A mechanical parameter identification method, system, device and storage medium of a motor

Abstract

The application discloses a mechanical parameter identification method, system and device of a motor and a storage medium, and applies to the technical field of motor control. The method comprises the following steps: determining real-time motor operation data at a current sampling moment; the real-time motor operation data comprises motor acceleration; determining a target storage space for storing the real-time motor operation data from each candidate storage space according to the motor acceleration; the capacities of the candidate storage spaces are different, and the capacity of the target storage space is positively correlated with the absolute value of the motor acceleration; storing the real-time motor operation data into the target storage space; and performing mechanical parameter identification of the motor based on the motor operation data stored in the target storage space and the non-target storage space. The application guarantees the timeliness and accuracy of the mechanical parameter identification of the motor, and can clearly determine the maximum required storage space in advance.

Description

Technical Field

[0001] This invention relates to the field of motor control technology, and in particular to a method, system, device, and storage medium for identifying the mechanical parameters of a motor. Background Technology

[0002] In motor control systems, moment of inertia, load torque, viscous friction coefficient, and Coulomb friction are all important mechanical parameters. In the vector control algorithms of servo motors or frequency converters, if mechanical parameters that are incompatible with the current system are set, vibrations or low response may occur, affecting positioning time and accuracy, causing speed fluctuations, and significantly impacting the production environment. Therefore, real-time identification of mechanical parameters is necessary in situations such as new equipment installation, variable mechanical inertia, variable load torque, and frequent forward and reverse rotation of speed.

[0003] In traditional techniques, conventional least squares or recursive least squares methods are typically used for mechanical parameter identification. However, conventional least squares requires accumulating a large amount of data before a single calculation can be performed, making real-time mechanical parameter identification impossible. While recursive least squares can achieve real-time calculation, the selection of parameters has a significant impact on the algorithm, resulting in lower accuracy in mechanical parameter identification.

[0004] In summary, how to effectively identify the mechanical parameters of motors and improve their accuracy is a technical problem that urgently needs to be solved by those skilled in the art. Summary of the Invention

[0005] The purpose of this invention is to provide a method, system, device, and storage medium for identifying the mechanical parameters of an electric motor, so as to effectively identify the mechanical parameters of the motor and improve accuracy.

[0006] To solve the above-mentioned technical problems, the present invention provides the following technical solution:

[0007] In a first aspect, the present invention provides a method for identifying the mechanical parameters of an electric motor, comprising:

[0008] Determine the real-time motor operating data at the current sampling moment; the real-time motor operating data includes motor acceleration;

[0009] Based on the motor acceleration, a target storage space for storing the real-time motor operation data is determined from each candidate storage space; the capacity of each candidate storage space is different, and the capacity of the target storage space is positively correlated with the absolute value of the motor acceleration;

[0010] The real-time motor operation data is stored in the target storage space, and the mechanical parameters of the motor are identified based on the motor operation data stored in the target storage space and the non-target storage space respectively.

[0011] In one implementation, determining the target storage space for storing the real-time motor operation data from candidate storage spaces based on the motor acceleration includes:

[0012] Determine the target acceleration range to which the motor acceleration belongs; the target acceleration range is any one of a plurality of preset acceleration ranges;

[0013] From each of the candidate storage spaces, determine the target storage space corresponding to the target acceleration interval;

[0014] Each candidate storage space corresponds to a preset acceleration interval, and the capacity of the candidate storage space is positively correlated with the absolute value of the median value of the corresponding preset acceleration interval.

[0015] In one implementation, the motor operating data includes motor speed; the step of identifying the motor's mechanical parameters based on the motor operating data stored in the target storage space and the non-target storage space includes:

[0016] Based on the motor speed, first operating data and second operating data are determined from the motor operating data stored in the target storage space and the non-target storage space, respectively; the motor speed in the first operating data is a positive number, and the motor speed in the second operating data is a negative number;

[0017] Based on the first operating data, the positive mechanical parameters are calculated;

[0018] Based on the second operating data, the negative mechanical parameters are calculated;

[0019] Based on the positive mechanical parameters and the negative mechanical parameters, the target mechanical parameters of the motor are determined.

[0020] In one implementation, the first operating data includes multiple operating parameters; the step of calculating the positive mechanical parameters based on the first operating data includes:

[0021] For each running parameter in the first running data, statistics are performed to obtain the statistical value corresponding to each running parameter;

[0022] Based on the statistical values ​​corresponding to each of the aforementioned operating parameters, the positive mechanical parameters are calculated.

[0023] In one embodiment, the positive mechanical parameters include positive moment of inertia, positive disturbance torque, and positive viscous friction coefficient; the operating parameters include motor acceleration ω′ and motor torque T. e Motor speed ω, ω′T e,ω′ω,T e ω, ω′ 2 and ω 2 The positive mechanical parameters are calculated based on the statistical values ​​of each of the aforementioned operating parameters, including:

[0024] Substitute the statistical values ​​of each of the aforementioned operating parameters into the preset identification formula group to calculate the positive disturbance torque Tp, positive rotational inertia Jp, and positive viscous friction coefficient Bp.

[0025] The preset identification formula group includes:

[0026]

[0027] a + b represents the statistical value corresponding to ω′. + T represents e The corresponding statistical value, c + d represents the statistical value corresponding to ω. + Represents ω′T e The corresponding statistical value, e + f represents the statistical value corresponding to ω′ω. + T represents e The statistical value corresponding to ω, g + Represents ω′ 2 The corresponding statistical value, h + Represents ω 2 The corresponding statistical value; M represents the total number of first and second run data.

[0028] In one implementation, storing the real-time motor operation data in the target storage space, and identifying the motor's mechanical parameters based on the motor operation data stored in the target storage space and the non-target storage space respectively, includes:

[0029] After storing the real-time motor operation data in the target storage space, determine whether the preset identification conditions are met based on the motor operation data stored in the target storage space and the non-target storage space respectively.

[0030] If so, the mechanical parameters of the motor are identified based on the motor operation data stored in the target storage space and the non-target storage space, respectively.

[0031] In one implementation, the preset identification conditions include:

[0032] The total amount of motor operation data stored in the target storage space and the non-target storage space is greater than the first threshold, and the difference between the largest and smallest space number in the space numbers corresponding to the storage spaces storing motor operation data exceeds the second threshold.

