A Method for Accelerated Life Analysis and Evaluation of Power Modules

Abstract

The present invention relates to the technical field of power module testing, and particularly to a method for accelerating the life analysis and evaluation of power modules. The method includes the following steps: obtaining power module accelerated test data; calculating a temperature acceleration factor based on the power module accelerated test data to obtain temperature acceleration factor data, and calculating a normal temperature life based on the temperature acceleration factor data to obtain normal temperature life data; extracting failure times from the power module accelerated test data to obtain failure time data; constructing a failure probability model based on the failure time data to obtain a failure probability model; predicting a failure life based on the failure probability model to obtain failure life data; calculating an acceleration factor based on the normal temperature life data and the failure life data to obtain acceleration factor data; and obtaining power module accelerated life data based on the acceleration factor data and the failure life data. The present invention makes the life prediction of power modules closer to the actual use environment.

Description

Technical Field

[0001] The present invention relates to the technical field of power module testing, and particularly to a method for accelerated life analysis and evaluation of power modules. Background Art

[0002] With the popularization of modern electronic devices, especially in the fields of power electronics, automation control, transportation, and new energy vehicles, power modules play a crucial role in power conversion, regulation, and control as key components. Power modules carry high-voltage and large-current loads and are long-term in extreme working conditions such as high temperature and high load. The reliability of their performance directly affects the stability and life of the entire system. Therefore, the life assessment and reliability analysis of power modules are one of the core tasks to ensure the long-term stable operation of the system. Summary of the Invention

[0003] To solve the above technical problems, the present invention proposes a method for accelerated life analysis and evaluation of power modules to solve at least one of the above technical problems.

[0004] The present application provides a method for accelerated life analysis and evaluation of power modules, including the following steps:

[0005] Obtain power module accelerated test data;

[0006] Calculate the temperature acceleration factor according to the power module accelerated test data to obtain temperature acceleration factor data, and calculate the normal temperature life according to the temperature acceleration factor data to obtain normal temperature life data;

[0007] Extract the failure time according to the power module accelerated test data to obtain failure time data; construct a failure probability model according to the failure time data to obtain a failure probability model; predict the failure life according to the failure probability model to obtain failure life data;

[0008] Calculate the acceleration factor according to the normal temperature life data and the failure life data to obtain acceleration factor data;

[0009] Predict the life under actual use conditions according to the acceleration factor data and the failure life data to obtain power module accelerated life data.

[0010] In the present invention, by combining the temperature acceleration factor and the failure time data, the temperature and load conditions in the accelerated test can be converted into the life prediction results under the actual working environment. Through an accurate failure probability model, the true life of the power module in the actual use environment can be effectively evaluated. Through the integration of the life prediction under the actual use conditions, the life prediction can be adapted to different temperatures, loads, and use scenarios. Through the dynamic calculation of the acceleration factor and the failure life, this method can adapt to different working environment conditions. For different working temperatures and load conditions, the model can automatically adjust the relevant parameters and can predict the failure life at any time to ensure real-time and effective life estimation under various use scenarios. Traditional life tests usually require long-term actual tests and a large number of samples, while the present invention can significantly reduce the test cycle and the test sample size by simulating the accelerated life calculation under different environments. At the same time, since more accurate life predictions can be obtained in a shorter time, it is possible to avoid failures caused by insufficient testing during actual use, thereby saving testing and maintenance costs.

[0011] Preferably, the obtaining of the accelerated test data of the power module includes:

[0012] Obtain the test condition data, perform an accelerated simulation test operation under the actual working conditions according to the test condition data, and record the parameters during the accelerated simulation test operation under the actual working conditions to obtain the accelerated test data of the power module.

[0013] In the present invention, by performing an accelerated simulation test under the actual working conditions, it can be more in line with the actual working state of the power module in the real application environment. By recording the parameters in detail during the accelerated simulation test, the performance of the power module under different working conditions can be recorded in detail. By simulating the actual working conditions for the accelerated test, the life data of the power module can be obtained in a shorter time, avoiding the problem of time consumption in traditional long-term tests.

[0014] Preferably, the calculation of the temperature acceleration factor according to the accelerated test data of the power module to obtain the temperature acceleration factor data, and the calculation of the normal temperature life according to the temperature acceleration factor data to obtain the normal temperature life data includes:

[0015] Extract the power module life according to the accelerated test data of the power module to obtain the power module life data;

[0016] Select the temperature points according to the accelerated test data of the power module to obtain the temperature point data;

[0017] Calculate the temperature acceleration factor according to the temperature point data to obtain the temperature acceleration factor data;

[0018] Perform normal temperature life calculation based on temperature acceleration factor data and power module life data to obtain normal temperature life data.

[0019] In the present invention, various test data recorded in experiments (such as life test data under high temperature, overload, etc.) are used. Through data analysis, the effective life of the power module under these specific test conditions is extracted. Based on the acceleration test data of the power module, a temperature range is selected, which covers the actual working environment of the module. According to the distribution of data points and the temperature change law, several temperature points are selected for subsequent analysis. Through the Arrhenius model, the acceleration effect on the life of the power module at different temperatures is calculated. There is a certain exponential relationship between temperature and life, and through fitting with actual test data, the temperature acceleration factor is obtained. Using the temperature acceleration factor, the life data obtained under high temperature tests is converted into the expected life at normal temperature. By applying the reverse acceleration factor, the life under acceleration conditions is corrected to the life at normal temperature, realizing the function of acceleration test.

