, and calibrate the parameters of the multiphysics order-reduced simulation model based on the measured performance data to obtain the calibrated multiphysics order-reduced simulation model. Acquisition module: used to acquire accelerated aging test data of preset key components in the energy storage converter, and to establish a device-level performance degradation proxy model based on the accelerated aging test data; Embedding module: used to embed the device-level performance degradation proxy model into the calibrated multiphysics reduced-order simulation model to obtain a system-level simulation model; Input module: Used to generate multiple sets of environmental parameter samples based on the uncertainty probability distribution, and input the environmental parameter samples into the system-level simulation model in sequence to obtain the performance simulation result set; Analysis module: used to analyze the performance simulation result set, obtain risk assessment results, and select at least one high-risk environmental parameter combination from the multiple sets of environmental parameter samples based on the risk assessment results; Drive module: Used to obtain corresponding physical test cases based on high-risk environmental parameter combinations, drive test equipment to perform physical extreme environment tests on the energy storage converter, and obtain actual test results; Optimization module: This module compares the measured results with the prediction results obtained from the simulation of the system-level simulation model based on the corresponding high-risk environmental parameter combination, and optimizes the parameters of the multiphysics order reduction simulation model and the device-level performance degradation proxy model based on the comparison results.

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