[0033] The N storage spaces are numbered in order from 1 to N.

[0034] Secondly, the present invention provides a mechanical parameter identification system for an electric motor, comprising:

[0035] The data sampling module is used to determine the real-time motor operation data at the current sampling moment; the real-time motor operation data includes motor acceleration.

[0036] A data classification module is used to determine a target storage space for storing the real-time motor operation data from each candidate storage space based on the motor acceleration; the capacity of each candidate storage space is different, and the capacity of the target storage space is positively correlated with the absolute value of the motor acceleration;

[0037] The identification module is used to store the real-time motor operation data in the target storage space, and to identify the mechanical parameters of the motor based on the motor operation data stored in the target storage space and the non-target storage space respectively.

[0038] Thirdly, the present invention provides a device for identifying the mechanical parameters of an electric motor, comprising:

[0039] Memory, used to store computer programs;

[0040] A processor for executing the computer program to implement the steps of the method for identifying the mechanical parameters of an electric motor as described above.

[0041] Fourthly, the present invention provides a computer-readable storage medium storing a computer program, which, when executed by a processor, implements the steps of the above-described method for identifying the mechanical parameters of an electric motor.

[0042] By applying the technical solution provided in this invention, whenever a sampling time is reached, the real-time motor operation data for that sampling time is determined. This real-time motor operation data includes motor acceleration. Therefore, based on the magnitude of the motor acceleration in the real-time motor operation data at the current sampling time, a target storage space for storing the real-time motor operation data can be determined from each candidate storage space. In other words, for real-time motor operation data at a given sampling time, the real-time motor operation data is placed into the corresponding target storage space for storage based on the magnitude of the motor acceleration. Then, based on the motor operation data stored in both the target and non-target storage spaces, the mechanical parameters of the motor can be identified. This means that each time new real-time motor operation data is obtained, this application can perform one instance of motor mechanical parameter identification, thus ensuring the timeliness of motor mechanical parameter identification. Furthermore, since the larger the absolute value of the motor acceleration, the more accurate the real-time motor operation data obtained, by setting different capacities for each candidate storage space, and with the target storage space capacity being positively correlated with the motor acceleration, the larger the capacity of the storage space corresponds to the larger the absolute value of the motor acceleration stored. The larger the capacity, the more data can be stored. Thus, among all the motor operation data used for motor mechanical parameter identification, there will be more motor operation data with larger absolute values ​​of acceleration and fewer motor operation data with smaller absolute values ​​of acceleration, which helps to ensure the accuracy of the identification results.

[0043] In summary, the solution presented in this application achieves the identification of the mechanical parameters of the motor and ensures the real-time performance and accuracy of the identification. Furthermore, since the capacity of each candidate storage space is preset, the solution presented in this application can predetermine the maximum required storage space. Attached Figure Description

[0044] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0045] Figure 1 This is a flowchart illustrating the implementation of a method for identifying the mechanical parameters of a motor according to one embodiment of the present invention.

[0046] Figure 2 This is a schematic diagram illustrating the division of acceleration intervals and the corresponding differences in storage space capacity in one specific embodiment of the present invention;

[0047] Figure 3 This is a schematic diagram of a storage method in a specific embodiment of the present invention;

[0048] Figure 4 This is a schematic diagram of the identification result in a specific embodiment of the present invention;

[0049] Figure 5 This is a schematic diagram of the mechanical parameter identification system for an electric motor according to one embodiment of the present invention. Detailed Implementation

[0050] The core of this invention is to provide a method for identifying the mechanical parameters of an electric motor, which realizes the identification of the mechanical parameters of the motor and ensures the timeliness and accuracy of the identification.

[0051] To enable those skilled in the art to better understand the present invention, the invention will be further described in detail below with reference to the accompanying drawings and specific embodiments. Obviously, the described embodiments are merely some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0052] Please refer to Figure 1 , Figure 1 This is a flowchart illustrating an implementation method for identifying the mechanical parameters of a motor according to one embodiment of the present invention. The method may include the following steps:

[0053] Step S101: Determine the real-time motor operation data at the current sampling time; the real-time motor operation data includes the motor acceleration.

[0054] Specifically, in practical applications, sampling can be performed periodically. As will be described later, each sampling allows for one identification of the motor's mechanical parameters. Therefore, the sampling period can be set based on the identification requirements, and is typically set to a short period to ensure high real-time performance of the mechanical parameter identification in this application. Of course, in other specific embodiments, other sampling triggering methods can be set as needed without affecting the implementation of this invention.

[0055] When a sampling moment arrives, it is necessary to determine the real-time motor operating data at that sampling moment. This real-time motor operating data typically includes multiple operating parameters. Of course, the specific operating parameters can be set and adjusted as needed, as long as the mechanical parameters of the motor can be identified subsequently based on the motor operating data from multiple sampling moments. For example, in one implementation, the real-time motor operating data at a sampling moment specifically includes the motor speed ω and the motor torque T at that sampling moment. eAnd the motor acceleration ω′. In another implementation described later, the motor operating data at a sampling moment specifically includes eight operating parameters to improve the ease of calculation when identifying mechanical parameters.

[0056] When a sampling moment arrives, real-time motor operating data at that moment can be obtained. Since real-time motor operating data typically includes multiple operating parameters, for ease of description, the real-time motor operating data at that sampling moment is referred to as one data set, meaning that one data set can be obtained at each sampling moment. The operating parameters in this data set can be obtained entirely through parameter sampling, or a portion of the operating parameters can be obtained through parameter sampling, with the remaining operating parameters calculated. Neither approach affects the implementation of the invention. The specific method for obtaining each operating parameter can be set according to the specific operating parameters included in the real-time motor operating data in the actual application.

[0057] In one specific embodiment of the present invention, step S101 may include the following two steps:

[0058] Step 1: Determine the motor speed ω and motor torque T at the current sampling time. e And the motor acceleration ω′;

[0059] Step 2: Based on the motor speed ω and motor torque T at the current sampling time e And the motor acceleration ω′, which will include ω′, T e ,ω,ω′T e ,ω′ω,T e ω, ω′ 2 and ω 2 The data set serves as the real-time motor operation data for the current sampling time.