[0020] Preferably, extracting failure time data according to the power module acceleration test data to obtain failure time data; constructing a failure probability model according to the failure time data to obtain a failure probability model; predicting the failure life according to the failure probability model to obtain failure life data, including:

[0021] Extracting failure time data according to the power module acceleration test data to obtain failure time data;

[0022] Constructing a Weibull distribution model according to the failure time data to obtain a failure probability model;

[0023] Predicting the failure life according to the failure probability model to obtain preliminary failure life data;

[0024] Correcting the failure life data to normal temperature life according to the temperature acceleration factor data to obtain failure life data.

[0025] In the present invention, the Weibull model can accurately describe the failure law of the power module and predict future failure events based on actual failure time data. Predicting the failure life according to the failure probability model can provide preliminary prediction data for the service life of the power module. Through the correction of the temperature acceleration factor, a life assessment closer to the actual working conditions can be provided, ensuring the accuracy of the life prediction result.

[0026] Preferably, calculating the acceleration factor data according to the normal temperature life data and the failure life data, including:

[0027] Performing division processing on the normal temperature life data and the failure life data to obtain acceleration factor data.

[0028] In the present invention, the Arrhenius model is used to calculate the acceleration factor and apply it to the normal-temperature life data and failure life data, which can provide a reliable life prediction under different environmental conditions. Based on the calculation of the acceleration factor using the Arrhenius model, the accelerated life data obtained by the power module in a high-temperature environment can be converted into reliability data in a normal-temperature environment.

[0029] Preferably, the life prediction under actual use conditions according to the acceleration factor data and the failure life data to obtain the accelerated life data of the power module includes:

[0030] Obtain the actual use condition data;

[0031] Perform actual life prediction according to the acceleration factor data and the failure life data to obtain the actual life data;

[0032] Perform actual use condition life calculation according to the actual use condition data and the actual life data to obtain the accelerated life data of the power module.

[0033] In the present invention, by combining the acceleration factor data and the failure life data, the life of the power module in the actual use environment can be more accurately simulated. Converting the laboratory test results into reliability data in actual applications avoids the high cost and time consumption brought by long-term actual use tests, and at the same time provides relatively accurate prediction results. By predicting the life according to the actual use conditions, it can ensure that the reliability and quality of the product are fully evaluated before leaving the factory.

[0034] Preferably, the selection of temperature points according to the accelerated test data of the power module to obtain the temperature point data includes:

[0035] Select equally spaced temperature points according to the accelerated test data of the power module to obtain the temperature point data; or,

[0036] Select non-equally spaced temperature points according to the accelerated test data of the power module to obtain the temperature point data.

[0037] The equally spaced temperature points in the present invention can provide uniform temperature change data within a certain range, while the non-equally spaced temperature points can focus on specific temperature segments for key tests and capture more refined temperature change characteristics. It improves the accuracy and pertinence of the selection of temperature points, ensures that all key temperature intervals can be covered, and obtains more representative and accurate test data, thereby optimizing the test results.

[0038] Preferably, the selection of non-equally spaced temperature points according to the accelerated test data of the power module to obtain the temperature point data includes:

[0039] Perform exponential regression calculation based on the accelerated test data of the power module to obtain exponential regression data;

[0040] Select temperature points in the high-temperature range according to the exponential regression data to obtain the first temperature point data;

[0041] Select temperature points in the normal-temperature range according to the exponential regression data to obtain the second temperature point data;

[0042] Select temperature points in the temperature change range according to the exponential regression data to obtain the third temperature point data;

[0043] Calculate the temperature point density for the third temperature point data to obtain the temperature point density data;

[0044] Screen the first temperature point data and the second temperature point data according to the temperature point density data to obtain the temperature point data.

[0045] In the present invention, through exponential regression calculation, the relationship between temperature and the life of the power module can be effectively extracted, so as to accurately select the key temperature ranges (such as high-temperature range, normal-temperature range and temperature change range). Through the selection of non-equidistant temperature points, within the high-temperature range, normal-temperature range and temperature change range, the temperature point density will be dynamically adjusted according to the actual test requirements and the working characteristics of the power module, avoiding the overly uniform or overly concentrated selection of temperature points in a certain specific range. The pertinence of the test is improved, so that the test data can be more focused on the key working temperature ranges faced by the power module, and thus a more accurate accelerated life analysis result can be obtained.

[0046] Preferably, the step of selecting temperature points in the high-temperature range according to the exponential regression data to obtain the first temperature point data includes:

[0047] Divide the high-temperature range according to the temperature test condition data corresponding to the accelerated test data of the power module through the preset high-temperature division interval parameter data to obtain the high-temperature range condition data;

[0048] Extract the high-temperature range data from the exponential regression data according to the high-temperature range condition data;

[0049] Calculate the life temperature sensitivity and the life change rate between temperatures based on the high-temperature range data, and obtain the life temperature sensitivity data and the life change rate between temperatures data respectively;

[0050] Divide the high-temperature range data according to the life temperature sensitivity data and select equidistant points to obtain the first high-temperature temperature point data;

[0051] Perform clustering processing on the life change rate between temperatures data to obtain the life change rate clustering data;

[0052] Regarding the high-temperature interval data corresponding to the number of life change rate clusters with the largest cluster center value in the life change rate clustering data as the second high-temperature temperature point data;

[0053] Removing duplicate points from the first high-temperature temperature point data and the second high-temperature temperature point data and determining them as the first temperature point data.

