Method for converting between user field reliability and verification reliability

Abstract

The present application relates to the field of vehicles, and more particularly, to a method, device and medium for converting between user field reliability and verification reliability of an electric drive system. The method comprises the steps of determining the torque and the rotating speed of a motor required by a sample user and voltage and current corresponding to the torque and the rotating speed based on the driving working condition of the sample user; calculating mechanical damage and / or thermal damage of the electric drive systems of all the sample users based on the torque, the rotating speed, the voltage and the current; according to mechanical damage and / or thermal damage of the electric drive system from all sample users, first-class users and second-class users are divided, and the ratio of the first-class users to the second-class users is determined; based on the mechanical damage and / or thermal damage of the first-class user and the second-class user, calculating a damage variation coefficient of the second-class user relative to the first-class user; and converting one of the user field reliability and the verification reliability into the other on the basis of the damage variation coefficient and the proportion of the first-class user and the second-class user.

Description

Technical Field

[0001] This application relates to the field of vehicles, and more specifically, to methods, apparatus, and media for converting between user-field reliability and verified reliability of electric drive systems. Background Technology

[0002] In the verification testing of a vehicle's electric drive system, the first step is to determine the reliability requirements to be met. If the reliability of the vehicle's electric drive system exceeds the reliability requirements, the verification test can be passed. Otherwise, adjustments or iterations of the vehicle's electric drive system are necessary. However, verification testing is typically conducted under a limited number of test conditions, and the process may involve wasted resources. Therefore, it is desirable to avoid resource waste during development and verification testing to make the process more efficient and flexible.

[0003] It should be noted that the information disclosed in the background section above is only used to enhance the understanding of the background of this application, and therefore may include information that does not constitute prior art known to those skilled in the art. Summary of the Invention

[0004] To address or at least mitigate one or more of the above problems, the following technical solutions are provided. This application provides a method, apparatus, and medium for converting between user field reliability and verification reliability of an electric drive system. This method can classify users into Class I and Class II users based on the driving conditions of existing sample users, and derive the correspondence between the reliability of all users (including Class I and Class II users) and the reliability of Class I users. Based on this correspondence, the corresponding reliability of Class I users can be selected according to the reliability requirements of all users, and used as the verification test standard under stringent test conditions. Through reasonable calculation and conversion, the method can select reliability requirements that match the stringent test conditions, thereby avoiding over-testing and greatly improving product development efficiency. Similarly, based on this correspondence, the user field reliability achievable in actual use by all users or Class II users can be predicted based on the verification reliability achieved during the testing and verification process.

[0005] According to a first aspect of this application, a method is provided for converting between user field reliability and verification reliability of an electric drive system. The method includes: determining, based on the driving conditions of sample users, the required motor torque, speed, and corresponding voltage and current for the sample users; calculating mechanical and / or thermal damage to the electric drive systems of all sample users based on the torque, speed, and voltage and current, respectively; classifying users into Category I and Category II based on the mechanical and / or thermal damage to the electric drive systems of all sample users, and determining the proportion of Category I and Category II users, wherein Category I users are sample users with driving conditions close to the test conditions, and Category II users are sample users other than Category I users; calculating the damage variation coefficient of Category II users relative to Category I users based on the mechanical and / or thermal damage to Category I and Category II users; and converting one of the user field reliability and the verification reliability into the other based on the damage variation coefficient and the proportion of Category I and Category II users, wherein the user field reliability is the reliability of the user in actual use, and the verification reliability is the reliability verified under test conditions.

[0006] As an alternative or supplement to the above solutions, in a method according to an embodiment of this application, the mechanical damage of the electric drive system includes bending fatigue, contact fatigue, and impact fatigue; and the thermal damage of the electric drive system includes stator thermal aging, thin film capacitor thermal aging, and power module thermal fatigue.

[0007] As an alternative or supplement to the above solutions, in a method according to an embodiment of this application, the damage variation coefficient is the damage ratio between a type I user and a type II user; and wherein the damage variation coefficient includes a mechanical damage variation coefficient, a thermal damage variation coefficient, and a system variation coefficient, the value of which is equal to the minimum of the mechanical damage variation coefficient and the thermal damage variation coefficient.

[0008] As an alternative or supplement to the above solutions, in a method according to an embodiment of this application, the value of the mechanical damage coefficient of variation is equal to the minimum value among the bending fatigue coefficient of variation, the contact fatigue coefficient of variation, and the impact fatigue coefficient of variation; and wherein the value of the thermal damage coefficient of variation is equal to the minimum value among the stator thermal aging coefficient of variation, the thin film capacitor thermal aging coefficient of variation, and the power module thermal fatigue coefficient of variation.