[0060] In this implementation, for the current sampling time, the motor speed ω and motor torque T at that sampling time are first determined. e And the motor acceleration ω′. In practical applications, the motor speed ω and the motor torque T e Typically, data can be collected using sensors. The motor acceleration ω′ can be calculated from the motor speed ω at adjacent sampling times, or it can be directly collected using relevant sensors. Neither method affects the implementation of this invention. Of course, a more common implementation method is to use sensors to measure the motor speed ω and motor torque T. e The data is collected, and the motor acceleration ω′ is calculated. Furthermore, it is understood that the scheme in this application requires the motor speed ω and motor torque T at the same sampling time. e In addition to the motor acceleration ω′, data synchronization is usually performed when sampling data in practical applications.

[0061] In this implementation, one data set needs to include eight operating parameters. That is, at a single sampling moment, the real-time motor operating data at that sampling moment includes eight operating parameters, in addition to the motor speed ω and the motor torque T. e In addition to the three operating parameters of motor acceleration ω′, there are five other operating parameters ω′T. e ,ω′ω,T e ω, ω′ 2 and ω 2 All of these can be obtained based on these three operating parameters.

[0062] As can be seen, when step S101 is executed according to the requirements of this implementation method, real-time motor operation data can be obtained at each sampling time, and the real-time motor operation data at each sampling time is a data group containing 8 operation parameters. When this implementation method is adopted, based on these 8 operation parameters in the real-time motor operation data at each sampling time, the mechanical parameters of the motor can be identified conveniently and effectively.

[0063] Step S102: Based on the motor acceleration, determine the target storage space for storing real-time motor operation data from each candidate storage space; the capacity of each candidate storage space is different, and the capacity of the target storage space is positively correlated with the absolute value of the motor acceleration.

[0064] After determining the real-time motor operation data at the current sampling time, it is necessary to store the real-time motor operation data at the current sampling time. In the scheme of this application, N different storage spaces are set up. Therefore, it is necessary to determine the target storage space for storing the real-time motor operation data from each candidate storage space.

[0065] The capacity of each candidate storage space is different, and the capacity of the determined target storage space is positively correlated with the absolute value of the motor acceleration. In other words, in the scheme of this application, the motor acceleration is classified according to the absolute value of the motor acceleration of the real-time motor operation data at the current sampling time, and then the selection of which candidate storage space to use as the target storage space to store the real-time motor operation data is determined.

[0066] In practical applications, there can be various ways to implement step S102. The key is to ensure that the capacity of the selected target storage space is positively correlated with the absolute value of the motor acceleration. For example, in one specific embodiment of the present invention, considering that determining which acceleration interval the absolute value of the motor acceleration belongs to allows for the selection of corresponding candidate storage spaces, step S102 can specifically include:

[0067] Determine the target acceleration range to which the motor acceleration belongs; the target acceleration range can be any one of multiple preset acceleration ranges.

[0068] From each candidate storage space, determine the target storage space corresponding to the target acceleration interval;

[0069] Each candidate storage space corresponds to a preset acceleration interval, and the capacity of the candidate storage space is positively correlated with the absolute value of the median value of the corresponding preset acceleration interval.

[0070] In this implementation, multiple acceleration intervals are preset, which are conveniently referred to as the preset N acceleration intervals. The specific range of each of the N acceleration intervals can be set and adjusted according to actual needs. In practical applications, to facilitate interval division, the interval length of each acceleration interval is usually set to be the same. For example, in a specific embodiment of the present invention, the preset N acceleration intervals are N acceleration intervals that are pre-divided evenly within the range of a_min to a_max. Here, a_min is the preset minimum acceleration threshold, and a_max is the preset maximum acceleration threshold. Both a_min and a_max can be pre-set based on the experience of the personnel or experimental data. That is to say, in this example, for the range of a_min to a_max, according to the principle of even division, N consecutive acceleration intervals can be obtained. For easier understanding, please refer to [reference needed]. Figure 2 , Figure 2 In the example, the range from a_min to a_max is divided into 8 acceleration intervals, which are labeled as the 1st acceleration interval to the 8th acceleration interval.

[0071] For the current sampling moment, it is necessary to determine the target acceleration interval to which the motor acceleration belongs based on the motor acceleration in the real-time motor operation data. It is understandable that, since the value of motor acceleration varies under different conditions, the target acceleration interval can be any one of N preset acceleration intervals. Figure 2 For example, at the current sampling time, the target acceleration interval to which the motor acceleration belongs may be any one of the acceleration intervals from the 1st acceleration interval to the 8th acceleration interval.

[0072] The acceleration interval to which the motor acceleration belongs is called the target acceleration interval. Once the target acceleration interval is determined, the target storage space corresponding to the target acceleration interval can be determined from the candidate storage spaces. Then, the real-time motor operation data at the current sampling moment can be stored in the target storage space. For example, in one scenario... Figure 2For example, if the motor acceleration in the real-time motor operation data at a certain sampling time belongs to the first acceleration interval, then the real-time motor operation data at that sampling time needs to be stored in the target storage space corresponding to the first acceleration interval. Similarly, if the motor acceleration in the motor operation data at a certain sampling time belongs to the fifth acceleration interval, then the motor operation data at that sampling time needs to be stored in the target storage space corresponding to the fifth acceleration interval.

[0073] For example Figure 3 In the example, R(i) refers to the target storage space corresponding to the i-th acceleration interval. It can be seen that... Figure 3 In the example, the motor operation data at a single sampling moment specifically includes ω′, T e ,ω,ω′T e ,ω′ω,T e ω, ω′ 2 and ω 2 These 8 operating parameters, Figure 3 The subscripts of the various operating parameters in the table represent the sampling time, such as ω. k ' represents the motor acceleration ω' at the k-th sampling time, and Te... k+2 This represents the motor torque at the (k+2)th sampling time.