[0054] In the present invention, by analyzing the temperature test conditions of the power module acceleration test data and combining with the preset high-temperature division interval parameter data for high-temperature interval division, it is ensured that the selected temperature points can fully cover the working performance of the module in a high-temperature environment. Extracting the exponential regression data according to the high-temperature interval condition data can accurately associate the life data within the high-temperature interval with the temperature effect, ensuring that the extracted data can better represent the performance of the power module in the actual use environment. The present invention improves the understanding of the relationship between temperature and life, enabling the selected temperature points to more accurately reflect the specific impact of temperature changes on the life of the power module, thereby improving the scientificity and accuracy of temperature point selection. Cluster analysis makes the temperature point selection more targeted and precise, capable of identifying and focusing on the temperature intervals that have a greater impact on the life of the power module. After removing duplicate points, the distribution of temperature points is more reasonable and uniform, avoiding temperature points that are overly concentrated in a certain specific temperature range.

[0055] Preferably, the screening of the first temperature point data and the second temperature point data according to the temperature point density data to obtain the temperature point data includes:

[0056] Calculating the temperature density at different scales for the first temperature point data and the second temperature point data to obtain the first temperature density data set and the second temperature density data set;

[0057] Judging whether the first temperature density data in the first temperature density data set is greater than or equal to the temperature point density data;

[0058] When it is determined that the first temperature density data in the first temperature density data set is greater than or equal to the temperature point density data, then retaining the first temperature point data corresponding to the first temperature density data to obtain the first temperature point retained data;

[0059] Judging whether the second temperature point data in the second temperature point data set is greater than or equal to the temperature point density data;

[0060] When it is determined that the second temperature density data in the second temperature density data set is greater than or equal to the temperature point density data, then retaining the second temperature point data corresponding to the second temperature density data to obtain the second temperature point retained data;

[0061] Determining the first temperature point retained data and the second temperature point retained data as the temperature point data.

[0062] In the present invention, by performing temperature density calculations on the first temperature point data and the second temperature point data at different scales, it is possible to ensure the characteristic that the selected temperature points cover the temperature ranges at different scales. By setting a threshold for the temperature point density (such as whether the temperature density is greater than or equal to the temperature point density data), it is possible to accurately screen out the temperature points with a higher density. By screening out the temperature points with a higher density, it can be ensured that the selected temperature points represent to a certain extent the true performance of the power module under different temperature conditions. By screening and retaining the temperature points that meet the conditions, the analysis results are prevented from being affected by the temperature points that do not meet the conditions.

[0063] The beneficial effects of the present invention are as follows: Through this temperature acceleration factor-corrected life estimation, the life prediction error caused by differences in the working environment temperature is reduced. The failure probability model can accurately reflect the failure law of the power module in the actual working environment. When dealing with different loads and usage environments, different types of failure modes can be simulated through the model to provide an accurate life assessment. By calculating the acceleration factor for the normal temperature life data and the failure life data, the present invention not only makes up for the deficiency of life prediction under normal temperature conditions, but also makes the life prediction closer to the actual usage environment through the correction of the acceleration factor. Through detailed life assessment, the stability and reliability of the power module under actual usage conditions are enhanced, which can help manufacturers and engineers identify failure modes in advance, thereby improving the operation ability of the product in extreme environments such as high voltage and high temperature. BRIEF DESCRIPTION OF THE DRAWINGS

[0064] Other features, objects, and advantages of the present application will become more apparent by reading the detailed description of the non-limiting embodiments with reference to the following drawings:

[0065] Figure 1 The flowchart showing the steps of a method for accelerated life analysis and assessment of a power module according to an embodiment;

[0066] Figure 2 The flowchart showing the steps of a method for calculating the temperature acceleration factor and calculating the normal temperature life according to an embodiment;

[0067] Figure 3 The flowchart showing the steps of a method for predicting the failure life according to an embodiment;

[0068] Figure 4 The flowchart showing the steps of a method for predicting the life under actual usage conditions according to an embodiment. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0069] The technical method of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are part of the embodiments of the present invention, rather than all embodiments. All other embodiments obtained by those skilled in the art based on the embodiments of the present invention without creative efforts belong to the scope of protection of the present invention.

[0070] In addition, the accompanying drawings are only schematic diagrams of the present invention and are not necessarily drawn to scale. The functional entities can be implemented in software form, or in one or more hardware modules or integrated circuits, or in different networks and / or processor methods and / or microcontroller methods.

[0071] It should be understood that although terms such as "first", "second", etc. may be used here to describe various units, these units should not be limited by these terms. These terms are only used to distinguish one unit from another. For example, without departing from the scope of the exemplary embodiments, the first unit can be called the second unit, and similarly the second unit can be called the first unit. The term "and / or" used here includes any and all combinations of one or more of the listed related items.

[0072] Please refer to Figures 1 to 4 , this application provides a method for power module accelerated life analysis and evaluation, including the following steps:

[0073] S1: Obtain power module accelerated test data;

[0074] S2: Calculate the temperature acceleration factor based on the power module accelerated test data to obtain temperature acceleration factor data, and calculate the normal temperature life based on the temperature acceleration factor data to obtain normal temperature life data;

[0075] S3: Extract the failure time from the power module accelerated test data to obtain failure time data; construct a failure probability model based on the failure time data to obtain a failure probability model; predict the failure life based on the failure probability model to obtain failure life data;

[0076] S4: Calculate the acceleration factor based on the normal temperature life data and the failure life data to obtain acceleration factor data;

[0077] S5: Predict the life under actual use conditions based on the acceleration factor data and the failure life data to obtain power module accelerated life data.

[0078] In one embodiment, the accelerated test data of the power module usually consists of experimental data obtained under high temperature, high current or high voltage conditions, including the operating temperature of the power module during the test, the failure time of the module under accelerated test conditions, the operating current and voltage. It is obtained through high-temperature aging experiments, where different environmental temperatures (such as 125 °C, 150 °C, 175 °C) are set. Rated or excess voltage and current are applied to make the module operate under overload conditions. The operating time is recorded and the failed samples are counted.

