Generator reliability test and life prediction method, device, equipment and medium

Abstract

The present application discloses a method, apparatus, device, and medium for generator reliability testing and life prediction. The method includes: determining a first life model and a second life model for the insulation winding; based on the first life model and the insulation winding temperature and the first operating time in the target data under the temperature dimension, predicting a first equivalent test time at the reliability test temperature, and predicting a second equivalent test time at the reliability test speed / torque combination; based on the first equivalent test time and / or the second equivalent test time, controlling the insulation winding of the generator to operate at the insulation winding voltage, controlling the rotor assembly of the target generator to operate at the reliability test temperature and / or the reliability test speed / torque combination, and obtaining the reliability test results of the target generator. The above scheme can reduce the test sample size and the number of tests, shorten the test time, and better simulate the stress state of the motor after assembly, thereby improving the accuracy of the motor reliability test.

Description

Technical Field

[0001] The present application relates to the field of reliability testing technology, and in particular to methods, devices, equipment, and media for reliability testing and life prediction of generators. Background Art

[0002] The reliability of a motor is crucial to its operational safety. Typically, current reliability tests for motors use accelerated life tests, which obtain life data under stress conditions higher than normal operating conditions, and then extrapolate the life under normal conditions based on an accelerated model.

[0003] Existing reliability accelerated life tests usually conduct individual tests on components and perform multiple tests. Each test sets different temperatures, voltages, etc., requiring a large number of test samples and testing time. It is also unable to simulate the overall stress state of the motor after assembly, resulting in reduced test accuracy.

[0004] Therefore, the large sample size, long test time and low test accuracy required in current reliability accelerated life tests have become problems that need to be solved urgently. Summary of the Invention

[0005] The main technical problem solved by this application is to provide a generator reliability test and life prediction method, device, equipment and medium, which can reduce the test sample volume and test time required in the reliability accelerated life test, simulate the overall stress state of the motor after assembly, and improve the test accuracy.

[0006] In order to solve the above problems, the first aspect of the present application provides a reliability test method for a vehicle generator, comprising: obtaining operating data of vehicle generators of several target users in various dimensions; wherein the various dimensions include temperature dimension, mechanical dimension and voltage dimension; respectively screening operating data covering a target proportion in the various dimensions as target data corresponding to the dimensions, and determining a first life model of the insulating winding based on the first test data of the insulating winding in the temperature dimension, and determining a second life model of the rotor assembly based on the second test data of the rotor assembly in the mechanical dimension; determining a second life model of the rotor assembly based on the first life model and the target data in the temperature dimension; The first equivalent test duration at the reliability test temperature is predicted based on the insulation winding temperature and the first operating time, and the second equivalent test duration at the reliability test speed / torque combination is predicted based on the second life model and the speed / torque combination and the second operating time in the target data under the mechanical dimension; based on the first equivalent test duration and / or the second equivalent test duration, the insulation winding of the target generator is controlled to operate at the insulation winding voltage in the target data under the voltage dimension, and the rotor assembly of the target generator is controlled to operate at the reliability test temperature and / or the reliability test speed / torque combination to obtain the reliability test result of the target generator.

[0007] Among them, based on the first equivalent test duration and / or the second equivalent test duration, the insulating winding of the target generator is controlled to operate at the insulating winding voltage in the target data under the voltage dimension, and the rotor assembly of the target generator is controlled to operate at the reliability test temperature and / or the reliability test speed / torque combination to obtain the reliability test result of the target generator, including: while controlling the insulating winding of the target generator to operate at the insulating winding voltage in the target data under the voltage dimension, and controlling the rotor assembly of the target generator to operate at the reliability test speed / torque combination for the second equivalent test duration, monitoring the real-time temperature of the insulating winding; adjusting at least one of the coolant temperature and the coolant flow rate based on whether the real-time temperature reaches the equivalent damage temperature of the first two equivalent test durations; in response to the target generator having run for the second equivalent test duration, controlling the target generator to stop running, and obtaining the reliability test result of the target generator.

[0008] Among them, adjusting at least one of the coolant temperature and the coolant flow rate based on whether the real-time temperature reaches the equivalent damage temperature of the first two equivalent test durations includes at least one of the following: in response to the real-time temperature not reaching the equivalent damage temperature of the first two equivalent test durations, executing at least one of increasing the coolant temperature and decreasing the coolant flow rate; in response to the real-time temperature exceeding the equivalent damage temperature of the first two equivalent test durations, executing at least one of decreasing the coolant temperature and increasing the coolant flow rate.

[0009] Among them, in the case that the various dimensions include the temperature dimension, the operation data covering the target proportion are respectively screened in the various dimensions as the target data corresponding to the dimension, including: for each operation data under the temperature dimension, based on the preset life model of the insulation winding and the insulation winding temperature and the first operation time in the operation data, predicting the equivalent operation time of the insulation winding at the target temperature; sorting the each operation data under the temperature dimension in order of the equivalent time from small to large; and based on the sorted operation data under the temperature dimension, selecting the operation data covering the target proportion as the target data of the temperature dimension.

[0010] Among them, in the case that the various dimensions include the mechanical dimension, the operating data covering the target proportion are respectively screened in the various dimensions as the target data corresponding to the dimension, including: selecting one as reference data from the operating data of the mechanical dimension; for each operating data under the mechanical dimension, predicting the pseudo-damage value of the operating data relative to the reference data based on the preset life model of the rotor assembly; sorting the various operating data under the mechanical dimension in order of the pseudo-damage values ​​from small to large; and selecting the operating data covering the target proportion as the target data of the mechanical dimension based on the sorted operating data under the mechanical dimension.

[0011] Among them, the first life model of the insulating winding is determined based on the first test data of the insulating winding under the temperature dimension, including: obtaining at least three first test data of the insulating winding under the temperature dimension; wherein, the first test data includes the failure time of the component when operating at the test temperature; based on the at least three first test data, a function is fitted to the preset life model of the insulating winding to obtain the predicted values ​​of the empirical parameters in the preset life model; based on the predicted values ​​of the empirical parameters in the preset life model of the insulating winding, the preset life model is updated to obtain the first life model.

[0012] Wherein, determining the second life model of the rotor assembly based on the second test data of the rotor assembly under the mechanical dimension includes: obtaining at least three second test data of the rotor assembly under the mechanical dimension; wherein, the second test data includes the failure time of the component when operating under the test speed / torque combination; performing function fitting on the preset life model of the rotor assembly based on the at least three second test data to obtain predicted values ​​of the empirical parameters in the preset model; updating the preset life model based on the predicted values ​​of the empirical parameters in the preset life model of the rotor assembly to obtain the second life model.

[0013] In order to solve the above problems, the second aspect of the present application provides a life prediction method for a vehicle generator, including: obtaining the speed / torque combination, insulation winding temperature and operating time of the rotor assembly in the generator to be tested, and obtaining a first life model of the insulation winding and a second life model of the rotor assembly; wherein, the first life model and the second life model are obtained based on the above-mentioned reliability test method of the vehicle generator; based on the first life model and the insulation winding temperature of the generator to be tested, the first life duration is predicted, and based on the second life model and the speed / torque combination of the generator to be tested, the second life duration is predicted; based on the first life duration, the second life duration and the operating time, the remaining life duration of the generator to be tested is obtained.

[0014] The first life model includes a first functional relationship between the confidence and the life span at different insulation winding temperatures, and the second life model includes a second functional relationship between the confidence and the life span at different speed / torque combinations.