[0074] In this application's scheme, there are N preset acceleration intervals, where N is a positive integer not less than 2. Each candidate storage space corresponds to one preset acceleration interval. The capacity of a candidate storage space is positively correlated with the absolute value of the median value of its corresponding preset acceleration interval. In other words, among the N acceleration intervals, the larger the absolute value of the median value of the acceleration interval, the larger the capacity of the candidate storage space corresponding to that acceleration interval. In short, the capacity of a candidate storage space is positively correlated with the absolute value of the median value of the acceleration interval corresponding to that storage space.

[0075] It is understandable that a larger storage capacity means that more data sets can be stored in that storage space, i.e., more real-time motor operation data at each sampling time. Since subsequent steps are based on all the motor operation data in each storage space to identify the motor's mechanical parameters, it means that a larger storage capacity has a greater impact on the identification results. Larger storage spaces store real-time motor operation data with larger absolute acceleration values. The larger the absolute acceleration value, the higher the accuracy of the set of real-time motor operation data. Therefore, this setting is beneficial to improving the identification accuracy of the solution in this application.

[0076] exist Figure 2In the example, the number of squares represents the capacity of the candidate storage space corresponding to different acceleration intervals. It can be seen that the fourth acceleration interval is one of the two acceleration intervals close to 0, therefore its corresponding candidate storage space capacity is relatively low. Figure 2 The capacity of the corresponding storage space is represented by two squares, and the same applies to the 5th acceleration interval as the 4th acceleration interval. From the 6th to the 8th acceleration interval, since the absolute value of the midpoint of the acceleration interval gradually increases, the capacity of the candidate storage space corresponding to these three acceleration intervals will also gradually increase. Figure 2 The diagram shows an increasing number of squares. Similarly, from the third acceleration interval to the first acceleration interval, as the absolute value of the midpoint of the acceleration interval gradually increases, the capacity of the candidate storage space corresponding to these three acceleration intervals will also gradually increase.

[0077] It should also be noted that since the capacity of each candidate storage space is preset, in actual operation, as motor operation data is continuously generated, any candidate storage space may become full. Therefore, in one implementation, each storage space can adopt a circular storage method, which is also a commonly used solution in practical applications. For example, if a candidate storage space can store 1000 data sets, as motor operation data is continuously generated, real-time motor operation data will be allocated to this candidate storage space for storage. The storage address will continuously move towards the end of the storage space until the storage space has stored 1000 data sets. If more data sets need to be stored in the candidate storage space later, the storage address will return to the beginning of the candidate storage space, so that the new data sets overwrite the old data sets. It can be understood that in this example, after this point, in subsequent processes, the candidate storage space will always store the most recent 1000 data sets. Figure 3 In the example, a circular storage method is also used. Figure 3 The sliding shown in the diagram indicates that the storage address will continuously move from the head to the tail of the storage space and then return to the head, thus continuously cycling.

[0078] In addition to the circular storage method mentioned above, other methods can be set as needed to address the situation where the storage space is full. For example, in one scenario, whenever the space of a candidate storage space is full, all data in that candidate storage space is directly cleared.

[0079] Step S103: Store the real-time motor operation data in the target storage space, and identify the mechanical parameters of the motor based on the motor operation data stored in the target storage space and the non-target storage space respectively.

[0080] In the scheme of this application, the mechanical parameters of the motor can be identified once whenever the data in any storage space is updated, so as to ensure the timeliness of identification. The specific identification method can be set and adjusted as needed.

[0081] As described above, based on the motor acceleration, the target storage space for storing real-time motor operation data can be determined from the candidate storage spaces. Then, the real-time motor operation data can be stored in the target storage space. At this point, the mechanical parameters of the motor can be identified once. During the identification, all data groups in all storage spaces are required. That is, the mechanical parameters of the motor are identified based on the motor operation data stored in the target storage space and the non-target storage space. It can be understood that the non-target storage space described here refers to the other storage spaces in the candidate storage spaces besides the target storage space.

[0082] In one specific embodiment of the present invention, step S103 may specifically include:

[0083] The first step: Based on the motor speed, determine the first running data and the second running data from the motor running data stored in the target storage space and the non-target storage space respectively; the motor speed in the first running data is a positive number, and the motor speed in the second running data is a negative number;

[0084] The second step: Based on the first operating data, calculate the positive mechanical parameters;

[0085] The third step: Based on the second operating data, calculate the negative mechanical parameters;

[0086] The fourth step: Based on the positive and negative mechanical parameters, determine the target mechanical parameters of the motor.

[0087] This implementation takes into account that when identifying the mechanical parameters of the motor, the motor operation data stored in the target storage space and the non-target storage space can be divided into two categories according to the different motor speeds, namely the first operation data and the second operation data.

[0088] The motor speed in the first set of running data is positive, and the motor speed in the second set of running data is negative. That is, for any set of motor running data in the entire storage space, if the motor speed in that set of running data is positive, that set of running data belongs to the first set of running data; conversely, if the motor speed in that set of running data is negative, that set of running data belongs to the second set of running data. Additionally, in a small number of cases, the motor speed in the running data is exactly 0. In such cases, the set of running data can be considered to belong to either the first or the second set of running data; for example, it is usually set to belong to the first set of running data.

[0089] Based on the first set of operating data, the positive mechanical parameters can be calculated. Based on the second set of operating data, the negative mechanical parameters can be calculated. Finally, based on the positive and negative mechanical parameters, the target mechanical parameters of the motor can be determined. The specific contents of the positive and negative mechanical parameters can be set and adjusted according to the actual situation, so as to effectively determine the target mechanical parameters of the motor and realize the identification of the motor's mechanical parameters.

[0090] Furthermore, the specific content of the target mechanical parameters of the motor can be set and adjusted according to actual needs. For example, in one specific embodiment of the present invention, considering that the moment of inertia, viscous friction coefficient, load torque, and Coulomb friction are all important mechanical parameters of the motor, one or more of these four mechanical parameters can be identified according to actual needs. For example Figure 4 This diagram illustrates the identification results in one specific embodiment. From top to bottom, it shows the identification results for the motor's moment of inertia, viscous friction coefficient, load torque, and Coulomb friction. Therefore, in practical applications, the determined target mechanical parameters of the motor can be one or more of the motor's moment of inertia, viscous friction coefficient, load torque, and Coulomb friction.