[0079] Calculation of temperature acceleration factor (AF):

[0080] ;

[0081] Where, is the temperature acceleration factor, is the exponential function, is the activation energy of the power module (0.7 - 1.2 eV), is the Boltzmann constant (8.617× eV / K), is the actual operating temperature (K), is the accelerated test temperature (K).

[0082] The failure time data is extracted from the test samples, and the failure times of multiple samples at the test temperature are recorded. The failure time is fitted using the Weibull distribution, and the specific calculation is as follows:

[0083] ;

[0084] is the probability density function of the failure time, that is, the probability of sample failure within a specific time , is the shape parameter. If represents the wear-out failure stage, is the characteristic life, is the failure time, which refers to the time from the start of the experiment to the failure of the sample, is the natural exponent. Based on the failure time data, the Weibull distribution is fitted using the maximum likelihood estimation (MLE) to obtain , .

[0085] Through the constructed failure probability model, the life at different reliability levels is calculated. Set the target reliability , and calculate the life . , where is the reliability life, is the life reliability.

[0086] Calculate the total acceleration factor based on the normal temperature life and the failure life: , where is the total acceleration factor, is the normal temperature life data, is the life obtained under the accelerated test conditions. In the traditional calculation of the acceleration factor, experimental data (such as the life at high temperature) is relied on for acceleration calculation, and then the normal temperature life is corrected according to the Arrhenius model or other empirical formulas. However, considering the influence of the failure probability model, such calculation can correct the acceleration factor more scientifically, making it not just a simple multiple based on temperature change, but more in line with the working load, vibration and other factors in the actual working condition environment.

[0087] Combine the acceleration factor and the failure life to deduce the actual service life: , is the accelerated life data of the power module, is the total acceleration factor, is the reliability life (i.e., the failure life data).

[0088] Preferably, the obtaining of the accelerated test data of the power module includes:

[0089] S11: Obtain the test condition data, perform the actual working condition acceleration simulation test operation according to the test condition data, and record the parameters during the actual working condition acceleration simulation test operation to obtain the accelerated test data of the power module.

[0090] In one embodiment, the test condition data are the operating parameters of the power module in the accelerated aging test. Determine the main factors affecting the life of the power module, such as temperature, current, voltage, environmental humidity, etc. Start the power module aging test according to the set accelerated stress conditions. The test equipment includes a high-temperature oven (providing a constant temperature environment, such as 150°C, 175°C), a DC regulated power supply (providing a constant current and voltage), a power load device (applying the actual working load, such as a constant current load), and a data acquisition system (recording the current, voltage, temperature and failure information in real time).

[0091] Place the power module in the constant temperature oven and maintain the temperature stable for 30 minutes. Apply the set high temperature (such as 150°C). Apply the set high current (such as 1.5 times the rated current). Apply the set high voltage (such as 1.2 times the rated voltage). Continuously operate the module and monitor the temperature, current and voltage. Record a data point every 10 minutes. The failure determination criterion is that the current deviation exceeds 10%, or the voltage drop exceeds 5%, or there is an open circuit / short circuit in the internal circuit. Once a failure is detected, stop the test and record the failure time.

[0092] Preferably, calculating a temperature acceleration factor based on the power module accelerated test data to obtain temperature acceleration factor data, and calculating a normal temperature life based on the temperature acceleration factor data, including:

[0093] S21: Extracting the power module life from the power module accelerated test data to obtain power module life data;

[0094] S22: Selecting temperature points from the power module accelerated test data to obtain temperature point data;

[0095] S23: Calculating a temperature acceleration factor based on the temperature point data to obtain temperature acceleration factor data;

[0096] S24: Calculating a normal temperature life based on the temperature acceleration factor data and the power module life data to obtain normal temperature life data.

[0097] In one embodiment, record the failure times of each power module at different temperatures. Statistically analyze the average failure times of multiple modules at the same temperature.

[0098] Set a specific time interval to select temperature points.

[0099] Calculation of temperature acceleration factor (AF):

[0100] ;

[0101] Wherein, is the temperature acceleration factor, is the exponential function, is the activation energy of the power module (0.7 - 1.2 eV), is the Boltzmann constant (8.617× eV / K), is the actual use temperature (K), is the accelerated test temperature (K).

[0102] Calculation at normal temperature: , wherein, is the normal temperature life data, is the life obtained under the accelerated test conditions.

[0103] Preferably, extracting failure time data based on the power module accelerated test data; constructing a failure probability model based on the failure time data; predicting a failure life based on the failure probability model, including:

[0104] S31: Extracting failure time data based on the power module accelerated test data to obtain failure time data;

[0105] S32: Construct a Weibull distribution model based on the failure time data to obtain a failure probability model;

[0106] S33: Predict the failure life according to the failure probability model to obtain preliminary failure life data;

[0107] S34: Correct the failure life data at room temperature according to the temperature acceleration factor data to obtain the failure life data.

[0108] In one embodiment, set the failure criteria for the power module, such as the output current drops by more than 10%. The output voltage drops by more than 5%. Internal short circuit or open circuit. Conduct accelerated life tests at different temperatures (such as 125 °C, 150 °C, 175 °C), and record the failure time of each sample. Count the failed samples at each temperature point to obtain the failure time data.

[0109] The failure time data is extracted from the test samples, and the failure times of multiple samples at the test temperature are recorded. The failure time is fitted using the Weibull distribution, and the specific calculation is as follows:

[0110] ;

[0111] is the probability density function of the failure time, that is, at a specific time within, the probability of the sample failing, is the shape parameter. If represents the wear failure stage, is the characteristic life, is the failure time, which refers to the time from the start of the experiment to the failure of the sample, is the natural exponent. Based on the failure time data, the Weibull distribution is fitted using the maximum likelihood estimation (MLE) to obtain , . is the shape parameter, which determines the failure mode: : Early failure (such as manufacturing defects). : Random failure (exponential distribution). : Wear failure (aging loss). is the characteristic life (i.e., the life when 63.2% of the samples fail). is the failure time.