[0015] Among them, the predicting of the first life duration based on the first life model and the insulation winding temperature of the generator to be tested includes: selecting a first functional relationship matching the insulation winding temperature of the generator to be tested based on the first life model, and predicting the first life duration at several target confidence levels based on the matched first functional relationship; the predicting of the second life duration based on the second life model and the speed / torque combination of the generator to be tested includes: selecting a second functional relationship matching the speed / torque combination of the generator to be tested based on the second life model, and predicting the second life duration at the several target confidence levels based on the matched second functional relationship; the obtaining of the remaining life duration of the generator to be tested based on the first life duration, the second life duration and the operating time includes: for the several target confidence levels, obtaining the remaining life duration of the generator to be tested at the corresponding target confidence levels based on the first life duration and the second life duration under the same target confidence level, as well as the operating time.

[0016] In order to solve the above problems, the third aspect of the present application provides a reliability test device for a vehicle generator, including: an acquisition module for acquiring operating data of vehicle generators of several target users in various dimensions; wherein the various dimensions include temperature dimension, mechanical dimension and voltage dimension; a screening module for respectively screening operating data covering a target proportion in the various dimensions as target data corresponding to the dimensions; a determination module for determining a first life model of the insulating winding based on the first test data of the insulating winding in the temperature dimension, and determining a second life model of the rotor assembly based on the second test data of the rotor assembly in the mechanical dimension; a prediction module for determining a first life model of the insulating winding based on the first test data of the rotor assembly in the mechanical dimension; and a prediction module for determining a second life model of the rotor assembly based on the first life model. The first equivalent test duration is predicted based on the insulation winding temperature and the first operating time in the target data under the temperature dimension, and the second equivalent test duration is predicted based on the second life model and the speed / torque combination and the second operating time in the target data under the mechanical dimension; a control module is used to control the insulation winding of the target generator to operate at the insulation winding voltage in the target data under the voltage dimension and control the rotor assembly of the target generator to operate at the reliability test speed / torque combination based on the first equivalent test time and the second equivalent test time, so as to obtain the reliability test result of the target generator.

[0017] In order to solve the above problems, the fourth aspect of the present application provides a life prediction device for a vehicle generator, including: an acquisition module for acquiring the speed / torque combination, insulation winding temperature and operating time of the rotor assembly in the generator to be tested, and acquiring a first life model of the insulation winding and a second life model of the rotor assembly; wherein, the first life model and the second life model are obtained based on the above-mentioned reliability test device for the vehicle generator; a prediction module for predicting the first life duration based on the first life model and the insulation winding temperature of the generator to be tested, and predicting the second life duration based on the second life model and the speed / torque combination of the generator to be tested; a determination module for obtaining the remaining life duration of the generator to be tested based on the first life duration, the second life duration and the operating time.

[0018] In order to solve the above problems, the fifth aspect of the present application provides an electronic device, including a memory and a processor coupled to each other, the memory storing program instructions, and the processor being used to execute the program instructions to implement the above-mentioned vehicle generator reliability test method or vehicle generator life prediction method.

[0019] In order to solve the above problems, the sixth aspect of the present application provides a computer-readable storage medium storing program instructions that can be executed by a processor, wherein the program instructions are used to implement the above-mentioned vehicle generator reliability test method or vehicle generator life prediction method.

[0020] The above scheme can reduce the test sample size and the number of tests, thereby shortening the test time, by simultaneously conducting accelerated reliability tests on the insulation winding and rotor of the motor and making the motor assembly operate simultaneously under the temperature, speed / torque, and voltage conditions of the accelerated reliability test. It can also better simulate the stress state of the motor after assembly and improve the accuracy of the motor reliability test. BRIEF DESCRIPTION OF THE DRAWINGS

[0021] Figure 1 This is a flow chart of an embodiment of a reliability test method for a vehicle generator in the present application;

[0022] Figure 2 is a flow chart of another embodiment of a reliability test method for a vehicle generator in the present application;

[0023] Figure 3 This is a flow chart of another embodiment of the reliability test method for a vehicle generator in the present application;

[0024] Figure 4 This is a flow chart of another embodiment of the reliability test method for a vehicle generator in the present application;

[0025] Figure 5 This is a flow chart of another embodiment of the reliability test method for a vehicle generator in the present application;

[0026] Figure 6 This is a flow chart of another embodiment of the reliability test method for a vehicle generator in the present application;

[0027] Figure 7 This is a flow chart of another embodiment of the reliability test method for a vehicle generator in the present application;

[0028] Figure 8 is a schematic diagram of data fitting analysis of the first test data;

[0029] Figure 9 It is a schematic diagram of fitting the Weibull distribution to the three first experimental data;

[0030] Figure 10 It is a schematic diagram of a framework of an embodiment of a reliability test device for a vehicle generator in the present application;

[0031] Figure 11 This is a schematic diagram of a framework of an embodiment of a vehicle generator life prediction device in the present application;

[0032] Figure 12 This is a schematic diagram of the framework of an embodiment of the electronic device of the present application;

[0033] Figure 13 This is a schematic diagram of a framework of an embodiment of a computer-readable storage medium of the present application. DETAILED DESCRIPTION

[0034] The following describes the embodiments of the present application in detail with reference to the accompanying drawings.

[0035] In the following description, for the purpose of explanation rather than limitation, specific details such as specific system structures, interfaces, and technologies are provided to facilitate a thorough understanding of the present application.

[0036] The terms "system" and "network" are often used interchangeably in this document. The term "and / or" is simply a description of an association between related objects, indicating that three possible relationships exist. For example, "A and / or B" can mean: A exists alone, A and B exist simultaneously, or B exists alone. Furthermore, the character " / " generally indicates that the related objects are in an "or" relationship. Furthermore, "multiple" in this document means two or more than two.

[0037] Before further elaborating on this application, it is necessary to first explain the accelerated life test method to facilitate readers to thoroughly understand this application; the accelerated life test uses a stress level higher than that of the component under normal operating conditions, so that the component operates under this stress level, so as to simulate the damage caused by the component under normal operating conditions for a long time in a shorter time, and thus the component assessment results can be obtained in a shorter time.

[0038] It should be noted that, in this article, the reliability test method and life prediction method of vehicle generators are explained by taking the life acceleration test of the extended-range generator as an example.

[0039] Please refer to Figure 1 , Figure 1 1 is a flow chart of an embodiment of a reliability test method for a vehicle generator in the present application; specifically, the reliability test method for a vehicle generator includes:

[0040] Step S11: Acquire operating data of vehicle generators of several target users in various dimensions; wherein the various dimensions include temperature dimension, mechanical dimension, and voltage dimension;

[0041] In one embodiment, the temperature-based operating data may include a first operating duration at at least one insulation winding temperature, the mechanical-based operating data may include a second operating duration at at least one set of speed / torque, and the voltage-based operating data may include a third operating duration at at least one insulation winding voltage. The first operating duration refers to the duration that the generator insulation winding operates at a certain insulation winding temperature; the second operating duration refers to the duration that the generator rotor assembly operates at a certain speed / torque combination; and the third operating duration refers to the duration that the generator insulation winding operates at a certain insulation winding voltage.