[0091] In one specific embodiment of the present invention, the first operating data may include multiple operating parameters; the second step described above may specifically include:

[0092] Statistical analysis was performed on each running parameter in the first running data to obtain the statistical value corresponding to each running parameter;

[0093] Based on the statistical values ​​corresponding to each operating parameter, the positive mechanical parameters are calculated.

[0094] This implementation takes into account that some traditional solutions require extensive matrix operations when identifying the mechanical parameters of a motor, consuming significant computational resources and hindering real-time identification. Therefore, this application addresses this issue by calculating the positive mechanical parameters by statistically analyzing each operating parameter in the first operating data to obtain corresponding statistical values, and then calculating the positive mechanical parameters based on these values. This simplifies both the statistical analysis of operating parameters and the calculation of positive mechanical parameters from these statistical values, reducing computational resource requirements and facilitating implementation. Similarly, when calculating the negative mechanical parameters based on the second operating data, statistical analysis can also be performed on each operating parameter in the second operating data to obtain corresponding statistical values, which can then be used to conveniently calculate the negative mechanical parameters.

[0095] Taking the positive mechanical parameters as an example, in one specific embodiment of the present invention, the positive mechanical parameters specifically include the positive moment of inertia Jp, the positive disturbance torque Tp, and the positive viscous friction coefficient Bp; the operating parameters include the motor acceleration ω′ and the motor torque T. e Motor speed ω, ω′T e ,ω′ω,T e ω, ω′ 2 and ω 2 Based on the statistical values ​​of various operating parameters, the positive mechanical parameters are calculated, including:

[0096] Substitute the statistical values ​​of each operating parameter into the preset identification formula group to calculate the positive disturbance torque Tp, positive rotational inertia Jp, and positive viscous friction coefficient Bp.

[0097] The preset recognition formula group includes:

[0098]

[0099] a + b represents the statistical value corresponding to ω′. + T represents e The corresponding statistical value, c + d represents the statistical value corresponding to ω. + Represents ω′T e The corresponding statistical value, e + f represents the statistical value corresponding to ω′ω. + T represents e The statistical value corresponding to ω, g + Represents ω′ 2 The corresponding statistical value, h + Represents ω 2The corresponding statistical value; M represents the total number of first and second run data.

[0100] In this implementation, for each data set in the first operating data, including motor acceleration ω′ and motor torque T e Motor speed ω, ω′T e ,ω′ω,T e ω, ω′ 2 and ω 2 These eight parameters are obtained by summing the motor acceleration ω′ of each data group in the first running data, and the result is the statistical value a corresponding to ω′. + Similarly, all T values ​​in the first running data will be... e Summing them up yields T. e The corresponding statistical value b + Sum all the ω values ​​in the first run of data to obtain the statistical value c corresponding to ω. + Take all ω′T from the first running data. e Summing these values ​​gives us ω′T. e The corresponding statistical value d + Summing all ω′ω in the first set of data yields the statistical value e corresponding to ω′ω. + Take all T values ​​from the first run data. e Summing ω together yields T. e The statistical value f corresponding to ω + Take all ω′ from the first running data. 2 Summing them together gives us ω′ 2 The corresponding statistical value g + All ω in the first running data 2 Summing them up gives us ω. 2 The corresponding statistical value h + .

[0101] As can be seen, in this implementation method, when calculating the positive disturbance torque Tp, positive rotational inertia Jp, and positive viscous friction coefficient Bp, the preset identification formula set used is scalar multiplication and addition, without involving any matrix multiplication, division, inversion, or other operations. Therefore, it is beneficial to reduce the computation time of related programs and chips, and to achieve real-time identification of mechanical parameters.

[0102] Based on the same principle, statistics can be performed on each operating parameter in the second operating data to obtain the statistical value corresponding to each operating parameter. Then, based on the statistical value corresponding to each operating parameter, the negative mechanical parameters can be calculated. The negative mechanical parameters may include negative rotational inertia, negative disturbance torque, and negative viscous friction coefficient.

[0103] Operating parameters include motor acceleration ω′ and motor torque T. e Motor speed ω, ω′T e ,ω′ω,T e ω, ω′ 2 and ω 2 Based on the statistical values ​​of various operating parameters, the negative mechanical parameters calculated may include:

[0104] After statistical analysis of each operating parameter in the second operating data and obtaining the statistical value corresponding to each operating parameter, the statistical value of each operating parameter is substituted into the negative identification formula group to calculate the negative disturbance torque Tn, negative rotational inertia Jn, and negative viscous friction coefficient Bn.

[0105] The negative identification formula set includes:

[0106]

[0107] Summing all ω′ in the second set of data yields the negative statistical value a corresponding to ω′. - Take all T values ​​from the second running data. e Summing them up yields T. e The corresponding negative statistic b - Summing all the ω values ​​in the second set of data yields the negative statistical value c corresponding to ω. - Take all ω′T from the second running data. e Summing these values ​​gives us ω′T. e The corresponding negative statistic d - Summing all ω′ω in the second set of data yields the negative statistical value e corresponding to ω′ω. - Take all T values ​​from the second running data. e Summing ω together yields T. e The negative statistic f corresponding to ω - Take all ω′ from the second running data. 2 Summing them together gives us ω′ 2 The corresponding negative statistic g - Take all ω from the second running data. 2 Summing them up gives us ω. 2 The corresponding negative statistic h - .

[0108] As can be seen, in this implementation method, when calculating the negative disturbance torque Tn, negative rotational inertia Jn, and negative viscous friction coefficient Bn, the negative identification formula set used is also scalar multiplication and addition, without involving any matrix multiplication, division, inversion, or other operations. Therefore, it is beneficial to reduce the computation time of related programs and chips, and to achieve real-time identification of mechanical parameters.

[0109] Furthermore, it should be noted that in this implementation method, determining the target mechanical parameters of the motor based on positive and negative mechanical parameters can be expressed as follows: Calculations were performed to obtain the motor's moment of inertia J, viscous friction coefficient B, load torque TL, and Coulomb friction force Tc.