[0112] Solve and using the maximum likelihood estimation (MLE): , where is the failure time, is the number of samples, is the shape parameter, is the characteristic life, representing the life when 63.2% of the samples fail, characterizing the scale of the distribution and directly affecting the time range of failure. is the failure time of the

[0113] th sample, that is, the time from the start of the experiment to the failure of this sample. Usually, it is the time data recorded in the experiment. Calculate the life at different reliability levels through the constructed failure probability model. Set the target reliability and calculate the life . Among them, is the reliability life, is the life reliability. Combine the acceleration factor and the failure life to estimate the actual service life: , is the accelerated life data of the power module, is the total acceleration factor, is the reliability life (i.e., the failure life data).

[0114] Preferably, calculating the acceleration factor according to the normal temperature life data and the failure life data to obtain the acceleration factor data, including:

[0115] S41: Perform a division process on the normal temperature life data and the failure life data to obtain the acceleration factor data.

[0116] In one embodiment, calculate the total acceleration factor based on the normal temperature life and the failure life: , where is the total acceleration factor, is the normal temperature life data, is the life obtained under the accelerated test conditions.

[0117] Preferably, predicting the life under the actual use conditions according to the acceleration factor data and the failure life data to obtain the accelerated life data of the power module, including:

[0118] S51: Obtain the actual use condition data;

[0119] S52: Predict the actual life according to the acceleration factor data and the failure life data to obtain the actual life data;

[0120] S53: Calculate the life under the actual use conditions according to the actual use condition data and the actual life data to obtain the accelerated life data of the power module.

[0121] In one embodiment, the operating temperature and load conditions of the power module are obtained from the actual application environment. The operating cycle of the power module is recorded, including the on / off frequency, operating duration, etc., to obtain the operating cycle and frequency. Using the acceleration factor to correct the failure life , where , and is the actual life data.

[0122] Under high load conditions, the life of the power module will be correspondingly shortened. The life is adjusted by the load factor LF (the load factor is obtained from experimental data or standard literature). The load factor LF is determined through experiments. When the load is 80%, the load factor is 0.8. Divide the actual life data by the load to obtain the accelerated life data of the power module.

[0123] Preferably, the temperature points are selected according to the accelerated test data of the power module to obtain temperature point data, including:

[0124] Selecting equally spaced temperature points according to the accelerated test data of the power module to obtain temperature point data; or,

[0125] Selecting non-equally spaced temperature points according to the accelerated test data of the power module to obtain temperature point data.

[0126] In one embodiment, the selection of equally spaced temperature points means that within the required temperature range (such as 25°C to 175°C), several temperature points are selected at the same interval. Select N temperature points. Set N = 5 as an example. Calculate the interval between each temperature point proportionally. Select the temperature points through the interval.

[0127] The selection of non-equally spaced temperature points is based on the operating characteristics of the module within this temperature range. According to the failure characteristics of the power module, the module fails more significantly in some temperature intervals (such as the high-temperature region). Therefore, the test frequency can be increased near these temperature points (the temperature interval can be obtained by curve fitting and calculating the inflection point to get the demarcation point, and the data interval is divided by the demarcation point. The area with lower temperature is the low-temperature region, and the area with higher temperature is the high-temperature region). For example, according to experimental requirements or module characteristics, more intensive temperature point selection is carried out in the interval of 85°C to 175°C. According to the module performance characteristics and experimental requirements, multiple temperature points are selected. In this case, the following temperature points are selected: low-temperature region (25°C to 85°C): the interval is 20°C, high-temperature region (85°C to 175°C): the interval is 10°C.

[0128] Preferably, the selection of non-equally spaced temperature points according to the accelerated test data of the power module to obtain temperature point data includes:

[0129] Performing exponential regression calculation according to the accelerated test data of the power module to obtain exponential regression data;

[0130] Select the temperature points in the high-temperature range according to the exponential regression data to obtain the first temperature point data;

[0131] Select the temperature points in the normal-temperature range according to the exponential regression data to obtain the second temperature point data;

[0132] In one embodiment, more importantly, calculate the second derivative according to the exponential regression data to obtain the curvature data; calculate the curvature change according to the curvature data to obtain the curvature change data; perform threshold screening according to the curvature change data to obtain the inflection point data; select the temperature points in the normal-temperature range according to the inflection point data to obtain the temperature point data in the normal-temperature range; construct the temperature point device parameter association according to the temperature point data in the normal-temperature range to obtain the primary second temperature point data; perform time equivalence calculation according to the primary second temperature point data to obtain the secondary second temperature point data; perform information entropy calculation according to the secondary second temperature point data to obtain the temperature point entropy value data; perform iterative pruning on the temperature point data in the normal-temperature range according to the temperature point entropy value data to obtain the second temperature point data;

[0133] The exponential regression model is expressed as: , is the exponential regression data, generally the accelerated test life data, is the regression coefficient, is the natural exponential term, is another regression coefficient, is the temperature test data in the power module accelerated test data; Second derivative calculation: , and the parameter meanings are the same as above. , is the curvature. Curvature change calculation (calculate the change rate of adjacent curvature values): , is the curvature change amount, is the +1 curvature value of the temperature point, is the curvature value of the temperature point, calculate the curvature change rate : , where is the time parameter data corresponding to the curvature change amount. Set the curvature change rate threshold , select the average value 3 times the standard deviation : , where is the curvature change rate threshold, is the mean of the curvature change, is the standard deviation of the curvature change, select the points that meet the conditions as inflection points: Select temperature points in the normal temperature range based on the inflection point data: Set the physical meaning of the normal temperature range (e.g., 25°C - 85°C). Determine the boundary points of the normal temperature range according to the inflection point data and : , , select the temperature points within this range: , obtain the temperature point data in the normal temperature range for device parameter correlation analysis.