[0042] In this embodiment, obtaining operational data on the vehicle generators of several target users in various dimensions may include: selecting driving data from users in typical regions of China (e.g., Northeast China, North China, East China, Northwest China, Southwest China, Central China, and South China) based on the seven major geographic regions of China, based on the automotive cloud platform system. To eliminate the influence of seasonal factors, the data collection period may be set to one year. Randomly selecting the same number of user data from each region and performing simple counting to obtain insulation winding temperature-time data, speed / torque-time data, and insulation winding terminal voltage-time data for each user. It should be noted that the data collection period may be set not only to one year but also to two years, ten years, or other periods as needed. User data may also be selected based on user age groups. For example, data may be collected for users aged 18 to 28, 29 to 38, and 39 to 48, respectively, to reduce the impact of age-related driving habits on motor operating conditions. Specifically, in this embodiment, the collected user temperature-dimensional operational data includes multiple first operating hours at insulation winding temperatures.

[0043] In this embodiment, considering that the main failure stress of the insulating winding on the stator is thermal stress, and its secondary factors include the winding terminal voltage, it is chosen to obtain data of the temperature dimension and voltage dimension in the user data; for the generator rotor, its mechanical fatigue is the main cause of failure, so it is chosen to obtain data of the mechanical dimension in the user data; in this application, the generator assembly is selected for life acceleration test, that is, the rotor + stator form, which can better simulate the assembly quality of the generator and the stress state during use.

[0044] Step S12: Filtering operating data covering a target ratio in each dimension as target data for the corresponding dimension, and determining a first life model for the insulation winding based on first test data of the insulation winding in the temperature dimension, and determining a second life model for the rotor assembly based on second test data of the rotor assembly in the mechanical dimension;

[0045] In this embodiment, according to the definition in GB / T20111, the life equation of the insulation winding conforms to the Arrhenius model. Therefore, the first life model of the insulation winding is the Arrhenius model, and the Arrhenius model can be expressed as:

[0046]

[0047] The acceleration factor can be defined as: the time of normal use conditions of the product divided by the time of accelerated life test. The acceleration factor can be calculated as:

[0048]

[0049] Among them, TF is life, A is a constant, T is temperature, T0 represents the temperature under normal use conditions, T s represents the temperature under accelerated conditions, K represents the Boltzmann constant, which is 8.617*10^-5ev / k, and E a is the activation energy. In this field, E a Often takes empirical values ​​based on failure modes;

[0050] The influence of mechanical fatigue of rotor assembly on product life is usually described by InversePower model. Therefore, the second life model of rotor assembly can be InversePower model, which can be expressed as: C=S σ N;

[0051] Similarly, the acceleration coefficient can be calculated:

[0052]

[0053] Wherein, C is a constant, S is the loading stress, Sp1 is the normal stress, Sp2 is the accelerated stress, N is the life, and σ is the exponential coefficient. In this field, σ is often taken as an empirical value based on the failure mode.

[0054] Please refer to Figure 2 , Figure 2 1 is a flow chart of another embodiment of a reliability test method for a vehicle generator in the present application. In one embodiment, when various dimensions include a temperature dimension, operating data covering a target ratio is screened in each dimension as target data for the corresponding dimension, including:

[0055] S21: For each operating data under the temperature dimension, based on the preset life model of the insulating winding and the insulating winding temperature and the first operating time in the operating data, predict the equivalent time of the insulating winding at the target temperature; wherein, the above-mentioned preset life model of the insulating winding may include an Arrhenius model; the insulating winding temperature in the operating data represents the temperature reached by the insulating winding under normal operating conditions; the target temperature is a manually set temperature value, which represents the temperature acceleration condition to be achieved during the reliability test, so as to run the corresponding equivalent time at the target temperature and thus achieve the damage equivalent of running for the first operating time at the above-mentioned insulating winding temperature.

[0056] S22: Sort the operation data under the temperature dimension in ascending order of equivalent time length;

[0057] S23: Based on the sorted operating data in the temperature dimension, select operating data covering a target ratio as target data in the temperature dimension.

[0058] In this embodiment, the automobile generator is used as the test object. In order to meet the requirements of high reliability and high coverage in the automobile industry, user data covering 95% of users are screened in various dimensions as target data for the corresponding dimensions. It should be noted that the user data covering 95% of users is taken as an example of the insulation winding temperature-time data of the generator insulation system, as shown in the following table. The following table is an example of the insulation winding temperature-time collected from a user in one year. It can be obtained that the user used the car for 800 hours and traveled 20,000 kilometers in one year. According to the empirical value of activation energy E aTake 0.7, the insulation winding temperature is the temperature under normal use conditions, then according to the calculation formula of the acceleration coefficient, the acceleration coefficient converted to the target temperature at each insulation winding temperature can be obtained respectively. After the acceleration coefficient is calculated, the equivalent time at the target temperature value can be calculated from the first operating time at each insulation winding temperature in the table below according to the relationship between the life of the product under normal use conditions and the life of the accelerated life test. For example: to calculate the equivalent time from the winding temperature of 35°C under normal use conditions to the accelerated test conditions, first calculate the corresponding acceleration coefficient at this time, substitute the empirical values ​​of the insulation winding temperature, target temperature and activation energy into the acceleration coefficient calculation formula, and you can get an acceleration coefficient of 950 424; Based on the definition of the acceleration factor: the time under normal product use conditions divided by the time of the accelerated life test, then given the time under normal use conditions and the acceleration factor, the equivalent time under the corresponding conditions can be calculated, which is approximately 0.0001 hours here. Similarly, the equivalent time converted to the target temperature at each winding temperature is calculated separately, and the cumulative equivalent time for the user is 22 hours. It is worth noting that the calculated equivalent time of 22 hours for the user indicates that if the life acceleration test is conducted under the predetermined temperature dimension and the reliability test temperature is 80°C, only 22 hours of testing is required to simulate the wear and tear on the generator caused by the user's 800 hours of driving in a year. After obtaining the equivalent time for each user, the equivalent time of each user is arranged from smallest to largest, and the equivalent time value at 95% user coverage and the temperature-time data of the user corresponding to this equivalent time value are selected as the target data under the temperature dimension.

[0059]

[0060] It should be noted that, in this article, the operating data of the coverage target ratio is based on the high coverage and high reliability required by the automotive industry, so a higher coverage target ratio is selected, and its specific value can be 95%, or other values, such as 90%, 85%, 98%, etc.; in other embodiments, the operating data of the coverage target ratio can also select a lower ratio based on factors such as cost, such as 70%.

[0061] Please refer to Figure 3 , Figure 3 This is a flow chart of another embodiment of the vehicle generator reliability test method in the present application. In one implementation scenario, when various dimensions include a mechanical dimension, operating data covering a target ratio is screened in each dimension as target data for the corresponding dimension, including:

[0062] S31: Select one of the operating data in the mechanical dimension as reference data; for each operating data in the mechanical dimension, predict a pseudo damage value of the operating data relative to the reference data based on a preset life model of the rotor assembly;

[0063] S32: sorting the various operation data under the mechanical dimension in ascending order of pseudo damage values;

[0064] S33: Based on the sorted operating data under the mechanical dimension, select the operating data that covers the target ratio as the target data of the mechanical dimension. To obtain the speed / torque-time under 95% user coverage, it is necessary to first randomly select the speed / torque-time data of a user, calculate the damage ratio of the remaining users relative to this user based on the InversePower model, that is, obtain the pseudo-damage value of the remaining users, and record the pseudo-damage value of the selected user as 1. Arrange all the pseudo-damage values ​​from small to large, select the pseudo-damage value under 95% user coverage and the speed / torque-time data of the user corresponding to the pseudo-damage value as the target data of the mechanical dimension; it should be noted that the calculation method of the damage ratio is a commonly used method in this field and will not be repeated in this application.