[0110] Specifically, in the above implementation, the mechanical motion equation of the motor can be expressed as Te = Jω′ + Bω + TL + Tc; where Te is the electromagnetic torque of the motor, J is the moment of inertia of the motor, B is the coefficient of viscous friction, TL is the load torque, Tc is the Coulomb friction force, ω is the motor speed, and ω′ is the motor acceleration. Furthermore, Tc = sign(ω). That is, when the motor speed ω is greater than or equal to 0, Te = Jω′ + Bω + Tp, where Tp = TL + Tc, representing the positive disturbance torque. Correspondingly, when the motor speed ω is less than 0, Te = Jω′ + Bω + Tn, where Tn = TL - Tc, representing the negative disturbance torque.

[0111] Taking a motor speed ω greater than or equal to 0 as an example, to identify the positive moment of inertia Jp, positive viscous friction coefficient Bp, and positive disturbance torque Tp, an objective function H can be set and the problem can be transformed into finding the minimum value of the objective function H. Finding the minimum value of the objective function H can be transformed into finding the partial derivative of the function. Furthermore, since the scheme of this application divides the data into N acceleration levels, i.e., N acceleration intervals (i = 1 to N), for any sampling time, the motor operation data at that sampling time is stored in the storage space corresponding to the acceleration interval to which the motor acceleration belongs in the motor operation data. Therefore, based on the above analysis, we can finally conclude that: for each data group stored in any current storage space, the data group with a motor speed ω greater than or equal to 0 is called the first type of data group, denoted by a. + to h + T represents all the data in the first type of data set in sequence. e The sum of ω, the sum of ω′T e The sum of ω′ω, T e The sum of ω, ω′ 2 The sum, ω 2 The sum of these equations can be obtained by solving the above equations simultaneously:

[0112]

[0113] Following the same principle, when the motor speed ω is less than 0, we can obtain:

[0114] Thus, we obtain the negative disturbance torque Tn, the negative moment of inertia Jn, and the negative viscous friction coefficient Bn.

[0115] Ultimately, through The calculations can then be used to identify the motor's moment of inertia J, viscous friction coefficient B, load torque TL, and Coulomb friction force Tc.

[0116] In one specific embodiment of the present invention, real-time motor operation data is stored in a target storage space, and the mechanical parameters of the motor are identified based on the motor operation data stored in the target storage space and the non-target storage space, respectively. This may specifically include:

[0117] After storing the real-time motor operation data in the target storage space, determine whether the preset identification conditions are met based on the motor operation data stored in the target storage space and the non-target storage space respectively.

[0118] If so, the mechanical parameters of the motor are identified based on the motor operation data stored in the target storage space and the non-target storage space respectively.

[0119] If not, the identification of the motor's mechanical parameters can be cancelled.

[0120] As described above, mechanical parameter identification can be performed once whenever data in any storage space is updated. This implementation takes into account that in some cases, such as when the total amount of data in each storage space is small, it is not conducive to ensuring the accuracy of the identification results. Therefore, in this implementation, after storing the real-time motor operation data to the target storage space, it determines whether the preset identification conditions are met based on the motor operation data stored in the target storage space and the non-target storage space. If the conditions are met, subsequent operations can be performed normally, that is, the mechanical parameters of the motor are identified based on the motor operation data stored in the target storage space and the non-target storage space. Otherwise, if the conditions are not met, the mechanical parameter identification can be canceled.

[0121] The specific content of the preset identification conditions can be set and adjusted according to actual needs. However, it is understandable that when the set identification conditions are met, the mechanical parameters of the motor should be accurately and effectively identified based on the current situation. Conversely, when the set identification conditions are not met, it means that the accuracy and reliability of the identification cannot be guaranteed based on the current situation.

[0122] For example, in one specific embodiment of the present invention, the preset identification conditions may specifically include:

[0123] The total amount of motor operation data stored in the current target storage space and non-target storage space is greater than the first threshold, and the difference between the largest and smallest space number in the space numbers corresponding to the storage spaces storing motor operation data is greater than the second threshold.

[0124] The N storage spaces are numbered in order from 1 to N.

[0125] In this implementation, the total number of motor operation data stored in the current target storage space and non-target storage space is required to be greater than a first threshold. That is, the total number of data groups stored in all current storage spaces is required to exceed the first threshold. If the total number M of data groups stored in all current storage spaces is low, it is not conducive to accurately and effectively identifying the mechanical parameters of the motor. Therefore, the identification of the mechanical parameters of the motor can be cancelled. As the motor runs, the total number of data groups stored in all storage spaces will continue to increase, which will make the identification condition meet the requirements.

[0126] This implementation also requires that, among the space numbers corresponding to the storage spaces containing data groups, the difference between the largest and smallest space numbers is greater than a second threshold. Since the N storage spaces are numbered sequentially from 1 to N, and the size of the space number is positively correlated with the median value of the acceleration interval corresponding to the storage space, for ease of understanding, the above explanation is still applicable. Figure 2 To illustrate, consider this example: at the current moment, only the storage space corresponding to the 6th acceleration interval contains several data sets, while the storage spaces corresponding to all other acceleration intervals are empty. In this example, the difference between the maximum and minimum numbers is 0. For instance, if the second threshold is 2, since 0 does not exceed 2, this identification condition is not met, and the identification of the motor's mechanical parameters can be cancelled. Similarly, as the motor continues to run, data sets will be successively stored in each storage space, thus ensuring that the identification condition is met.

[0127] For example, in one scenario, at the current moment, the second storage space corresponding to the second acceleration interval, the third storage space corresponding to the third acceleration interval, and the sixth storage space corresponding to the sixth acceleration interval all contain several data sets, while the storage spaces corresponding to the other acceleration intervals are empty. In this example, the difference between the maximum number and the minimum number is 4. For example, if the second threshold is 2, then since 4 exceeds 2, the identification condition is met.

[0128] Furthermore, if the circular storage implementation method described above is adopted, in practical applications, after the motor runs for a certain period of time, each storage space will eventually reach full data storage, that is, each storage space will be full and new data will continuously overwrite old data.

