Motor parameter identification method and device, equipment and storage medium

Abstract

The invention provides a motor parameter identification method and device, equipment and a storage medium, and relates to the technical field of motors. The method comprises the following steps: respectively constructing a state matrix and an output equation of a motor; constructing a parameter matrix containing to-be-identified parameters, wherein the to-be-identified parameters at least comprise permanent magnet flux linkage and quadrature-axis inductance; performing iterative recursion on the parameter matrix based on the model equation, the state matrix and the output equation of the least square method; and determining an estimated value of the to-be-identified parameter as a target motor parameter in response to determining that the to-be-identified parameter meets a convergence condition and / or the number of iterations of the parameter matrix reaches a preset value. Therefore, motor parameters such as permanent magnet flux linkage and quadrature-axis inductance can be accurately identified, vector control is more accurate under the condition that the motor parameters mainly change due to motor temperature rise, magnetic saturation and running state change, the vector control can be realized under the auxiliary condition of existing load equipment, and extra cost does not need to be increased.

Description

Technical Field

[0001] The present application relates to the field of motor technology, and in particular to a motor parameter identification method, device, equipment and storage medium. Background Art

[0002] In actual working conditions, motor parameters will change due to factors such as motor temperature rise, magnetic circuit saturation, inductor cross coupling, etc. In order to obtain the best vector control effect, the motor parameters need to be identified online. At present, the main methods for motor parameter identification include model reference adaptive algorithm, least squares method, Kalman filter method and neural network.

[0003] The traditional batch least squares method requires a matrix inversion in each identification process, which results in a huge amount of calculation and cannot realize online identification. Therefore, how to improve the accuracy of online identification of motor parameters and reduce the complexity of calculation is a problem that needs to be solved at present. Summary of the invention

[0004] The present application provides a motor parameter identification method, device, equipment and storage medium, aiming to solve one of the technical problems in the related art at least to a certain extent.

[0005] In a first aspect, the present application provides a method for identifying motor parameters, comprising:

[0006] Construct the state matrix and output equation of the motor respectively;

[0007] Constructing a parameter matrix including parameters to be identified, wherein the parameters to be identified include at least permanent magnet flux linkage and quadrature-axis inductance;

[0008] Iteratively deducing the parameter matrix based on the model equation of the least squares method, the state matrix and the output equation;

[0009] In response to determining that the parameter to be identified satisfies a convergence condition and / or the number of iterations of the parameter matrix reaches a preset value, the estimated value of the parameter to be identified is determined to be a target motor parameter.

[0010] In a second aspect, the present application provides a motor parameter identification device, comprising:

[0011] A first building block is used to respectively build a state matrix and an output equation of the motor;

[0012] A second construction module is used to construct a parameter matrix containing parameters to be identified, wherein the parameters to be identified include at least permanent magnet flux linkage and quadrature-axis inductance;

[0013] An iteration module, used for iteratively recursively deducing the parameter matrix based on the model equation of the least square method, the state matrix and the output equation;

[0014] The determination module is used to determine that the estimated value of the parameter to be identified is a target motor parameter in response to determining that the parameter to be identified meets a convergence condition and / or the number of iterations of the parameter matrix reaches a preset value.

[0015] In a third aspect, the present application provides an electronic device, comprising: a processor; and a memory for storing instructions executable by the processor; wherein the processor is configured to execute instructions to implement a motor parameter identification method.

[0016] In a fourth aspect, the present application provides a computer-readable storage medium. When instructions in the computer-readable storage medium are executed by a processor of an electronic device, the electronic device is enabled to perform a motor parameter identification method.

[0017] In a fifth aspect, the present application provides a computer program product, including a computer program, and the computer program is executed by a processor to perform a motor parameter identification method.

[0018] In the embodiment of the present application, the state matrix and output equation of the motor are first constructed respectively, and then a parameter matrix containing the parameters to be identified is constructed, wherein the parameters to be identified include at least the permanent magnet flux and the cross-axis inductance, and then the parameter matrix is ​​iteratively deduced based on the model equation of the least squares method, the state matrix and the output equation, and finally, in response to determining that the parameters to be identified meet the convergence conditions and / or the number of iterations of the parameter matrix reaches a preset value, the estimated value of the parameter to be identified is determined to be the target motor parameter. Thus, the motor parameters such as the permanent magnet flux and the cross-axis inductance can be accurately identified, so that the motor's motion torque is stable, the noise is small, and the efficiency is high, thereby solving the problem that the motor parameters are mainly changed by the motor temperature rise, magnetic saturation, and operating state changes, making the vector control more accurate and not out of step, and can be achieved with the assistance of the existing load equipment without adding additional costs.