[0065] In this embodiment, in order to obtain the target data of the voltage dimension under 95% user coverage, after collecting the insulation winding end voltage-time data of each user, the voltage value with 95% coverage in the insulation winding end voltage distribution, that is, the insulation winding end voltage value whose voltage is greater than 95% of other voltage values, is directly selected as the target data of the voltage dimension.

[0066] Please refer to Figure 4 , Figure 4 1 is a flow chart of another embodiment of a reliability test method for a vehicle generator in the present application. In one possible implementation, determining a first life model of the insulation winding based on first test data of the insulation winding in the temperature dimension includes:

[0067] S41: Obtain at least three first test data of the insulated winding under the temperature dimension; wherein the first test data includes the failure time of the component when operating at the test temperature; since the Arrhenius model is TF=A0exp(E a / ), taking the natural logarithm of both sides of the above equation, we can get: ln(TF)=ln(A0)+ a / , from the above formula we can see that ln(TF) and 1 / T are linearly related, and its slope is E a / K, so only three sets of stator insulation winding components need to be tested to fit the straight line to obtain the slope and the activation energy E aThe value of is used to determine the first life model of the insulation winding. In this embodiment, at least three stator insulation winding components can be selected and divided into three groups. They are tested under three different temperature conditions until all components fail, and the failure time of each component is recorded. If each group includes multiple stator insulation winding components, then for the failure times of different stator insulation winding components under the same temperature condition, the median value of all failure times can be selected as the life value of the stator insulation winding component under the corresponding temperature condition; in other embodiments, the mean value of all failure times can also be selected as the life value of the stator insulation winding component under the corresponding temperature condition. Other value-taking methods can also be used, which are not limited in this application;

[0068] S42: Perform function fitting on a preset life model of the insulation winding based on at least three first test data to obtain predicted values ​​of empirical parameters in the preset life model; wherein the predicted values ​​of the empirical parameters in the preset life model are the activation energy E in the Arrhenius model. a Based on the failure time of the stator insulation winding component at different temperatures recorded in step S42 as the first test data, a coordinate system is established with 1 / T as the abscissa and ln(TF) as the ordinate. At least three sets of data are required to obtain a straight line in the coordinate system by data fitting, and the slope of the straight line is calculated to obtain the value of the activation energy;

[0069] S43: updating the preset life model based on the predicted values ​​of the empirical parameters in the preset life model of the insulation winding to obtain a first life model.

[0070] The above solution determines the first life model of the insulation winding through experiments, which is more accurate than the activation energy based on empirical values ​​and avoids over-testing or under-testing.

[0071] Please refer to Figure 5 , Figure 5 : is a flow chart of another embodiment of a reliability test method for a vehicle generator in the present application; in one possible implementation, based on second test data of the rotor assembly in a mechanical dimension, determining a second life model of the rotor assembly includes:

[0072] S51: Obtain at least three second test data of the rotor assembly in the mechanical dimension; wherein the second test data includes the failure time of the component when operating at the test speed / torque combination; the InversePower model is C=S σ N, taking the natural logarithm of both sides of the above formula can be obtained: From the above formula, we can see that lnN and lnS are linearly related, and the slope is Therefore, only three groups of rotor components need to be tested to obtain a straight line with a slope, and the value of the exponential coefficient σ can be obtained, thereby determining the second life model of the rotor assembly. In this embodiment, similar to the determination of the first life model of the insulating winding in the above embodiment, at least three rotor components can be selected and divided into three groups. They are tested under three different speed / torque conditions until all components fail, and the failure time of each component is recorded. If each group includes multiple rotor components, then for the failure times of different rotor components under the same speed / torque condition, the median value of all failure times can be selected as the life value of the rotor component under the corresponding speed / torque condition; in other embodiments, the mean value of all failure times can be selected as the life value of the rotor component under the corresponding speed / torque condition. Other value-determination methods are also possible, and this application does not limit them here.

[0073] S52: Performing function fitting on a preset life model of the rotor assembly based on at least three second test data to obtain predicted values ​​of empirical parameters in the preset model. The specific fitting method is similar to that used in the aforementioned embodiment to determine the first life model of the insulation winding, and will not be further described here.

[0074] S53: updating the preset life model based on the predicted values ​​of the empirical parameters in the preset life model of the rotor assembly to obtain a second life model.

[0075] Step S13: Based on the first life model and the insulating winding temperature and the first operating time in the target data under the temperature dimension, predict the first equivalent test time at the reliability test temperature, and based on the second life model and the speed / torque combination and the second operating time in the target data under the mechanical dimension, predict the second equivalent test time under the reliability test speed / torque combination; wherein, the reliability test temperature is the insulating winding temperature used in the reliability test, that is, during the reliability test, the insulating winding needs to operate at the reliability test temperature for the first equivalent test time, so that the damage caused to the insulating winding in the reliability test can be equivalent to the damage caused by the insulating winding running at the insulating winding temperature in the target data for the first operating time.

[0076] In step S13, since the predicted values ​​of the empirical parameters in the first life model and the second life model have been obtained in step S12, that is, the activation energy E in the Arrhenius model aand the value of the exponential coefficient σ in the InversePower model. Therefore, both models are determined, and the equivalent test duration can be calculated based on the determined model; according to the target data under each dimension obtained in step S12, the equivalent test duration under the corresponding dimension is predicted, including: based on the insulation winding temperature-time data under 95% user coverage, the first equivalent test duration equivalent to the reliability test temperature is calculated using the determined Arrhenius model. The calculation method has been introduced in the implementation method of the aforementioned step S23 and will not be repeated here; based on the speed / torque-time data under 95% user coverage, the second equivalent test duration equivalent to the reliability test speed / torque combination is calculated using the determined InversePower model. The specific calculation method is similar to the calculation method for calculating the first equivalent test duration in the aforementioned step S23. It is only necessary to change the Arrhenius model used therein to the InversePower model, which will not be repeated here.

[0077] It should be noted that in step S13 , the reliability test temperature and the reliability test speed / torque combination are manually selected and can be any values ​​that meet the accelerated test conditions as long as they do not exceed the predetermined maximum load of the component.

[0078] Step S14: Based on the first equivalent test duration and / or the second equivalent test duration, the insulation winding of the target generator is controlled to operate at the insulation winding voltage in the target data under the voltage dimension, and the rotor assembly of the target generator is controlled to operate at the reliability test temperature and / or the reliability test speed / torque combination to obtain the reliability test result of the target generator.

[0079] It should be noted that, since the reliability test temperature and the reliability test speed / torque combination in step S13 are artificially selected, although the calculated first equivalent test duration and second equivalent test duration can obtain temperature acceleration reliability test results or mechanical acceleration reliability test results in independent temperature or mechanical acceleration tests, in order to comprehensively consider various factors of generator failure and perform acceleration tests on the generator in various dimensions at the same time, it is necessary to meet the temperature acceleration equivalence condition and the mechanical acceleration equivalence condition at the same time under a certain equivalent test duration; and different reliability test temperatures or reliability test speed / torque combinations can respectively obtain different first equivalent test durations and second equivalent test durations. Therefore, in step S13, when the first equivalent test duration and the second equivalent test duration calculated based on the selected reliability test temperature and reliability test speed / torque combination are not equal, the reliability test temperature or reliability test speed / torque combination can be adjusted in the test to make the first equivalent test duration equal to the second equivalent test duration, so that the temperature acceleration equivalence condition and the mechanical acceleration equivalence condition can be met at the same time. In some implementation scenarios, if the first equivalent test duration is equal to the second equivalent test duration, the insulation winding of the target generator can be controlled to operate at the insulation winding voltage in the target data under the voltage dimension based on the first equivalent test duration and the second equivalent test duration, and the rotor assembly of the target generator can be controlled to operate at the reliability test temperature and / or reliability test speed / torque combination to obtain the reliability test results of the target generator.