[0009] As an alternative or supplement to the above solutions, in a method according to an embodiment of this application, the sample users are selected based on user region, user activation time, and user annual mileage.

[0010] As an alternative or supplement to the above solutions, in a method according to an embodiment of this application, converting the verification reliability into the user field reliability includes: determining the specification life of a type of user based on the verification reliability; calculating the specification life of a type of user based on the specification life of the type of user and the damage variation coefficient of a type of user; calculating the reliability of the type of user based on the specification life of the type of user according to the Weibull distribution; and weighting and summing the reliability of the type of user and the reliability of the type of user based on the proportion of the type of user and the type of user to obtain the user field reliability.

[0011] As an alternative or supplement to the above solutions, in a method according to an embodiment of this application, converting the user field reliability into the verification reliability includes: calculating the specification lifetime of a type of user based on the damage variation coefficient of the two types of users, the proportion of type I users to type II users, and the user field reliability; and calculating the reliability of a type of user based on the specification lifetime of the type of user according to the Weibull distribution, as the verification reliability.

[0012] As an alternative or supplement to the above solutions, in a method according to an embodiment of this application, the reliability of each user site and the verification reliability corresponding to each user site reliability are stored in the form of a lookup table.

[0013] According to a second aspect of this application, an apparatus is provided for converting between user-site reliability and verified reliability of an electric drive system, the apparatus comprising: a memory; a processor; and a computer program stored in the memory and executable by the processor, the execution of the computer program causing any of the methods according to the first aspect of this application to be performed.

[0014] According to a third aspect of this application, a computer-readable storage medium is provided, the computer-readable storage medium including instructions that, when executed, perform any one of the methods according to a first aspect of this application. Attached Figure Description

[0015] The above and / or other aspects and advantages of this application will become clearer and more readily understood through the following description taken in conjunction with the accompanying drawings, in which the same or similar elements are denoted by the same reference numerals. In the drawings: Figure 1 This is a flowchart of a method 100 for converting between user field reliability and verified reliability of an electric drive system according to an embodiment of this application; Figure 2 This is a block diagram of a model for calculating the torque, speed, and corresponding voltage and current required by a sample user according to an embodiment of this application; Figure 3A This is a block diagram of a model for calculating mechanical damage to an electric drive system according to an embodiment of this application; Figure 3B This is a block diagram of a model for calculating thermal damage to an electric drive system according to an embodiment of this application; Figure 4 This is a schematic diagram illustrating the relationship between the coefficient of variation and reliability of users at different quantiles according to embodiments of this application; and Figure 5 This is a block diagram of an apparatus 500 for converting between user-site reliability and verified reliability of an electric drive system according to an embodiment of this application. Detailed Implementation

[0016] The following detailed description is merely exemplary in nature and is not intended to limit the disclosed technology or its application and use. Furthermore, it is not intended to be bound by any express or implied theory presented in the foregoing technical fields, background art, or the following detailed description.

[0017] In the following detailed description of the embodiments, numerous specific details are set forth in order to provide a more thorough understanding of the disclosed technology. However, it will be apparent to those skilled in the art that the disclosed technology can be practiced without these specific details. In other instances, well-known features have not been described in detail to avoid unnecessarily complicating the description.

[0018] Terms such as "possessing" and "comprising" indicate that, in addition to the units (modules) and steps directly and explicitly stated in the specification and claims, the technical solution of this application does not exclude the presence of other units (modules) and steps not directly or explicitly stated. Terms such as "first" and "second" do not indicate the order of the units in terms of time, space, size, etc., but are merely used to distinguish the units. Furthermore, the steps in this document are not limited to being performed in the order they are written; a step written later may be performed simultaneously with or before a step written earlier.

[0019] Furthermore, it should be noted that all user personal information involved in the various embodiments of this application is processed in strict accordance with legal and regulatory requirements, adhering to the principles of legality, legitimacy, and necessity, and based on reasonable purposes within the business scenario. This processing involves personal information actively provided by users during the use of the product / service, information generated as a result of using the product / service, and personal information obtained with user authorization. The user personal information processed by the applicant may vary depending on the specific product / service scenario and should be based on the specific scenario in which the user uses the product / service. This may involve user account information, device information, driving information, vehicle information, or other related information. The applicant will treat user personal information and its processing with a high degree of diligence. The applicant attaches great importance to the security of user personal information and has taken industry-standard and reasonable security measures to protect user information and prevent unauthorized access, disclosure, use, modification, damage, or loss of personal information.