[0019] Additional aspects and advantages of the present disclosure will be given in part in the following description and in part will be obvious from the following description or learned through practice of the present disclosure. BRIEF DESCRIPTION OF THE DRAWINGS

[0020] The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments consistent with the present application and, together with the description, serve to explain the principles of the present application.

[0021] Figure 1 is a flow chart of a method for identifying motor parameters according to the first embodiment of the present application;

[0022] Figure 2 is a flow chart of a method for identifying motor parameters according to the second embodiment of the present application;

[0023] Figure 3 is a block diagram of a motor parameter identification device according to the present application;

[0024] Figure 4 A block diagram of an exemplary electronic device suitable for implementing the embodiments of the present application is shown.

[0025] The above drawings have shown clear embodiments of the present application, which will be described in more detail later. These drawings and text descriptions are not intended to limit the scope of the present application in any way, but to illustrate the concept of the present application to those skilled in the art by referring to specific embodiments. DETAILED DESCRIPTION

[0026] Embodiments of the present application are described in detail below, and examples of the embodiments are shown in the accompanying drawings, wherein the same or similar reference numerals throughout represent the same or similar elements or elements with the same or similar functions. The embodiments described below with reference to the accompanying drawings are exemplary and are only used to explain the present application, and cannot be construed as limiting the present application. On the contrary, the embodiments of the present application include all changes, modifications and equivalents that fall within the spirit and connotation of the appended claims.

[0027] It should be noted that the executor of the motor parameter identification method of this embodiment may be a motor parameter identification device, or may be any electronic device, which is not limited here, and the device may be implemented by software and / or hardware.

[0028] As an example, the application scenario of the embodiment of the present application may be a scenario for system parameter identification in driving vector control of a permanent magnet synchronous motor.

[0029] Figure 1 is a flow chart of a method for identifying motor parameters according to the first embodiment of the present application, such as Figure 1 As shown, the method includes:

[0030] S101: construct the state matrix and output equation of the motor respectively.

[0031] Among them, the state matrix can be used to describe the change of the motor system state over time.

[0032] Among them, the output equation can be used to describe the observed output of the motor system, which can be expressed in the form of an observation matrix. By using the state matrix and the output equation, the motor system can be modeled and identified, and control design can be performed.

[0033] Optionally, a state matrix can be constructed based on the direct-axis current and rotor electrical angular velocity of the motor, and an output equation can be constructed based on the stator resistance, direct-axis current, quadrature-axis current, direct-axis voltage, quadrature-axis voltage, direct-axis inductance and quadrature-axis inductance of the motor.

[0034] As an example, the state matrix may be:

[0035] As an example, the output equation may be:

[0036] Among them, u d is the direct axis voltage, u q is the quadrature axis voltage, i q is the quadrature axis current, i d is the direct axis current, ω e is the rotor electrical angular velocity, Rs is the stator resistance, L d is the direct-axis inductance, L q is the quadrature-axis inductance.

[0037] S102: constructing a parameter matrix including parameters to be identified, where the parameters to be identified include at least permanent magnet flux linkage and quadrature-axis inductance.

[0038] Among them, the parameters to be identified can be the parameters that need to be identified and determined among the various motor parameters. It should be noted that the motor parameters are mainly determined by the motor temperature rise, magnetic saturation, and operating status. Temperature affects the value of the winding resistance, and the magnetic circuit saturation mainly reflects the changes in inductance and flux, and the change in cross-axis inductance is more obvious than the change in direct-axis inductance. In order to make the vector control more accurate, in the embodiment of the present disclosure, at least the permanent magnet flux and the cross-axis inductance can be selected as the parameters to be identified.

[0039] As an optional implementation, the parameter matrix θ k Can be Among them, L q is the quadrature-axis inductance, ψ f is the permanent magnet flux, k represents the kth identification, and k is the number of identification steps.

[0040] S103: Based on the model equation, state matrix and output equation of the least square method, the parameter matrix is ​​iteratively deduced.

[0041] As a possible implementation method, the least squares method with forgetting factor (Recursive Least Squares with Forgetting Factor, RLS-FF) can be used to establish the model equation. RLS-FF is a recursive algorithm suitable for online parameter identification, and can reduce the computational complexity and reduce the data accumulation effect. By using the least squares method with forgetting factor, it can better adapt to the changes in system parameters and improve the accuracy of identification.