[0080] Please refer to Figure 6 , Figure 6 This is a flow chart of another embodiment of the reliability test method for a vehicle generator in the present application. In one implementation scenario, based on the first equivalent test duration and / or the second equivalent test duration, the insulation winding of the target generator is controlled to operate at the insulation winding voltage in the target data under the voltage dimension, and the rotor assembly of the target generator is controlled to operate at the reliability test temperature and / or the reliability test speed / torque combination to obtain the reliability test results of the target generator, including:

[0081] S61: while controlling the insulation winding of the target generator to operate at the insulation winding voltage in the target data under the voltage dimension, and controlling the rotor assembly of the target generator to operate at the reliability test speed / torque combination for the second equivalent test time, monitoring the real-time temperature of the insulation winding;

[0082] S62: adjusting at least one of the coolant temperature and the coolant flow rate based on whether the real-time temperature reaches the equivalent damage temperature of the second equivalent test duration;

[0083] S63: In response to the target generator having run for the second equivalent test time, the target generator is controlled to stop running, and a reliability test result of the target generator is obtained. The reliability test result can be obtained by observation by professionals or by testing relevant data with professional instruments.

[0084] When conducting an accelerated life test on a range-extended generator, when it is mechanically accelerated during operation, its rotor will operate at a speed / torque combination that exceeds normal operating conditions, which will not only cause damage to its mechanical structure but also cause the temperature of the stator insulation winding to rise. The range-extended generator uses coolant to cool it down during operation, so it is only necessary to adjust the flow or temperature of the coolant to achieve stator insulation winding temperature control.

[0085] In some embodiments, in order to make the accelerated life test of the range-extended generator simulate its working state during normal operation as much as possible, it is necessary to make it cycle under different speed / torque combinations during the accelerated life test.

[0086] In step S61, during the accelerated life test, in addition to operating the range-extended generator under temperature and mechanical acceleration conditions, a voltage is applied to its stator insulation winding. This voltage is the target voltage data for 95% user coverage obtained in the aforementioned embodiment. Specifically, after collecting the insulation winding terminal voltage-time data for each user, the voltage value with 95% coverage in the insulation winding terminal voltage distribution is directly selected, i.e., the insulation winding terminal voltage value that is greater than 95% of the other voltage values. This voltage is applied to the stator insulation winding throughout the entire test.

[0087] It should be noted that, since it is believed that the first equivalent test duration and the second equivalent test duration corresponding to the selected temperature acceleration condition and mechanical acceleration condition are not necessarily equal, the temperature acceleration condition or the mechanical acceleration condition will be adjusted in subsequent tests to make the two equal. Specifically, the temperature acceleration condition can be adjusted to change the first equivalent test duration to make it equal to the second equivalent test duration. At this time, based on whether the real-time temperature reaches the equivalent damage temperature of the second equivalent test duration, at least one of the coolant temperature and the coolant flow rate is adjusted, including at least one of the following:

[0088] 1. In response to the real-time temperature not reaching the equivalent damage temperature of the second equivalent test duration, performing at least one of increasing the coolant temperature and reducing the coolant flow rate;

[0089] 2. In response to the real-time temperature exceeding the equivalent damage temperature of the second equivalent test duration, at least one of lowering the coolant temperature and increasing the coolant flow rate is performed.

[0090] In other possible implementations, the mechanical acceleration condition may be adjusted to change the second equivalent test duration to be equal to the first equivalent test duration. In this case, based on whether the real-time temperature reaches the equivalent damage temperature of the second equivalent test duration, at least one of the coolant temperature and the coolant flow rate is adjusted, including at least one of the following:

[0091] 1. In response to the real-time temperature not reaching the equivalent damage temperature of the first equivalent test duration, performing at least one of increasing the coolant temperature and reducing the coolant flow rate;

[0092] 2. In response to the real-time temperature exceeding the equivalent damage temperature of the first equivalent test duration, at least one of lowering the coolant temperature and increasing the coolant flow rate is performed.

[0093] Please refer to Figure 7 , Figure 7 : is a flow chart of an embodiment of a vehicle generator life prediction method in this application; specifically, the vehicle generator life prediction method includes:

[0094] S71: Obtaining a speed / torque combination, insulation winding temperature, and operating time of a rotor assembly in the generator under test, and obtaining a first life model of the insulation winding and a second life model of the rotor assembly; wherein the first life model and the second life model are obtained based on the above-mentioned vehicle generator reliability test method;

[0095] In some possible implementations, the first life model includes a first functional relationship between confidence and life at different insulation winding temperatures, and the second life model includes a second functional relationship between confidence and life at different speed / torque combinations.

[0096] S72: Predicting a first life span based on the first life model and the insulation winding temperature of the generator to be tested, and predicting a second life span based on the second life model and the speed / torque combination of the generator to be tested;

[0097] In some possible implementations, predicting a first life span based on a first life model and an insulation winding temperature of the generator to be tested includes: selecting a first functional relationship that matches the insulation winding temperature of the generator to be tested based on the first life model, and predicting the first life span at a plurality of target confidence levels based on the matched first functional relationship; predicting a second life span based on a second life model and a speed / torque combination of the generator to be tested includes: selecting a second functional relationship that matches the speed / torque combination of the generator to be tested based on the second life model, and predicting the second life span at a plurality of target confidence levels based on the matched second functional relationship;

[0098] Specifically, in the aforementioned embodiment, after obtaining at least three first test data of the insulation winding in the temperature dimension and at least three second test data of the rotor assembly in the mechanical dimension, data fitting is performed based on the obtained three first test data and second test data respectively with the aid of analysis software. Please refer to Figure 8 , Figure 8 This is a data fitting analysis diagram for the first test data. The first test data are fitted with Weibull distribution, 3-parameter Weibull distribution, exponential distribution and normal distribution respectively. Figure 8 As can be seen from the figure, the Weibull distribution has the best fit and the three lines are relatively parallel, so the best fit is the Weibull distribution. Figure 9 , Figure 9 is a schematic diagram of Weibull distribution fitting for three first test data; wherein the three first test data are obtained by testing the insulated winding at temperatures of 60 degrees Celsius, 80 degrees Celsius, and 100 degrees Celsius, respectively; Figure 9 In the figure, the three straight lines represent the relationship between the life span of the insulation winding and the confidence level at different temperatures. The larger the percentage value on the vertical axis, the lower the confidence level. The horizontal axis time represents the life span of the insulation winding at the corresponding temperature. As an example, the software can be used to calculate the life span of the insulation winding based on the confidence level. Figure 9 The resulting first functional relationship predicts, at a 50% confidence level, first lifespans of 146 hours, 81 hours, and 22 hours at temperatures of 60°C, 80°C, and 100°C, respectively. Similarly, in the aforementioned embodiment, at least three second test data points of the rotor assembly in the mechanical dimension were obtained, and their best fit was a Weibull distribution. A second functional relationship was obtained, and this can predict second lifespans at several confidence levels. This will not be further elaborated here.

[0099] S73: Based on the first life span, the second life span and the operating time, obtain the remaining life span of the generator to be tested.

[0100] In some possible implementations, the remaining life of the generator to be tested is obtained based on the first life, the second life, and the operating time, including: for several target confidence levels, based on the first life and the second life under the same target confidence, as well as the operating time, obtaining the remaining life of the generator to be tested under the corresponding target confidence level.