[0129] By applying the technical solution provided in this invention, whenever a sampling time is reached, the real-time motor operation data for that sampling time is determined. This real-time motor operation data includes motor acceleration. Therefore, based on the magnitude of the motor acceleration in the real-time motor operation data at the current sampling time, a target storage space for storing the real-time motor operation data can be determined from each candidate storage space. In other words, for real-time motor operation data at a given sampling time, the real-time motor operation data is placed into the corresponding target storage space for storage based on the magnitude of the motor acceleration. Then, based on the motor operation data stored in both the target and non-target storage spaces, the mechanical parameters of the motor can be identified. This means that each time new real-time motor operation data is obtained, this application can perform one instance of motor mechanical parameter identification, thus ensuring the timeliness of motor mechanical parameter identification. Furthermore, since the larger the absolute value of the motor acceleration, the more accurate the real-time motor operation data obtained, by setting different capacities for each candidate storage space, and with the target storage space capacity being positively correlated with the motor acceleration, the larger the capacity of the storage space corresponds to the larger the absolute value of the motor acceleration stored. The larger the capacity, the more data can be stored. Thus, among all the motor operation data used for motor mechanical parameter identification, there will be more motor operation data with larger absolute values ​​of acceleration and fewer motor operation data with smaller absolute values ​​of acceleration, which helps to ensure the accuracy of the identification results.

[0130] In summary, the solution presented in this application achieves the identification of the mechanical parameters of the motor and ensures the real-time performance and accuracy of the identification. Furthermore, since the capacity of each candidate storage space is preset, the solution presented in this application can predetermine the maximum required storage space.

[0131] Corresponding to the above method embodiments, this invention also provides a mechanical parameter identification system for an electric motor, which can be referred to in conjunction with the above.

[0132] See Figure 5 The diagram shown is a structural schematic of a motor mechanical parameter identification system according to one embodiment of the present invention, comprising:

[0133] The data sampling module 501 is used to determine the real-time motor operation data at the current sampling moment; the real-time motor operation data includes motor acceleration.

[0134] The data classification module 502 is used to determine the target storage space for storing real-time motor operation data from each candidate storage space based on the motor acceleration; the capacity of each candidate storage space is different, and the capacity of the target storage space is positively correlated with the absolute value of the motor acceleration;

[0135] The identification module 503 is used to store real-time motor operation data in the target storage space and identify the mechanical parameters of the motor based on the motor operation data stored in the target storage space and the non-target storage space respectively.

[0136] In one specific embodiment of the present invention, the data classification module 502 is specifically used for:

[0137] Determine the target acceleration range to which the motor acceleration belongs; the target acceleration range can be any one of multiple preset acceleration ranges.

[0138] From each candidate storage space, determine the target storage space corresponding to the target acceleration interval;

[0139] Each candidate storage space corresponds to a preset acceleration interval, and the capacity of the candidate storage space is positively correlated with the absolute value of the median value of the corresponding preset acceleration interval.

[0140] In one specific embodiment of the present invention, the motor operating data includes the motor speed; the identification module 503 includes:

[0141] The data partitioning unit is used to store real-time motor operation data into a target storage space, and based on the motor speed, determine first operation data and second operation data from the motor operation data stored in the target storage space and the non-target storage space respectively; the motor speed in the first operation data is a positive number, and the motor speed in the second operation data is a negative number;

[0142] A forward mechanical parameter calculation unit is used to calculate forward mechanical parameters based on the first operating data;

[0143] The negative mechanical parameter calculation unit is used to calculate the negative mechanical parameters based on the second operating data;

[0144] The target mechanical parameter determination unit is used to determine the target mechanical parameters of the motor based on the positive and negative mechanical parameters.

[0145] In one specific embodiment of the present invention, the first operating data includes multiple operating parameters; the forward mechanical parameter calculation unit is specifically used for:

[0146] Statistical analysis was performed on each running parameter in the first running data to obtain the statistical value corresponding to each running parameter;

[0147] Based on the statistical values ​​corresponding to each operating parameter, the positive mechanical parameters are calculated.

[0148] In one specific embodiment of the present invention, the positive mechanical parameters include positive moment of inertia, positive disturbance torque, and positive viscous friction coefficient; the operating parameters include motor acceleration ω′ and motor torque T. e Motor speed ω, ω′T e ,ω′ω,T e ω, ω′ 2 and ω 2 The forward mechanical parameter calculation unit calculates the forward mechanical parameters based on the statistical values ​​of various operating parameters, specifically including:

[0149] Substitute the statistical values ​​of each operating parameter into the preset identification formula group to calculate the positive disturbance torque Tp, positive rotational inertia Jp, and positive viscous friction coefficient Bp.

[0150] The preset recognition formula group includes:

[0151]

[0152] a + b represents the statistical value corresponding to ω′. + T represents e The corresponding statistical value, c + d represents the statistical value corresponding to ω. + Represents ω′T e The corresponding statistical value, e + f represents the statistical value corresponding to ω′ω. + T represents e The statistical value corresponding to ω, g + Represents ω′ 2 The corresponding statistical value, h + Represents ω 2 The corresponding statistical value; M represents the total number of first and second run data.

[0153] In one specific embodiment of the present invention, the identification module 503 is specifically used for:

[0154] After storing the real-time motor operation data in the target storage space, determine whether the preset identification conditions are met based on the motor operation data stored in the target storage space and the non-target storage space respectively.

[0155] If so, the mechanical parameters of the motor are identified based on the motor operation data stored in the target storage space and the non-target storage space respectively.

[0156] In one specific embodiment of the present invention, the preset identification conditions include:

[0157] The total amount of motor operation data stored in the current target storage space and non-target storage space is greater than the first threshold, and the difference between the largest and smallest space number in the space numbers corresponding to the storage spaces storing motor operation data exceeds the second threshold.

[0158] The N storage spaces are numbered in order from 1 to N.

[0159] Corresponding to the above methods and system embodiments, this invention also provides a mechanical parameter identification device for an electric motor and a computer-readable storage medium, which can be referred to in conjunction with the above.

[0160] The mechanical parameter identification device for the motor may include:

[0161] Memory, used to store computer programs;

[0162] A processor is used to execute computer programs to implement the steps of the method for identifying the mechanical parameters of an electric motor as described above.

[0163] The computer-readable storage medium stores a computer program that, when executed by a processor, implements the steps of the above-described method for identifying the mechanical parameters of an electric motor. The computer-readable storage medium referred to herein includes random access memory (RAM), main memory, read-only memory (ROM), electrically programmable ROM, electrically erasable programmable ROM, registers, hard disks, removable disks, CD-ROMs, or any other form of storage medium known in the art.