[0042] Specifically, we can first set the initial estimate of the parameter matrix θ^(0) and the covariance matrix P(0), as well as the forgetting factor λ, and then calculate the gain matrix K(k). We can use the formula in is the state matrix, and P(k-1) is the covariance matrix of the last identification.

[0043] Furthermore, the estimated value θ^(k) can be updated using the formula Where y(k) is the output equation.

[0044] Furthermore, the covariance matrix P(k) can be updated using the formula Where I is the identity matrix and λ is the forgetting factor.

[0045] Furthermore, the number of identification times k may be increased, and the above steps may be repeatedly performed to iteratively deduced the parameter matrix θ^(k) until the condition described in step S104 is met.

[0046] S104: In response to determining that the parameter to be identified meets a convergence condition and / or the number of iterations of the parameter matrix reaches a preset value, determining an estimated value of the parameter to be identified as a target motor parameter.

[0047] The target motor parameters may be final estimated values ​​of the parameters to be identified, which serve as the model output of the system.

[0048] Optionally, if the number of iterations of the parameters to be identified in the parameter matrix reaches a preset value, it can be considered that the accuracy and reliability of the parameters to be identified meet the requirements, and then the estimated value of the parameters to be identified at the current number of steps can be used as the target motor parameter.

[0049] For example, for the parameter matrix If the preset value of the number of iterations is k+1, then the parameter matrix θ k+1 The corresponding permanent magnet flux linkage and quadrature-axis inductance are used as target motor parameters.

[0050] Specifically, judging whether the parameters to be identified meet the convergence conditions can be achieved in the following ways:

[0051] For example, the stability of the estimated value of the parameter to be identified can be observed, and the change of the estimated value of the parameter can be observed during the iteration process. If the estimated value gradually tends to be stable after iteration, and the amplitude of the change gradually decreases, it can be considered that the parameter estimation has converged. In other words, when it is determined that the amplitude of the change of the parameter to be identified is less than the preset threshold, it can be determined that the parameter to be identified meets the convergence condition.

[0052] Alternatively, you can check the confidence intervals for the estimates by calculating the confidence intervals for the parameter estimates and observing how the confidence intervals change. If the confidence intervals gradually decrease and eventually contain the true value, then the parameter estimates are considered to have converged.

[0053] Alternatively, you can also observe how the likelihood function value or loss function value changes during the iteration process. If the function value gradually converges to a smaller value, then the parameter estimation can be considered to have converged.

[0054] In the disclosed embodiment, the state matrix and output equation of the motor are first constructed respectively, and then a parameter matrix containing the parameters to be identified is constructed, wherein the parameters to be identified include at least the permanent magnet flux and the cross-axis inductance, and then the parameter matrix is ​​iteratively deduced based on the model equation of the least squares method, the state matrix and the output equation, and finally, in response to determining that the parameters to be identified meet the convergence condition and / or the number of iterations of the parameter matrix reaches a preset value, the estimated value of the parameter to be identified is determined to be the target motor parameter. Thus, the motor parameters such as the permanent magnet flux and the cross-axis inductance can be accurately identified, so that the motor's motion torque is stable, the noise is small, and the efficiency is high, thereby solving the problem that the motor parameters are mainly changed by the motor temperature rise, magnetic saturation, and operating state changes, making the vector control more accurate and not out of step, and can be achieved with the assistance of the existing load equipment without adding additional costs.

[0055] Figure 2 is a flow chart of a motor parameter identification method according to the second embodiment of the present application, such as Figure 2 As shown, the method includes:

[0056] S201: Construct the state matrix and output equation of the motor respectively.

[0057] S202: constructing a parameter matrix including parameters to be identified, where the parameters to be identified include at least permanent magnet flux linkage and quadrature-axis inductance.

[0058] It should be noted that the specific implementation of steps S201 and S202 can refer to the above embodiment and will not be described in detail here.

[0059] S203: Determine initial estimated values ​​of the parameters to be identified, initial values ​​of the covariance matrix, and initial values ​​of the adaptive gain matrix.

[0060] As an example, the direct-axis stator inductance can be set to 0.330 mH, the quadrature-axis stator inductance to 0.630 mH, the permanent magnet flux to 0.068 Wb, the stator resistance Rs = 1.100 Ω, the rotor electrical angular speed to 3600 r / min, and the initial value G(0) of the adaptive gain matrix G(k) to 10 5 I, where I is the identity matrix and the covariance matrix P(k) Initial value of

[0061] S204: Calculate an adaptive gain matrix based on the state matrix, the covariance matrix and a preset forgetting factor.