[0101] Specifically, after obtaining the first life time, the second life time and the operating time, the first life time and the second life time at the same confidence level can be subtracted from the operating time, and the smaller one is the remaining life time of the generator; for example, the first life time at 50% confidence level at 60 degrees Celsius is 146 hours, assuming that the second life time is 100 hours at 50% confidence level and the operating time is 60 hours, the remaining life time should be 40 hours; in other implementation scenarios, the reliability test will cause the generator rotor assembly to operate under different speed / torque conditions, so assuming that the generator rotor assembly operates under condition A for 40 hours, it has been running in working condition B for 10 hours, and applying the second functional relationship obtained above, it can be obtained that the second life of the generator rotor when operating in working condition A with a confidence level of 50% is 80 hours, which means that 1 / 2 of the loss has been caused; the second life of the generator rotor assembly when operating in working condition B with a confidence level of 50% is 100 hours, which means that 1 / 10 of the loss has been caused; adding the loss percentages of the generator rotor assembly under various working conditions to obtain a loss percentage of 60%, dividing the running time by the loss percentage to obtain the total life, and subtracting the running time from the total life to obtain the remaining life in the mechanical dimension.

[0102] See also Figure 10 , Figure 10 It is a schematic diagram of the framework of an embodiment of a reliability test device 100 for a vehicle generator in the present application. The reliability test device 100 for a vehicle generator includes: an acquisition module 101, which is used to obtain the operating data of the vehicle generators of several target users in various dimensions; wherein the various dimensions include temperature dimension, mechanical dimension and voltage dimension; a screening module 102, which is used to screen the operating data covering the target ratio in various dimensions as the target data of the corresponding dimension; a determination module 103, which is used to determine the first life model of the insulation winding based on the first test data of the insulation winding in the temperature dimension, and to determine the second life model of the rotor assembly based on the second test data of the rotor assembly in the mechanical dimension; a prediction module 104, which is used to determine the first life model of the insulation winding based on the first test data of the insulation winding in the temperature dimension, and to determine the second life model of the rotor assembly based on the second test data of the rotor assembly in the mechanical dimension; As well as the insulating winding temperature and the first operating time in the target data under the temperature dimension, the first equivalent test time at the reliability test temperature is predicted, and based on the second life model and the speed / torque combination and the second operating time in the target data under the mechanical dimension, the second equivalent test time under the reliability test speed / torque combination is predicted; the control module 105 is used to control the insulating winding of the target generator to operate at the insulating winding voltage in the target data under the voltage dimension and control the rotor assembly of the target generator to operate at the reliability test speed / torque combination based on the first equivalent test time and the second equivalent test time, so as to obtain the reliability test result of the target generator.

[0103] The above scheme, since the reliability testing device 100 for a vehicle generator implements the steps in the embodiment of the reliability testing method for a vehicle generator, by simultaneously performing reliability accelerated tests on the insulation winding and rotor of the motor, and making the motor assembly simultaneously operate under the temperature, speed / torque, and voltage conditions of the reliability accelerated test, can reduce the amount of test samples and the number of tests, thereby shortening the test time; and can better simulate the stress state of the motor after assembly, thereby improving the accuracy of the motor reliability test.

[0104] In some disclosed embodiments, the control module 105 includes a detection submodule 1051, an adjustment submodule 1052, and a result acquisition submodule 1053; wherein, the detection submodule 1051 is used to monitor the real-time temperature of the insulation winding while controlling the insulation winding of the target generator to operate at the insulation winding voltage in the target data under the voltage dimension, and controlling the rotor assembly of the target generator to operate at the reliability test speed / torque combination for the second equivalent test duration; the adjustment submodule 1052 is used to adjust at least one of the coolant temperature and the coolant flow rate based on whether the real-time temperature reaches the equivalent damage temperature for the second equivalent test duration; the result acquisition submodule 1053 controls the target generator to stop operating in response to the target generator having operated for the second equivalent test duration, and obtains the reliability test results of the target generator.

[0105] Therefore, by regulating the coolant temperature, temperature damage equivalence and mechanical damage equivalence can be achieved at the same time, making the reliability test closer to the actual operating conditions, the results more accurate, and reducing the number of test factors.

[0106] In some disclosed embodiments, the screening module 102 includes a first equivalent time calculation submodule 1021, a first sorting submodule 1022, and a first data selection submodule 1023; wherein, the first equivalent time calculation submodule 1021 is used to predict the equivalent time of the insulation winding at the target temperature based on the preset life model of the insulation winding and the insulation winding temperature and the first operation time in the operation data; the first sorting submodule 1022 is used to sort the various operation data under the temperature dimension in order from small to large according to the equivalent time; the first data selection submodule 1023 selects the operation data covering the target proportion as the target data of the temperature dimension based on the sorted operation data under the temperature dimension.

[0107] Therefore, by obtaining the operating data under the temperature dimension as the target data and screening the operating data covering the target ratio, the target data can meet specific requirements and make the subsequent model determination more accurate.

[0108] In some disclosed embodiments, the screening module 102 also includes a second equivalent time calculation submodule 1024, a second sorting submodule 1025, a second data selection submodule 1026 and a reference data selection submodule 1027; wherein, the reference data selection submodule 1027 is used to select one as reference data from the operating data of the mechanical dimension; the second equivalent time calculation submodule 1024 is used to predict the pseudo-damage value of the operating data relative to the reference data for each operating data under the mechanical dimension based on the preset life model of the rotor assembly; the second sorting submodule 1025 is used to sort each operating data under the mechanical dimension in order of the pseudo-damage value from small to large; the second data selection submodule 1026 selects the operating data covering the target ratio as the target data of the mechanical dimension based on the sorted operating data under the mechanical dimension.

[0109] Therefore, by obtaining the operating data in the mechanical dimension as the target data and screening the operating data covering the target ratio, the target data can meet specific requirements and make the subsequent model determination more accurate.

[0110] In some disclosed embodiments, the determination module 103 includes a first test data acquisition submodule 1031, a first function fitting submodule 1032, and a first update submodule 1033, wherein the first test data acquisition submodule 1031 acquires at least three first test data of the insulating winding in the temperature dimension; wherein the first test data includes the failure time of the component when operating at the test temperature; the first function fitting submodule 1032 performs function fitting on the preset life model of the insulating winding based on at least three first test data to obtain the predicted values ​​of the empirical parameters in the preset life model; the first update submodule 1033 updates the preset life model based on the predicted values ​​of the empirical parameters in the preset life model of the insulating winding to obtain the first life model.

[0111] Therefore, obtaining predicted values ​​of empirical parameters through experiments and determining the first life model can make the calculation results of the first life model more accurate and avoid under-testing and over-testing.

[0112] In some disclosed embodiments, the determination module 103 also includes a second test data acquisition submodule 1034, a second function fitting submodule 1035, and a second update submodule 1036, wherein the second test data acquisition submodule 1034 acquires at least three second test data of the rotor assembly in the mechanical dimension; wherein the second test data includes the failure time of the component when operating under the test speed / torque combination; the second function fitting submodule 1035 performs function fitting on the preset life model of the rotor assembly based on at least three second test data to obtain the predicted values ​​of the empirical parameters in the preset model; the second update submodule 1036 updates the preset life model based on the predicted values ​​of the empirical parameters in the preset life model of the rotor assembly to obtain a second life model.

[0113] Therefore, obtaining predicted values ​​of empirical parameters through experiments and determining the second life model can make the calculation results of the second life model more accurate and avoid under-testing and over-testing.