[0020] During the verification testing of vehicle electric drive systems, the reliability of the system needs to be verified under test conditions. If this reliability exceeds the reliability requirements specified in the standard, the test passes; otherwise, it fails. However, to accommodate various real-world usage scenarios and habits, current test conditions are essentially close to the limits of normal use. In actual user experience, most users have more user-friendly habits, resulting in significantly higher field reliability compared to verification test reliability (which meets the standard's reliability requirements). Specifically, field reliability is the reliability achieved by the vehicle (or its subsystems) during actual user use, while verification reliability is the reliability achieved during verification testing. Field reliability may differ for users with different driving habits.

[0021] The embodiments of this application will now be described in detail with reference to the accompanying drawings. Figure 1 , Figure 1 This is a flowchart of a method 100 for converting between user-site reliability and verified reliability of an electric drive system according to an embodiment of this application. Figure 1 As illustrated in the diagram, in step 102, based on the driving conditions of the sample users, the required motor torque, speed, and corresponding voltage and current for the sample users are determined. Sample users can be test vehicles from previous tests or real users who have agreed to provide their actual driving data. Furthermore, sample users can be selected from all users based on user region, user activation time, and user annual mileage. As an example, users from different provinces who were first activated more than two years ago and whose annual mileage exceeds a threshold mileage can be selected as sample users.

[0022] In some embodiments, the torque, speed, and corresponding voltage and current required by the sample user can be calculated using formulas or models. (Reference) Figure 2 , Figure 2 This is a block diagram of a model for calculating the torque, speed, and corresponding voltage and current required by a sample user, according to an embodiment of this application. Figure 2 As illustrated in the diagram, the model's input can include data related to inherent vehicle parameters (such as vehicle weight, frontal area, electric drive characteristics, tire rolling radius, and electric drive gear ratio) and user driving conditions. Based on these inputs, the model can calculate the motor torque, motor speed, voltage, and current of the sample user. Specifically, the motor torque Tq can be calculated using the following formula: (1) Among them, i g It is the electric drive gear ratio, r is the tire rolling radius, F g It is rolling resistance, F w It is air resistance, F j It is acceleration drag and F i It is the slope resistance.

[0023] F g F w F j and F i It can be further calculated using the following formulas: (2) Where m is the total mass of the vehicle, and g is the acceleration due to gravity. a It is the slope angle (related to the area). f is the rolling resistance coefficient; Cd is the drag coefficient; A is the frontal area; u is the vehicle speed (e.g., the average vehicle speed of the sample users); & is the rotational mass conversion factor; du / dt is the rate of change of vehicle speed; the values ​​of these parameters can be obtained based on the vehicle's own parameters and driving conditions.

[0024] Furthermore, the motor speed n of the sample users can be obtained using the following formula: (3) Where u is the vehicle speed, i g is the electric drive gear ratio, and r is the tire rolling radius.

[0025] After obtaining the torque Tq and speed n required by the sample user, the corresponding values ​​of the motor's direct-axis current Id and quadrature-axis current Iq in a two-phase rotating coordinate system can be obtained by looking up a table. Next, the three-phase AC voltage and AC current of the motor can be calculated using the inverse Park transform and the inverse Clark transform.

[0026] Specifically, based on the motor's Id and Iq values, the following can be obtained through the Park transformation: (4) Where Iα is the α-axis current in the two-phase stationary coordinate system, Iβ is the β-axis current in the two-phase stationary coordinate system, and θ is the rotor angle. Then, based on Iα and Iβ, the values ​​of the three-phase currents Ia, Ib, and Ic in the three-phase stationary coordinate system can be obtained through the Clark inverse transformation, as follows: (5) Similarly, based on the motor's Id and Iq values, combined with other parameters related to the sample user's motor, the values ​​of the three-phase AC voltages Ua, Ub, and Uc can be obtained through (6) to (8). Specifically: (6) (7) (8) Where Ud is the d-axis voltage in the two-phase rotating coordinate system, Uq is the q-axis voltage in the two-phase rotating coordinate system, Uα is the α-axis voltage in the two-phase stationary coordinate system, Uβ is the β-axis voltage in the two-phase stationary coordinate system, Rs is the stator phase resistance, ΔUd(PID) and ΔUq(PID) are the corrections for the d-axis and q-axis voltages, respectively, Ld and Lq are the direct-axis (d-axis) and quadrature-axis (q-axis) inductances, respectively, ωe is the electric angular velocity, and λm is the permanent magnet flux linkage.