[0134] Temperature point - device parameter correlation construction: Calculate the device parameters corresponding to each temperature point (such as life, stress, deformation, etc.), calculate the sensitivity of the temperature to the device parameters: , where is the sensitivity of the temperature point to the device parameter, is the device parameter (such as life, stress, deformation amount, etc.), is the temperature point. Construct the temperature - device parameter correlation matrix: , where represents the temperature to the parameter sensitivity. According to the calculated sensitivity , construct the temperature - device parameter correlation matrix :

[0135] ;

[0136] is the temperature - device parameter correlation matrix, is the temperature point to the device parameter sensitivity, is the number of selected temperature points, is the number of device parameters studied;

[0137] Select the temperature point with the highest sensitivity in: , where is the sensitivity screening threshold, determined by the global sensitivity mean and standard deviation , is the sensitivity mean of all temperature points, is the standard deviation. Screen out the temperature points that meet this condition to form the primary second temperature point data for time equivalence calculation.

[0138] Time equivalence calculation uses the acceleration factor (AF) model to calculate the time equivalence coefficient of temperature: , is the acceleration factor, is the base of the natural logarithm, is the activation energy, is the Boltzmann constant, is the reference temperature, is the actual test temperature. Calculate the time-weighting coefficient: , is the time-weighting coefficient, is the power adjustment value of the acceleration factor, is the material characteristic index, which is determined according to the material aging characteristics. is used to adjust the weights of high-temperature points and low-temperature points, making the contributions of different temperature points to the reliability analysis more balanced. For example, the acceleration factor at high-temperature points is larger, which will lead to a significant reduction in the experimental time, while the acceleration factor at low-temperature points is smaller and closer to the actual working environment. Through the time-weighting coefficient, the influence weight of high-temperature points is reduced. Let represent the temperature point corresponding device parameters (such as lifetime, stress, deformation amount, etc.). Calculate the corrected parameter value: where, is the adjusted parameter value, is used as the time-equivalent correction factor. Set the time-equivalent influence threshold (preset or calculated based on the previous threshold calculation method), and select the temperature points that meet the conditions (greater than the threshold). After time-equivalent calculation and screening, the system obtains the secondary second temperature point data. These temperature points are adjusted by time-weighting, enabling the test data to more accurately represent the actual usage situation. Compared with the primary second temperature point data, the secondary second temperature point data more evenly considers the influence of different temperature points on device aging, improving the reliability and engineering applicability of the experimental data.

[0139] Calculate the probability distribution of each temperature point: , is the conditional probability distribution of the characteristic variable at the temperature point , is the temperature point where the characteristic variable is observed frequency, is the characteristic variable, representing a certain characteristic of the device at temperature , such as current change, etc., is the temperature point, referring to different temperature conditions measured during the experiment. Calculate the information entropy: , is the information entropy of the temperature point .

[0140] Calculate the global entropy mean and standard deviation: , is the global entropy mean, is the total number of temperature points, is the temperature point The corresponding information entropy. Calculate the standard deviation: , is the standard deviation of the global entropy. , is the pruning threshold, used to remove redundant temperature points with low information entropy. , is the pruning threshold. If the information entropy of a certain temperature point is lower than the pruning threshold , then this temperature point will be deleted. Calculate the information entropy of all temperature points . Calculate the global entropy mean and the standard deviation . Set the pruning threshold . Delete all temperature points that satisfy . Recalculate the information entropy of the remaining temperature points. If there are still low-entropy points, repeat the above steps until the information entropy of all temperature points is higher than the pruning threshold.

[0141] Select temperature points in the temperature change interval according to the exponential regression data to obtain the third temperature point data;

[0142] Calculate the temperature point density for the third temperature point data to obtain the temperature point density data;

[0143] Screen the first temperature point data and the second temperature point data according to the temperature point density data to obtain the temperature point data.

[0144] In one embodiment, an exponential model is used for regression fitting: , is the exponential regression data, generally the accelerated test life data, is the regression coefficient, is the natural exponential term, is another regression coefficient, is the temperature test data in the power module accelerated test data.

[0145] Divide the exponential regression data by a threshold (the threshold can be obtained by the bisection method) to obtain the exponential regression normal temperature range data and the exponential regression high temperature range data. Select temperature points in equal proportion according to the exponential regression normal temperature range data and the exponential regression high temperature range data to obtain the first temperature point data and the second temperature point data respectively. Divide the exponential regression normal temperature range data and the exponential regression high temperature range data into smaller scales (such as further trisecting), to obtain the refined normal temperature range data and the refined high temperature range data; calculate the slopes according to the refined normal temperature range data and the refined high temperature range data to obtain the normal temperature range slope data and the high temperature range slope data; calculate the ratio of adjacent intervals according to the normal temperature range slope data and the high temperature range slope data to obtain the adjacent interval ratio data; normalize the adjacent interval ratio data to obtain the ratio normalized data; perform weighted calculation and rounding on the preset temperature point quantity data according to the ratio normalized data to obtain the temperature point quantity data corresponding to each interval (i.e., the interval temperature point quantity data); select temperature points (select in equal proportion) according to the interval temperature point quantity data to obtain the third temperature point data;

[0146] Calculate the temperature point density for the third temperature point data to obtain the temperature point density data; divide the first temperature point data and the second temperature point data into smaller scales (such as further trisecting) to obtain the refined first temperature point data and the refined second temperature point data; calculate the temperature point density for the refined first temperature point data and the refined second temperature point data through the scale data corresponding to the scale division to obtain the first temperature point density data and the second temperature point density data respectively; compare the first temperature point density data and the second temperature point density data with the temperature point density data respectively, and only retain the first temperature point density data corresponding to the temperature point density data / the first temperature point data / the second temperature point data corresponding to the second temperature point density data, so as to obtain the temperature point data.