[0114] See also Figure 11 , Figure 11 The figure is a schematic diagram of a framework of an embodiment of a vehicle generator life prediction device 110 in the present application. Specifically, the life prediction device 110 includes an acquisition module 111 for acquiring the speed / torque combination, insulation winding temperature, and operating time of the rotor assembly in the generator under test, and acquiring a first life model of the insulation winding and a second life model of the rotor assembly; wherein the first life model and the second life model are obtained based on the reliability test device described above; a prediction module 112 for predicting a first life duration based on the first life model and the insulation winding temperature of the generator under test, and a second life duration based on the second life model and the speed / torque combination of the generator under test; and a determination module 113 for determining the remaining life duration of the generator under test based on the first life duration, the second life duration, and the operating time.

[0115] The above scheme can calculate the remaining life of the generator through the life and operating time under different test dimensions.

[0116] In some disclosed embodiments, the prediction module 112 includes a first prediction submodule 1121 and a second prediction submodule 1122; wherein the first prediction submodule 1121 is configured to select a first functional relationship that matches the insulation winding temperature of the generator under test based on a first life model, and predict a first life duration at a plurality of target confidence levels based on the matched first functional relationship; and the second prediction submodule 1122 is configured to select a second functional relationship that matches the speed / torque combination of the generator under test based on a second life model, and predict a second life duration at a plurality of target confidence levels based on the matched second functional relationship.

[0117] In some disclosed embodiments, the determination module 113 includes a determination submodule 1131, which is used to obtain the remaining life of the generator to be tested at the corresponding target confidence level for several target confidence levels based on the first life span and the second life span at the same target confidence level, as well as the operating time.

[0118] See also Figure 12 , Figure 12This is a schematic diagram of an embodiment of an electronic device 120 of the present application. Electronic device 120 includes a memory 121 and a processor 122, which are coupled to each other. Memory 121 stores program instructions, and processor 122 is configured to execute the program instructions to implement the steps described in any of the aforementioned generator reliability testing methods or generator life prediction method embodiments. Specifically, electronic device 120 may include, but is not limited to, desktop computers, laptop computers, servers, mobile phones, tablet computers, and in-vehicle computers, among others.

[0119] Specifically, the processor 122 is used to control itself and the memory 121 to implement the steps in any of the above-mentioned embodiments of the circumference calibration method. The processor 122 can also be called a CPU (Central Processing Unit). The processor 122 may be an integrated circuit chip with signal processing capabilities. The processor 122 can also be a general-purpose processor, a digital signal processor (DSP), an application-specific integrated circuit (ASIC), a field-programmable gate array (FPGA) or other programmable logic devices, discrete gates or transistor logic devices, discrete hardware components. The general-purpose processor can be a microprocessor or the processor can also be any conventional processor, etc. In addition, the processor 122 can be implemented by an integrated circuit chip.

[0120] In the above scheme, since the electronic device 120 implements the steps in the embodiment of the vehicle generator reliability test or life prediction method, on the one hand, by simultaneously performing reliability accelerated tests on the insulation winding and rotor of the motor, and making the motor assembly work simultaneously under the temperature, speed / torque and voltage conditions of the reliability accelerated test, the test sample size and the number of tests can be reduced, thereby shortening the test time; and it can better simulate the stress state of the motor after assembly, thereby improving the accuracy of the motor reliability test. On the other hand, the remaining life of the generator can be calculated through the life and operating time under different test dimensions.

[0121] See also Figure 13 , Figure 13 This is a schematic diagram of a computer-readable storage medium 130 according to an embodiment of the present invention. The computer-readable storage medium 130 stores program instructions 131 executable by a processor, which are used to implement the steps of any of the above-mentioned vehicle generator reliability test methods or vehicle generator life prediction methods.

[0122] The above scheme, since the computer-readable storage medium 130 implements the steps in the above-mentioned vehicle generator reliability test or life prediction method embodiment, on the one hand, by simultaneously performing reliability accelerated tests on the insulation winding and rotor of the motor, and making the motor assembly work simultaneously under the temperature, speed / torque, and voltage conditions of the reliability accelerated test, can reduce the test sample size and the number of tests, thereby shortening the test time; and can better simulate the stress state of the motor after assembly, thereby improving the accuracy of the motor reliability test; on the other hand, through the life and operating time under different test dimensions, the remaining life of the generator can be calculated.

[0123] In the several embodiments provided in this application, it should be understood that the disclosed methods and devices can be implemented in other ways. For example, the device implementation methods described above are only schematic. For example, the division of modules or units is only a logical function division. There may be other division methods in actual implementation. For example, multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some interfaces, and the indirect coupling or communication connection of devices or units can be electrical, mechanical or other forms.

[0124] Units described as separate components may or may not be physically separate, and components shown as units may or may not be physical units, that is, they may be located in one place or distributed across multiple network units. Some or all of these units may be selected to achieve the purpose of this embodiment according to actual needs.

[0125] In addition, the functional units in the various embodiments of the present application may be integrated into a single processing unit, or each unit may exist physically separately, or two or more units may be integrated into a single unit. The aforementioned integrated units may be implemented in the form of hardware or software functional units.

[0126] If the integrated unit is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present application, or the part that contributes to the prior art, or all or part of the technical solution can be embodied in the form of a software product, which is stored in a storage medium and includes several instructions for enabling a computer device (which can be a personal computer, server, or network device, etc.) or a processor to execute all or part of the steps of each embodiment method of the present application. The aforementioned storage medium includes: various media that can store program codes, such as a USB flash drive, a mobile hard disk, a read-only memory (ROM), a random access memory (RAM), a magnetic disk or an optical disk.

[0127] If the technical solution of this application involves personal information, the product that applies the technical solution of this application has clearly informed the personal information processing rules and obtained the individual's voluntary consent before processing personal information. If the technical solution of this application involves sensitive personal information, the product that applies the technical solution of this application has obtained the individual's separate consent before processing sensitive personal information, and at the same time meets the "explicit consent" requirement. For example, on personal information collection devices such as cameras, a clear and prominent sign is set to inform that the personal information collection scope has been entered and personal information will be collected. If the individual voluntarily enters the collection scope, it is deemed that they agree to the collection of their personal information; or on the personal information processing device, when the personal information processing rules are notified by obvious signs / information, the individual's authorization is obtained through pop-up information or by asking the individual to upload their personal information; among which, the personal information processing rules may include information such as the personal information processor, the purpose of personal information processing, the processing method, and the type of personal information processed.

Claims

1. A reliability test method for a vehicle generator, characterized in that: include: Obtaining operational data of vehicle generators of several target users in various dimensions; wherein the various dimensions include temperature dimension, mechanical dimension, and voltage dimension; Selecting operating data covering a target ratio in each dimension as target data for the corresponding dimension, and determining a first life model of the insulation winding based on first test data of the insulation winding in the temperature dimension, and determining a second life model of the rotor assembly based on second test data of the rotor assembly in the mechanical dimension; Based on the first life model and the insulation winding temperature and the first operating duration in the target data under the temperature dimension, predict a first equivalent test duration at the reliability test temperature; and based on the second life model and the speed / torque combination and the second operating duration in the target data under the mechanical dimension, predict a second equivalent test duration at the reliability test speed / torque combination; Based on the first equivalent test duration and / or the second equivalent test duration, the insulating winding of the target generator is controlled to operate at the insulating winding voltage in the target data under the voltage dimension, and the rotor assembly of the target generator is controlled to operate at the reliability test temperature and / or reliability test speed / torque combination to obtain the reliability test result of the target generator.