[0027] In step 102, the torque, speed and voltage and current required by the sample user can be determined by formulas (1) to (8) above.

[0028] Then, in step 104, using the mechanical damage model and / or thermal damage model of the electric drive system, the mechanical damage and / or thermal damage of the sample user's electric drive system can be calculated based on the torque, speed, voltage, and current obtained in step 102, respectively. Next, refer to... Figure 3A The calculation process for mechanical damage will be described in detail below. In some embodiments, the mechanical damage of an electric drive system includes bending fatigue, contact fatigue, and impact fatigue. Figure 3A This is a block diagram of a model for calculating mechanical damage to an electric drive system according to an embodiment of this application. Figure 3AAs illustrated, in some embodiments, the model takes torque Tq and rotational speed n as inputs, and combines torque variation ΔTq, bending fatigue parameter h, contact fatigue parameter l, and impact fatigue parameter m to calculate bending fatigue damage W1, contact fatigue damage W2, and impact fatigue damage W3 of an electric drive system (e.g., gears, shafts, bearings, or other components in an electric drive system) using the following formulas: (9) Among them, bending fatigue parameter h, contact fatigue parameter l, and impact fatigue parameter m are material coefficients, which are related to the material of the parts, heat treatment process, and damage mechanism; N and ∆Tq are the cycle number and torque change value obtained through rainflow statistics, respectively; ∆t is the sampling time interval.

[0029] As described above, using formula (9), the mechanical damage to the electric drive system of all sample users can be calculated based on the torque and speed required by all sample users. The following will refer to... Figure 3B This describes the process of calculating the thermal damage of an electric drive system based on the voltage and current required by all sample users. Figure 3B This is a block diagram of a model for calculating thermal damage to an electric drive system according to embodiments of this application. In some embodiments, thermal damage to the electric drive system includes stator thermal aging, thin-film capacitor thermal aging, and power module thermal fatigue. Figure 3B As shown in the diagram, the model's inputs include the three-phase voltage and current of the electric drive system, as well as ambient temperature, coolant temperature, coolant flow rate, and switching frequency. Specifically, the stator thermal aging damage L1, thin-film capacitor thermal aging damage L2, and power module thermal fatigue damage L3 in the electric drive system can be calculated using the following formulas: (10) Where T1 and T2 are the stator and capacitor operating temperatures, respectively, Ths is the capacitor's maximum rated operating temperature; Ea is the activation energy, k is the Boltzmann constant; A1~A3, β1~β6, δ1~δ2, t on D is a constant; ∆T3, T 3max These are the temperature rise and maximum junction temperature of the power module, respectively; U n U w These represent the rated voltage and operating voltage; I represents the load current of the power module, and V represents the blocking voltage of the power module.

[0030] Therefore, using formula (10), the thermal damage of the electric drive system of all sample users can be calculated based on the voltage and current required by all sample users.

[0031] return Figure 1In step 106, users can be categorized into Category I and Category II users based on the mechanical and / or thermal damage to the electric drive systems of all sample users, and the proportion of Category I and Category II users can be determined. Category I users are sample users with driving conditions close to the test conditions, while Category II users can be all sample users other than Category I users. Specifically, based on the mechanical and thermal damage values ​​of all sample users, they can be divided into different ranges, such as 0-10% percentile (the 10% of sample users with the lowest damage values, possibly representing the most user-friendly driving experience), 10-25% percentile...90-95% percentile, and greater than 95% percentile (the 5% of sample users with the highest damage values, possibly representing the sample users whose driving conditions are closest to the test conditions). In some embodiments, Category I users are defined as sample users with damage values ​​greater than or equal to the 95th percentile, and users in other ranges (damage values ​​between the 0th and 95th percentiles) are all Category II users. That is, compared to Category I users, Category II users can include sample users from multiple ranges. Furthermore, a user category I may include one or more sample users, and a user category II may also include one or more sample users. Accordingly, the proportion p of the number of sample users in each interval range (e.g., including both user categories I and II) to the total number of sample users can be determined. For example, the proportion of sample users in the 0-10% quantile is p1, the proportion in the 10-25% quantile is p2, ..., the proportion in the 90-95% quantile is pn, and the proportion in the quantile greater than 95% is p.