[0164] It should also be noted that, in this application, relational terms such as "first" and "second" are used merely to distinguish one entity or operation from another, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Furthermore, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or apparatus. Without further limitations, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes the element.

[0165] Those skilled in the art will further recognize that the units and algorithm steps of the various examples described in conjunction with the embodiments disclosed in this application can be implemented in electronic hardware, computer software, or a combination of both. To clearly illustrate the interchangeability of hardware and software, the components and steps of each example have been generally described in terms of functionality in the foregoing description. Whether these functions are implemented in hardware or software depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementations should not be considered beyond the scope of this invention.

[0166] This application uses specific examples to illustrate the principles and implementation methods of the present invention. The descriptions of the above embodiments are only for the purpose of helping to understand the technical solutions and core ideas of the present invention. It should be noted that those skilled in the art can make several improvements and modifications to the present invention without departing from the principles of the present invention, and these improvements and modifications also fall within the protection scope of the present invention.

Claims

1. A method for identifying the mechanical parameters of an electric motor, characterized in that, include: Determine the real-time motor operating data at the current sampling moment; The real-time motor operation data includes motor acceleration; Based on the motor acceleration, a target storage space for storing the real-time motor operation data is determined from each candidate storage space; The capacity of each candidate storage space is different, and the capacity of the target storage space is positively correlated with the absolute value of the motor acceleration; The real-time motor operation data is stored in the target storage space, and the mechanical parameters of the motor are identified based on the motor operation data stored in the target storage space and the non-target storage space respectively.

2. The method for identifying the mechanical parameters of a motor according to claim 1, characterized in that, The step of determining the target storage space for storing the real-time motor operation data from each candidate storage space based on the motor acceleration includes: Determine the target acceleration range to which the motor acceleration belongs; the target acceleration range is any one of a plurality of preset acceleration ranges; From each of the candidate storage spaces, determine the target storage space corresponding to the target acceleration interval; Each candidate storage space corresponds to a preset acceleration interval, and the capacity of the candidate storage space is positively correlated with the absolute value of the median value of the corresponding preset acceleration interval.

3. The method for identifying the mechanical parameters of a motor according to claim 1, characterized in that, The motor operating data includes the motor speed; the identification of the motor's mechanical parameters based on the motor operating data stored in the target storage space and the non-target storage space includes: Based on the motor speed, first operating data and second operating data are determined from the motor operating data stored in the target storage space and the non-target storage space, respectively; the motor speed in the first operating data is a positive number, and the motor speed in the second operating data is a negative number; Based on the first operating data, the positive mechanical parameters are calculated; Based on the second operating data, the negative mechanical parameters are calculated; Based on the positive mechanical parameters and the negative mechanical parameters, the target mechanical parameters of the motor are determined.

4. The method for identifying the mechanical parameters of a motor according to claim 3, characterized in that, The first operating data includes multiple operating parameters; the calculation of the positive mechanical parameters based on the first operating data includes: For each running parameter in the first running data, statistics are performed to obtain the statistical value corresponding to each running parameter; Based on the statistical values ​​corresponding to each of the aforementioned operating parameters, the positive mechanical parameters are calculated.

5. The method for identifying the mechanical parameters of a motor according to claim 4, characterized in that, The positive mechanical parameters include positive moment of inertia, positive disturbance torque, and positive viscous friction coefficient; the operating parameters include motor acceleration ω′ and motor torque T. e Motor speed ω, ω′T e ,ω′ω,T e ω, ω′ 2 and ω 2 ; The positive mechanical parameters are calculated based on the statistical values ​​of each of the aforementioned operating parameters, including: Substitute the statistical values ​​of each of the aforementioned operating parameters into the preset identification formula group to calculate the positive disturbance torque Tp, positive rotational inertia Jp, and positive viscous friction coefficient Bp. The preset identification formula group includes: a + b represents the statistical value corresponding to ω′. + T represents e The corresponding statistical value, c + d represents the statistical value corresponding to ω. + Represents ω′T e The corresponding statistical value, e + f represents the statistical value corresponding to ω′ω. + T represents e The statistical value corresponding to ω, g + Represents ω′ 2 The corresponding statistical value, h + Represents ω 2 The corresponding statistical value; M represents the total number of first and second run data.

6. The method for identifying the mechanical parameters of a motor according to claim 1, characterized in that, The step of storing the real-time motor operation data in the target storage space, and identifying the motor's mechanical parameters based on the motor operation data stored in the target storage space and the non-target storage space respectively, includes: After storing the real-time motor operation data in the target storage space, determine whether the preset identification conditions are met based on the motor operation data stored in the target storage space and the non-target storage space respectively. If so, the mechanical parameters of the motor are identified based on the motor operation data stored in the target storage space and the non-target storage space, respectively.

7. The method for identifying the mechanical parameters of a motor according to claim 6, characterized in that, The preset recognition conditions include: The total amount of motor operation data stored in the target storage space and the non-target storage space is greater than the first threshold, and the difference between the largest and smallest space number in the space numbers corresponding to the storage spaces storing motor operation data is greater than the second threshold. The N storage spaces are numbered in order from 1 to N.

8. A mechanical parameter identification system for an electric motor, characterized in that, include: The data sampling module is used to determine the real-time motor operating data at the current sampling moment; The real-time motor operation data includes motor acceleration; The data classification module is used to determine the target storage space for storing the real-time motor operation data from each candidate storage space based on the motor acceleration. The capacity of each candidate storage space is different, and the capacity of the target storage space is positively correlated with the absolute value of the motor acceleration; The identification module is used to store the real-time motor operation data in the target storage space, and to identify the mechanical parameters of the motor based on the motor operation data stored in the target storage space and the non-target storage space respectively.

9. A device for identifying the mechanical parameters of an electric motor, characterized in that, include: Memory, used to store computer programs; A processor for executing the computer program to implement the steps of the method for identifying the mechanical parameters of an electric motor as described in any one of claims 1 to 7.

10. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores a computer program that, when executed by a processor, implements the steps of the method for identifying the mechanical parameters of an electric motor as described in any one of claims 1 to 7.

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