[0147] Screen the first temperature point data and the second temperature point data according to the temperature point density data to obtain the temperature point data.

[0148] Preferably, the step of selecting the high temperature range temperature points according to the exponential regression data to obtain the first temperature point data includes:

[0149] Divide the high temperature range according to the temperature test condition data corresponding to the power module acceleration test data through the preset high temperature division interval parameter data to obtain the high temperature range condition data;

[0150] Extract the high temperature range data from the exponential regression data according to the high temperature range condition data;

[0151] Calculate the life temperature sensitivity and the life change rate between temperatures based on the high-temperature range data, and obtain the life temperature sensitivity data and the life change rate data between temperatures respectively;

[0152] Divide the high-temperature range data according to the life temperature sensitivity data and select equally spaced points to obtain the first high-temperature point data;

[0153] Perform clustering processing on the life change rate data between temperatures to obtain the life change rate clustering data;

[0154] Regard the high-temperature range data corresponding to the life change rate cluster number with the largest cluster center value in the life change rate clustering data as the second high-temperature point data;

[0155] Remove duplicate points from the first high-temperature point data and the second high-temperature point data and determine them as the first temperature point data.

[0156] In one embodiment, according to the condition data of the power module acceleration test, preset the division parameters of the high-temperature range. The definition of the high-temperature range is temperature and is further divided into different small intervals according to different temperature segments (such as the principle of trisecting). The preset high-temperature division parameters are: low high-temperature range: medium high-temperature range: high high-temperature range: Group the data according to the temperature information in the test data according to the above-mentioned divided high-temperature ranges. Extract the high-temperature range data from the exponential regression data according to the high-temperature range condition data;

[0157] Calculation of life temperature sensitivity:

[0158] ;

[0159] is the life temperature sensitivity, and are the life data of adjacent temperature points respectively, and are adjacent temperature points respectively.

[0160] Life change rate between temperatures Calculation:

[0161] ;

[0162] According to the life temperature sensitivity data, divide the high-temperature interval data according to the trend of sensitivity change. For example, if the sensitivity value changes greatly, more temperature points are selected in this interval. Set a threshold. When the change rate of sensitivity is greater than a certain predetermined threshold, it indicates that the temperature change has a greater impact on life. At this time, more dense temperature points should be selected, and this interval is divided into multiple smaller temperature sub-intervals with a smaller temperature interval for each sub-interval (for example: every 5 °C or less) to capture the rapid change of sensitivity. For the area where the change rate of sensitivity is less than the threshold, fewer temperature points can be selected, such as choosing a larger temperature interval (for example, every 20 °C or more), because in these intervals, the temperature change has a smaller impact on life, so too dense test points are not required.

[0163] Use a clustering algorithm to process the data of the life change rate between temperatures and divide the data into different clusters. For example, adopt a distance-based clustering method to cluster temperature points with similar change rates into one cluster. According to the similarity of the change rates, the data is clustered into three categories: Category 1: The change rate is small, representing the low-sensitivity interval; Category 2: The change rate is medium, representing the medium-sensitivity interval; Category 3: The change rate is large, representing the high-sensitivity interval. According to the clustering results, select the cluster with the largest cluster center value. The change rate cluster of Category 3 is the largest cluster, and the data points in the corresponding high-temperature interval data are regarded as the second high-temperature temperature point data. Merge the first high-temperature temperature point data and the second high-temperature temperature point data and remove duplicate temperature points.

[0164] Preferably, the screening of the first temperature point data and the second temperature point data according to the temperature point density data to obtain temperature point data includes:

[0165] Calculate the temperature densities at different scales for the first temperature point data and the second temperature point data to obtain the first temperature density data set and the second temperature density data set;

[0166] Judge whether the first temperature density data in the first temperature density data set is greater than or equal to the temperature point density data;

[0167] When it is determined that the first temperature density data in the first temperature density data set is greater than or equal to the temperature point density data, retain the first temperature point data corresponding to the first temperature density data to obtain the first temperature point retained data;

[0168] Judge whether the second temperature point data in the second temperature point data set is greater than or equal to the temperature point density data;

[0169] When it is determined that the second temperature density data in the second temperature density data set is greater than or equal to the temperature point density data, retain the second temperature point data corresponding to the second temperature density data to obtain the second temperature point retained data;

[0170] Determine the first temperature point retained data and the second temperature point retained data as temperature point data.

[0171] In one embodiment, when calculating the temperature density, a suitable scale is selected according to different temperature ranges. For example, in a region where the temperature changes slowly (such as the low-temperature region), a larger interval is used to calculate the density. In a region where the temperature changes violently (such as the high-temperature region), a smaller interval is used to calculate the density in order to capture the changes more precisely. The temperature density is evaluated by calculating the interval between adjacent temperature points to assess the temperature change in a certain interval. Different calculation scales are required for the selected low-temperature region (25 degrees to 85 degrees) and high-temperature region (85 degrees to 175 degrees) respectively. In the low-temperature region, an interval of 30 degrees or a bisected temperature interval is selected for temperature density calculation. In the high-temperature region, an interval of 15 degrees or a fifth-divided temperature interval is selected for temperature density calculation. Determine whether the temperature density corresponding to each interval in the low-temperature region or the high-temperature region is greater than the threshold. If it is greater, retain it; if not, remove it.