2. The method according to claim 1, characterized in that The step of controlling the insulating winding of the target generator to operate at the insulating winding voltage in the target data under the voltage dimension based on the first equivalent test duration and / or the second equivalent test duration, and controlling the rotor assembly of the target generator to operate at the reliability test temperature and / or the reliability test speed / torque combination, to obtain a reliability test result of the target generator includes: During the process of controlling the insulating winding of the target generator to operate at the insulating winding voltage in the target data under the voltage dimension, and controlling the rotor assembly of the target generator to operate at the reliability test speed / torque combination for the second equivalent test duration, monitoring the real-time temperature of the insulating winding; adjusting at least one of a coolant temperature and a coolant flow rate based on whether the real-time temperature reaches an equivalent damage temperature of the second equivalent test duration; In response to the target generator having been running for the second equivalent test time, the target generator is controlled to stop running, and a reliability test result of the target generator is obtained.

3. The method according to claim 2, characterized in that The adjusting at least one of the coolant temperature and the coolant flow rate based on whether the real-time temperature reaches the equivalent damage temperature of the second equivalent test duration includes at least one of the following: In response to the real-time temperature not reaching the equivalent damage temperature of the second equivalent test duration, performing at least one of increasing the coolant temperature and reducing the coolant flow rate; In response to the real-time temperature exceeding the equivalent damage temperature of the second equivalent test duration, at least one of lowering the coolant temperature and increasing the coolant flow rate is performed.

4. The method according to claim 1, wherein In a case where the various dimensions include the temperature dimension, the filtering of the operating data covering the target ratio in the various dimensions as the target data corresponding to the dimension includes: For each of the operating data under the temperature dimension, based on a preset life model of the insulation winding and the insulation winding temperature and the first operating duration in the operating data, predicting an equivalent operating time of the insulation winding at a target temperature; Sort the operation data under the temperature dimension in ascending order of the equivalent time length; Based on the sorted operating data under the temperature dimension, operating data covering the target ratio is selected as target data in the temperature dimension.

5. The method according to claim 1, characterized in that In the case where the various dimensions include the mechanical dimension, the filtering of the operating data covering the target ratio in the various dimensions as the target data corresponding to the dimension includes: Among the operation data of the mechanical dimension, select one as reference data; For each of the operating data in the mechanical dimension, based on a preset life model of the rotor assembly, predict a pseudo damage value of the operating data relative to the reference data; sorting the operation data under the mechanical dimension in ascending order of the pseudo damage values; Based on the sorted operating data under the mechanical dimension, operating data covering the target ratio is selected as target data of the mechanical dimension.

6. The method according to claim 1, characterized in that The determining, based on first test data of the insulation winding under the temperature dimension, a first life model of the insulation winding includes: Acquire at least three first test data of the insulated winding under the temperature dimension; wherein the first test data includes the failure time of a component when operating at the test temperature; Performing function fitting on a preset life model of the insulation winding based on the at least three first test data to obtain predicted values ​​of empirical parameters in the preset life model; The preset life model of the insulation winding is updated based on the predicted value of the empirical parameter in the preset life model to obtain the first life model.

7. The method according to claim 1, characterized in that The determining, based on the second test data of the rotor assembly under the mechanical dimension, a second life model of the rotor assembly includes: Acquire at least three second test data of the rotor assembly under the mechanical dimension; wherein the second test data includes a component failure time when operating under a test speed / torque combination; Performing function fitting on a preset life model of the rotor assembly based on the at least three second test data to obtain predicted values ​​of empirical parameters in the preset life model; The preset life model of the rotor assembly is updated based on the predicted values ​​of the empirical parameters in the preset life model to obtain the second life model.

8. A method for predicting the life of a vehicle generator, characterized in that: include: Obtaining a speed / torque combination, an insulation winding temperature, and an operating time of a rotor assembly in a generator to be tested, and obtaining a first life model of the insulation winding and a second life model of the rotor assembly; wherein the first life model and the second life model are obtained based on the method according to any one of claims 1 to 7; Predicting a first life span based on the first life model and the insulation winding temperature of the generator to be tested, and predicting a second life span based on the second life model and the speed / torque combination of the generator to be tested; The remaining life span of the generator to be tested is obtained based on the first life span, the second life span and the operating time.

9. The method according to claim 8, characterized in that The first life model includes a first functional relationship between confidence and life duration at different insulation winding temperatures, and the second life model includes a second functional relationship between confidence and life duration at different speed / torque combinations.

10. The method according to claim 9, characterized in that The predicting of a first life span based on the first life model and the insulation winding temperature of the generator to be tested includes: selecting a first functional relationship that matches the insulation winding temperature of the generator to be tested based on the first life model, and predicting first life durations at a plurality of target confidence levels based on the matched first functional relationship; The predicting of a second life span based on the second life model and the speed / torque combination of the generator to be tested includes: selecting a second functional relationship matching the speed / torque combination of the generator to be tested based on the second life model, and predicting second life durations at the plurality of target confidence levels based on the matching second functional relationship; The obtaining of the remaining life of the generator to be tested based on the first life, the second life, and the operating time includes: For the plurality of target confidence levels, based on the first life span and the second life span at the same target confidence level, and the operating time, the remaining life span of the generator to be tested at the corresponding target confidence level is obtained.

11. A reliability test device for a vehicle generator, characterized in that: include: An acquisition module is used to acquire operating data of vehicle generators of several target users in various dimensions; wherein the various dimensions include temperature dimension, mechanical dimension and voltage dimension; A screening module, configured to screen the operating data covering the target ratio in each of the dimensions as target data corresponding to the dimensions; a determination module, configured to determine a first life model of the insulation winding based on first test data of the insulation winding in the temperature dimension, and to determine a second life model of the rotor assembly based on second test data of the rotor assembly in the mechanical dimension; a prediction module, configured to predict a first equivalent test duration at a reliability test temperature based on the first life model and the insulation winding temperature and the first operating duration in the target data under the temperature dimension, and to predict a second equivalent test duration at a reliability test speed / torque combination based on the second life model and the speed / torque combination and the second operating duration in the target data under the mechanical dimension; A control module is used to control the insulation winding of the target generator to operate at the insulation winding voltage in the target data under the voltage dimension and control the rotor assembly of the target generator to operate at the reliability test speed / torque combination based on the first equivalent test duration and the second equivalent test duration, so as to obtain a reliability test result of the target generator.

12. A vehicle generator life prediction device, characterized in that: include: an acquisition module, configured to acquire a speed / torque combination, an insulation winding temperature, and an operating time of a rotor assembly in a generator under test, and to acquire a first life model of the insulation winding and a second life model of the rotor assembly; wherein the first life model and the second life model are obtained based on the apparatus according to claim 11; a prediction module, configured to predict a first life span based on the first life model and the insulation winding temperature of the generator to be tested, and to predict a second life span based on the second life model and the speed / torque combination of the generator to be tested; A determination module is used to obtain the remaining life of the generator to be tested based on the first life, the second life and the operating time.

13. An electronic device, characterized in that: It includes a memory and a processor coupled to each other, the memory storing program instructions, and the processor being used to execute the program instructions to implement the reliability test method of a vehicle generator as described in any one of claims 1 to 7, or the life prediction method of a vehicle generator as described in any one of claims 8 to 10.

14. A computer-readable storage medium, characterized in that Program instructions that can be executed by a processor are stored, and the program instructions are used to implement the reliability test method of the vehicle generator according to any one of claims 1 to 7, or the life prediction method of the vehicle generator according to any one of claims 8 to 10.

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