[0032] Next, in step 108, based on the mechanical and / or thermal damage of Class I and Class II users, the coefficient of variation (CVR) of damage for Class II users relative to Class I users is calculated. Using Class I users as the standard, the CVR of damage for Class I users is 1, and the CVR of damage for Class II users is equal to the damage ratio between Class I and Class II users (e.g., the ratio of the maximum damage values ​​of sample users within two ranges). In an embodiment where sample users with damage values ​​greater than the 95th percentile are defined as Class I users, the damage ratio between Class I users and Class II users in different ranges can be determined separately. For example, the CVR of damage between Class I users and Class II users in the 0–10th percentile range is the first CVR (e.g., the ratio of the damage value of the user at the 100th percentile to that of the user at the 10th percentile), the CVR of damage between Class I users and Class II users in the 10–25th percentile range is the second CVR (e.g., the ratio of the damage value of the user at the 100th percentile to that of the user at the 25th percentile), and so on.

[0033] As mentioned above, damage to the electric drive system includes mechanical damage and / or thermal damage. Accordingly, in some embodiments, the damage coefficient of variation includes the mechanical damage coefficient of variation (CVR1), the thermal damage coefficient of variation (CVR2), and the system coefficient of variation (CVR), where the system coefficient of variation (CVR) is equal to the minimum of the mechanical damage coefficient of variation (CVR1) and the thermal damage coefficient of variation (CVR2). Further, mechanical damage includes bending fatigue, contact fatigue, and impact fatigue; therefore, the mechanical damage coefficient of variation (CVR1) is equal to the minimum of the bending fatigue coefficient of variation, the contact fatigue coefficient of variation, and the impact fatigue coefficient of variation. Similarly, since thermal damage includes stator thermal aging, thin-film capacitor thermal aging, and power module thermal fatigue, the thermal damage coefficient of variation (CVR2) is equal to the minimum of the stator thermal aging coefficient of variation, the thin-film capacitor thermal aging coefficient of variation, and the power module thermal fatigue coefficient of variation.

[0034] As an example, for sample users whose damage values ​​are in the 80% to 90% quantile (e.g., sample users in the i-th interval), the coefficient of variation for bending fatigue is 3.1, the coefficient of variation for contact fatigue is 1.8, and the coefficient of variation for impact fatigue is 1.9. Therefore, the coefficient of variation for mechanical damage CVR1 for sample users in this quantile is... i =1.8 (the minimum of 3.1, 1.8, and 1.9). Meanwhile, for the sample users at this quantile, the stator thermal aging coefficient of variation is 1.6, the thin-film capacitor thermal aging coefficient of variation is 1.5, and the power module thermal fatigue coefficient of variation is 1.6. Therefore, the thermal damage coefficient of variation CVR2 for the sample users at this quantile is... i =1.5 (the minimum of 1.6, 1.5, and 1.6). Then, the systematic coefficient of variation (CVR) for the sample users at that quantile can be determined. i =1.5 (the minimum value between 1.8 and 1.5).

[0035] Continue to refer to Figure 1 In step 110, based on the damage variation coefficient and the proportion of Class I users and Class II users, one of the user field reliability and the verification reliability is converted into the other. The user field reliability is the user field reliability for all sample users, including Class II users and Class I users, while the verification reliability is the verification reliability to be achieved under the operating conditions of Class I users.

[0036] Specifically, the coefficient of variation of impairment for type II users relative to type I users, determined in step 108, reflects the degree to which the impairment of type II users at that quantile is less severe than that of type I users. A larger coefficient of variation indicates that the impairment of type II users at that quantile is less severe than that of type I users. Therefore, based on this correlation, the reliability of type II users can be calculated given the reliability of type I users. Furthermore, this correlation allows the conversion between user field reliability and verification reliability. For example, verification reliability (i.e., the reliability required under test conditions similar to those of type I users) can be calculated based on known user field reliability requirements; or the corresponding user field reliability of type I users can be determined based on verification reliability. This is very helpful in avoiding overtesting and in subsequent vehicle development. The bidirectional conversion process between user field reliability and verification reliability will be described in detail below.

[0037] In some embodiments, converting verification reliability into user field reliability may include four sub-steps: determining the specification lifetime of a class of users based on verification reliability; calculating the specification lifetime of a class of users based on the specification lifetime of the class of users and the damage variation coefficient of a class of users; calculating the reliability of the class of users based on the specification lifetime of the class of users according to the Weibull distribution; and weighting and summing the reliability of the class of users (i.e., verification reliability) and the reliability of the class of users based on the proportion of the class of users to the class of users to obtain the user field reliability for all sample users. Specifically, the specification lifetime η can be calculated using the following formula: (11) Here, 1-F(t) can be regarded as reliability, β is the Weibull shape parameter (slope), and t is a certain base time quantity.