[0172] The above are only specific embodiments of the present invention, enabling those skilled in the art to understand or implement the present invention. Various modifications to these embodiments will be obvious to those skilled in the art. The general principles defined herein can be implemented in other embodiments without departing from the spirit or scope of the present invention. Therefore, the present invention will not be limited to these embodiments shown herein, but rather to the broadest scope consistent with the principles and novel features invented herein.

Claims

1. A method for analyzing and evaluating the accelerated life of a power module, characterized in that: The following steps are involved: Obtain power module accelerated test data; Extract the life of the power module according to the power module accelerated test data to obtain the life data of the power module; select the temperature point according to the power module accelerated test data to obtain the temperature point data; calculate the temperature acceleration factor according to the temperature point data to obtain the temperature acceleration factor data; calculate the normal temperature life according to the temperature acceleration factor data and the power module life data to obtain the normal temperature life data; Failure time extraction is performed based on the power module accelerated test data to obtain failure time data; a failure probability model is constructed based on the failure time data to obtain a failure probability model; failure life prediction is performed based on the failure probability model to obtain failure life data; Calculate the acceleration factor based on the normal temperature life data and the failure life data to obtain the acceleration factor data; According to the acceleration factor data and failure life data, the actual use condition life prediction is carried out to obtain the accelerated life data of the power module; The step of selecting temperature points according to the power module accelerated test data to obtain temperature point data includes: Select temperature points at equal intervals according to the power module accelerated test data to obtain temperature point data; or, Perform exponential regression calculation on the power module accelerated test data to obtain exponential regression data; select temperature points in the high temperature range according to the exponential regression data to obtain first temperature point data; select temperature points in the normal temperature range according to the exponential regression data to obtain second temperature point data; select temperature points in the temperature change range according to the exponential regression data to obtain third temperature point data; perform temperature point density calculation on the third temperature point data to obtain temperature point density data; screen the first temperature point data and the second temperature point data according to the temperature point density data to obtain temperature point data; The step of selecting a temperature point in a high temperature range according to the exponential regression data to obtain first temperature point data includes: According to the temperature test condition data corresponding to the power module accelerated test data, the high temperature interval is divided by the preset high temperature division interval parameter data to obtain the high temperature interval condition data; according to the high temperature interval condition data, the exponential regression data is extracted from the high temperature interval to obtain the high temperature interval data; according to the high temperature interval data, the life temperature sensitivity is calculated and the life change rate between temperatures is calculated to obtain the life temperature sensitivity data and the life change rate data between temperatures respectively; according to the life temperature sensitivity data, the high temperature interval data is divided and equidistant points are selected to obtain the first high temperature temperature point data; the life change rate data between temperatures are clustered to obtain the life change rate clustering data; the high temperature interval data corresponding to the life change rate cluster number with the largest cluster center value in the life change rate clustering data is regarded as the second high temperature temperature point data; the first high temperature temperature point data and the second high temperature temperature point data are subjected to duplicate point removal and determined as the first temperature point data.

2. The method according to claim 1, characterized in that The obtaining of power module accelerated test data includes: Acquire test condition data, and perform accelerated simulation test operations under actual working conditions according to the test condition data, and record parameters during the process of the accelerated simulation test operations under actual working conditions to obtain accelerated test data of the power module.

3. The method according to claim 1, characterized in that The failure time is extracted according to the power module accelerated test data to obtain the failure time data; A failure probability model is constructed according to the failure time data to obtain a failure probability model; Failure life prediction is performed based on the failure probability model to obtain failure life data, including: Extract failure time according to power module accelerated test data to obtain failure time data; The Weibull distribution model is constructed based on the failure time data to obtain the failure probability model; Failure life prediction is performed based on the failure probability model to obtain preliminary failure life data; The failure life data is corrected at normal temperature according to the temperature acceleration factor data to obtain the failure life data.

4. The method according to claim 1, characterized in that: The acceleration factor is calculated based on the normal temperature life data and the failure life data to obtain the acceleration factor data, including: The acceleration factor data is obtained by dividing the normal temperature life data and the failure life data.

5. The method according to claim 1, characterized in that The actual use condition life prediction is performed according to the acceleration factor data and the failure life data to obtain the power module accelerated life data, including: Obtain actual usage condition data; According to the acceleration factor data and failure life data, actual life prediction is performed to obtain actual life data; The actual use condition life calculation is performed based on the actual use condition data and the actual life data to obtain the power module accelerated life data.

6. The method according to claim 1, characterized in that The step of screening the first temperature point data and the second temperature point data according to the temperature point density data to obtain the temperature point data includes: Perform temperature density calculations of different scales on the first temperature point data and the second temperature point data to obtain a first temperature density data set and a second temperature density data set; Determine whether the first temperature density data of the first temperature density data set is greater than or equal to the temperature point density data; When it is determined that the first temperature density data of the first temperature density data set is greater than or equal to the temperature point density data, the first temperature point data corresponding to the first temperature density data is retained to obtain the first temperature point retained data; Determine whether the second temperature point data of the second temperature point data set is greater than or equal to the temperature point density data; When it is determined that the second temperature density data of the second temperature density data set is greater than or equal to the temperature point density data, the second temperature point data corresponding to the second temperature density data is retained to obtain second temperature point retained data; The first temperature point retention data and the second temperature point retention data are determined as the temperature point data.

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