[0038] In some embodiments, based on the test verification reliability R90 requirement, a user field reliability R is required under the operating conditions of a certain type of user (i.e., the user's driving conditions corresponding to the actual verification test conditions). primary =R test =90%, and set the value of β. The specification lifetime η for a class of users (e.g., sample users greater than the 95th percentile) can be calculated using formula (11). primary =a·t, where a represents a constant.

[0039] Next, in the next sub-step, the specification lifetime of the second-class user can be calculated based on the specification lifetime of the first-class user and the damage variation coefficient of the second-class user. Specifically, since specification lifetime is inversely proportional to damage, it can be calculated based on the specification lifetime η of the first-class user. primaryThe coefficient of variation (CVR) of damage for Class II users relative to Class I users is used to calculate the specification life of Class II users.

[0040] As an example, based on the system damage variation coefficients CVR1 and CVR2 of Class II users in the first and second quantile intervals (e.g., the 0–10% quantile interval and the 10–25% quantile interval), the specification lifetime η of Class II users in these intervals can be calculated respectively. secondary1 and η secondary2 The details are as follows: Similarly, the specification lifetime η of Class II users in other quantile intervals... secondary It can also be calculated based on the system variation coefficient of the corresponding interval.

[0041] Next, in the third sub-step, using formula (11) and the specification lifetime of the second-class users, the user reliability R of the second-class users in each interval can be calculated. secondary For the two types of users in the first and second quantile intervals described in the example above, given the reliability R of one type of user... test Given 90% and shape parameter β, the corresponding reliability can be calculated: Similarly, the reliability of Class II users in other quantile intervals can also be calculated. As an example, the impairment intervals, proportions, coefficients of variation, and corresponding reliability of sample users (e.g., Class I and Class II users) in each quantile interval can be listed in the following table: refer to Figure 4 , Figure 4 This is a schematic diagram illustrating the relationship between the coefficient of variation and reliability of users at different quantiles according to embodiments of this application (given the reliability R of a class of users at the 95th quantile). test =90%). It can be seen that the reliability of the second type of users is higher than that of the first type, and this reliability increases as the quantile decreases (the higher the coefficient of variation). It should be understood that, given other different R values... test In the case of different shape parameters β (e.g., 85%, 92%, or 95%) or different form parameters β (e.g., 1, 1.5, or 3), the reliability R of the second type of user is... secondary It can be calculated accordingly.

[0042] Finally, based on the proportion of users in category one and category two (e.g., p, p1, p2...pn in Table 1), the reliability R of users in category one is calculated. primary Reliability R of Type II users secondary1， R secondary2… Rsecondaryn Perform a weighted summation to obtain the user site reliability R. field Specifically, on-site reliability. R field It can be calculated using the following formula: (12) As indicated in Table 1, p represents the percentage of users in category one, p1~pn represent the percentage of users in category two in each interval, and R... primary This refers to the on-site reliability for a certain type of user, R. secondary1 ~R secondaryn The reliability of a certain type of user is R. primary The on-site reliability of the second type of users in each time interval, and R field The reliability of a certain type of user is R primary This refers to the on-site reliability for all users. As mentioned above, since the operating conditions used in the testing and verification process are similar to those of a class of users, the verification reliability R obtained during the testing and verification process is... test It can be regarded as R primary Furthermore, based on the above formula 12, the user field reliability R for all users can be predicted. field Or the on-site reliability R of Class II users in each interval secondary This is very beneficial for the development and iteration of subsequent models.

[0043] On the other hand, contrary to the conversion of verification reliability to field reliability described above, in some embodiments, field reliability can also be converted to verification reliability. Similarly, converting the field reliability to verification reliability includes: calculating the specification lifetime of a class of users based on the damage variation coefficients of the two types of users, the proportion of class I users to class II users, and the field reliability; and calculating the reliability of the class of users based on their specification lifetime using a Weibull distribution, as the verification reliability.

[0044] As shown above, under the Weibull distribution, specification lifetime can be expressed as a function of reliability, i.e., η=f ( R Therefore, reliability can also be determined inversely using the specified lifetime and an inverse function, i.e., R=f -1 ( η Furthermore, the ratio between the specification lifetime of Category II users and that of Category I users can be measured by the coefficient of variation of damage (CVR). Therefore, in formula (12), R primary and R secondary1 ~R secondarynAll can be achieved through the specified lifespan η of a certain type of user. primary The damage variation coefficients of the two types of users in each interval are used to represent the damage. Formula (12) can be rewritten as: (13) Using formula (13), based on the damage variation coefficient (CVR1~CVR) n The percentages of first-class users and second-class users in each interval (p, p1, p2…pn) can be used to assess the user's on-site reliability R. field Calculate the specification lifespan η of a type of user primary After obtaining the specified lifespan η of a certain type of user... primary Then, using the inverse function f -1 ( η The reliability R of a class of users can be calculated. primary , to serve as the verification reliability R test .

[0045] Step 110 establishes a correspondence between various verification reliability levels and user field reliability levels for a class of user operating conditions. In some embodiments, the various reliability levels for a class of users, which serve as verification reliability levels, and the corresponding user field reliability levels can be stored in the form of a lookup table.

[0046] As described above, the reliability of type II users is higher than that of type I users, and it increases as the quantile decreases (the larger the coefficient of variation). Therefore, the field reliability for all users is often higher than that for type I users. Steps 102-110 can be used to obtain the field reliability for each verification reliability, or the verification reliability for each user's field reliability. Since the reliability requirements for verification testing are for all users, the reliability requirements should actually correspond to the field reliability for users. However, the test conditions used often correspond to the driving conditions for type I users. Therefore, if a verification reliability for type I users is met during verification testing, the field reliability corresponding to that verification reliability will also be met for all users under the driving conditions. Conversely, if the requirements for field reliability for users are determined according to the standard, then during verification testing, as long as the verification reliability corresponding to that field reliability (which is usually lower than the field reliability for that user) is met, it can be determined that the field reliability for all sample users can be achieved.

[0047] Reliability R of a class of users primary =R test In 90% of the embodiments, the user field reliability R calculated through step 110 is... fieldIt could be 96.45%. Therefore, when a standard requires the reliability of an electric drive system to reach 96.45% or lower, 90% can be used as the reliability requirement under verification test conditions. If the reliability of the electric drive system reaches above 90% under verification test conditions, it indicates that the reliability of the electric drive system at the user's site under various operating conditions is higher than 96.45%, meaning the verification test passes. In reality, for some verification processes, the sample size for different reliability requirements varies. Therefore, excessive reliability requirements will lead to additional waste in terms of time and cost. Based on the user's site reliability R... field With verification reliability R test This correspondence allows for the selection of more appropriate verification reliability requirements (e.g., the required number of samples), thereby reducing the time and economic costs required for testing and verification.

[0048] In some embodiments, the electric drive system may include multiple subsystems, such as a reducer, a controller, and a motor. For each subsystem, a correspondence between its user field reliability and the reliability of a class of users can be determined using method 100 (e.g., a lookup table). Then, for each subsystem, the reliability of a class of users corresponding to the user field reliability requirement can be selected as the verification test reliability requirement, thereby avoiding over-verification during the verification test process for each subsystem.

[0049] This application establishes a scientifically sound correlation or correspondence between reliability under verification test conditions and user field reliability. This allows testers to conveniently and quickly select accurate verification test reliability based on user field reliability requirements. Furthermore, testers can accurately predict user field reliability for all users or second-tier users based on the verification test reliability achievable during testing, providing valuable reference for development and iteration processes. By avoiding over-verification of user field reliability while meeting user field reliability requirements, it significantly saves verification and testing time and costs. Moreover, compared to verification test conditions that more closely resemble complex real-world conditions, the method in this application is easier to implement and reuse.

[0050] refer to Figure 5 , Figure 5 This is a block diagram of an apparatus 500 for converting between user-field reliability and verified reliability of an electric drive system according to an embodiment of this application. The apparatus 500 includes a processor 520, a memory 510, and a computer program 530, which is stored in the memory 510 and can run on the processor 520. The execution of the computer program 530 causes... Figure 1 The method 100 shown is executed.

[0051] Additionally, as described above, this application can also be implemented as a computer-readable storage medium storing information for causing a computer to perform, such as Figure 1 The instructions for method 100 are shown. Here, various types of computer-readable storage media can be used as computer-readable storage media, such as disks (e.g., magnetic disks, optical disks, etc.), cards (e.g., memory cards, optical cards, etc.), semiconductor memories (e.g., ROM, non-volatile memory, etc.), and tapes (e.g., magnetic tape, cassette tape, etc.). This application can also be implemented as a computer program product comprising instructions that cause a computer to perform... Figure 1 Method 100 is shown.

[0052] Where applicable, the various embodiments provided in this application may be implemented using hardware, software, or a combination of hardware and software. Furthermore, where applicable, without departing from the scope of this application, the various hardware and / or software components described herein may be combined into composite components comprising software, hardware, and / or both. Where applicable, without departing from the scope of this application, the various hardware and / or software components described herein may be divided into sub-components comprising software, hardware, or both. Additionally, where applicable, it is contemplated that software components may be implemented as hardware components, and vice versa.

[0053] The software (such as program code and / or data) according to this application may be stored on one or more computer-readable storage media. It is also contemplated that the software identified herein may be implemented using one or more networked and / or otherwise general-purpose or special-purpose computers and / or computer systems. Where applicable, the order of the various steps described herein may be changed, combined into compound steps, and / or divided into sub-steps to provide the features described herein.

[0054] The embodiments and examples presented herein are provided to best illustrate embodiments of this application and its particular applications, thereby enabling those skilled in the art to implement and use this application. However, those skilled in the art will understand that the above description and examples are provided for ease of illustration and example only. The descriptions presented are not intended to cover all aspects of this application or to limit this application to the precise forms disclosed.

Claims

1. A method for converting between user-site reliability and verified reliability of an electric drive system, characterized in that, The method includes: Based on the driving conditions of the sample users, determine the required motor torque, speed, and corresponding voltage and current for the torque and speed. The mechanical and / or thermal damage to the electric drive systems of all sample users is calculated based on the torque, speed, voltage, and current, respectively. Based on the mechanical and / or thermal damage to the electric drive systems of all sample users, users were divided into Category I and Category II users, and the proportion of Category I and Category II users was determined. Category I users are sample users with driving conditions close to the test conditions, and Category II users are sample users other than Category I users. Based on the mechanical and / or thermal damage of Class I and Class II users, calculate the damage variation coefficient of Class II users relative to Class I users; Based on the damage variation coefficient and the proportion of Class I and Class II users, one of the user field reliability and the verification reliability is converted into the other. The user field reliability is the reliability of the user in actual use, and the verification reliability is the reliability of verification testing under test conditions.

2. The method as described in claim 1, wherein, The mechanical damage of the electric drive system includes bending fatigue, contact fatigue, and impact fatigue; and the thermal damage of the electric drive system includes stator thermal aging, thin film capacitor thermal aging, and power module thermal fatigue.

3. The method as described in claim 2, wherein, The damage variation coefficient is the damage ratio between type I users and type II users; and wherein the damage variation coefficient includes mechanical damage variation coefficient, thermal damage variation coefficient and system variation coefficient, and the value of the system variation coefficient is equal to the minimum value of the mechanical damage variation coefficient and the thermal damage variation coefficient.

4. The method of claim 3, wherein, The value of the mechanical damage coefficient of variation is equal to the minimum value among the bending fatigue coefficient of variation, the contact fatigue coefficient of variation, and the impact fatigue coefficient of variation; and the value of the thermal damage coefficient of variation is equal to the minimum value among the stator thermal aging coefficient of variation, the thin film capacitor thermal aging coefficient of variation, and the power module thermal fatigue coefficient of variation.

5. The method of claim 1, wherein, The sample users were selected based on user region, user activation time, and user annual mileage.

6. The method of claim 1, wherein, Converting the verification reliability into the user's on-site reliability includes: Based on the verification reliability, the specification lifespan of a certain type of user is determined; The specification life of the second type of user is calculated based on the specification life of the first type of user and the damage variation coefficient of the second type of user. Based on the specification lifetime of the second-class users, the reliability of the second-class users is calculated according to the Weibull distribution; and Based on the proportion of users in category 1 and category 2, the reliability of users in category 1 and category 2 is weighted and summed to obtain the on-site reliability of the user.

7. The method of claim 1, wherein, Converting the user's on-site reliability into the verification reliability includes: Based on the damage variation coefficient of the second type of users, the proportion of the first type of users to the second type of users, and the on-site reliability of the users, the specification life of the first type of users is calculated. Based on the specification lifetime of a class of users, the reliability of a class of users is calculated according to the Weibull distribution, and used as the verification reliability.

8. The method of claim 1, wherein, The on-site reliability of each user and the verification reliability corresponding to each on-site reliability of the user are stored in the form of a lookup table.

9. A device for converting between user-site reliability and verified reliability of an electric drive system, characterized in that, The apparatus includes: a memory; a processor; and a computer program stored in the memory and executable by the processor, the execution of which causes the method as described in any one of claims 1-8 to be performed.

10. A computer-readable storage medium, characterized in that, The computer-readable storage medium includes instructions that, when executed, perform the method as described in any one of claims 1-8.

Images