[0062] Among them, the adaptive gain matrix G can be calculated by the following formula (k+1) :

[0063]

[0064] Among them, λ can be the preset forgetting factor, is the state matrix, P (k) is the covariance matrix.

[0065] It should be noted that there is a problem with the recursive least squares method. As the number of identifications continues to accumulate, more and more data will be collected, resulting in data accumulation to saturation. In the disclosed embodiment, by introducing the forgetting factor, the impact of new data is made greater, thereby increasing the accuracy of identification. Specifically, data at earlier times will gradually be forgotten, while newer data will have more weight. In the identification of motor system parameters, the temperature rise, magnetic saturation and operating status of the motor are the key factors that determine the motor parameters. In the disclosed embodiment, when determining the size of the forgetting factor, it can be comprehensively determined based on actual experience combined with factors such as the temperature rise, magnetic saturation and operating status of the motor.

[0066] S205: Update the covariance matrix based on the adaptive gain matrix, the state matrix and the forgetting factor.

[0067] Among them, the updated covariance matrix P can be calculated by the following formula (k+1) :

[0068]

[0069] S206: Based on the updated covariance matrix and the output equation, the parameter matrix is ​​updated.

[0070] Specifically, the updated parameter matrix θ can be calculated by the following formula: k+1 :

[0071]

[0072] S207: Increase the step length.

[0073] Specifically, the step length k may be increased to k+1, and then step S208 is executed.

[0074] S208: Repeat the above steps to iteratively deduce the parameters to be identified in the parameter matrix.

[0075] Specifically, the above steps S204-S207 may be repeatedly executed to iteratively update the parameters to be identified in the parameter matrix until the condition of step S209 is met.

[0076] S209: In response to determining that the parameter to be identified meets a convergence condition and / or the number of iterations of the parameter matrix reaches a preset value, determining the estimated value of the parameter to be identified as a target motor parameter.

[0077] It should be noted that the specific implementation of step S209 can refer to the above embodiment and will not be described in detail here.

[0078] In the disclosed embodiment, the state matrix and output equation of the motor are first constructed respectively, and then a parameter matrix containing the parameters to be identified is constructed, the parameters to be identified include at least the permanent magnet flux and the quadrature axis inductance, the initial estimated values ​​of the parameters to be identified, the initial values ​​of the covariance matrix and the initial values ​​of the adaptive gain matrix are determined, and then the adaptive gain matrix is ​​calculated based on the state matrix, the covariance matrix and the preset forgetting factor, and then the covariance matrix is ​​updated based on the adaptive gain matrix, the state matrix and the forgetting factor, and then the parameter matrix is ​​updated based on the updated covariance matrix and the output equation, and the step size is increased, and the above steps are repeated to iterate and recurse the parameters to be identified in the parameter matrix, and finally in response to determining that the parameters to be identified meet the convergence condition and / or the number of iterations of the parameter matrix reaches the preset value, the estimated value of the parameter to be identified is determined to be the target motor parameter. Thus, by using the recursive least squares method with a forgetting factor, new data can be more effectively incorporated into parameter estimation, and old data can be gradually forgotten during the identification process, thereby improving the identification accuracy. The parameters of the motor system can be identified online, and the parameters can be adapted to the situation where the parameters change over time, so that the vector control is more accurate. By using the least squares method with forgetting factor, the motor system parameters can be identified online and can adapt to the situation where the parameters change over time. This can improve the accuracy and stability of motor control and meet the requirements under different working conditions.

[0079] Figure 3 is a block diagram of a motor parameter identification device according to the present application, such as Figure 3 As shown, the motor parameter identification device 300 includes:

[0080] A first construction module 310 is used to construct a state matrix and an output equation of the motor respectively;

[0081] A second construction module 320 is used to construct a parameter matrix including parameters to be identified, wherein the parameters to be identified include at least permanent magnet flux linkage and quadrature axis inductance;

[0082] An iteration module 330, configured to iteratively recursively perform the parameter matrix based on the model equation of the least square method, the state matrix and the output equation;

[0083] The determination module 340 is configured to determine that the estimated value of the parameter to be identified is a target motor parameter in response to determining that the parameter to be identified satisfies a convergence condition and / or the number of iterations of the parameter matrix reaches a preset value.

[0084] Optionally, the first building module 310 is specifically used to:

[0085] Constructing the state matrix based on the direct axis current and the rotor electrical angular velocity of the motor;

[0086] The output equation is constructed based on the stator resistance of the motor, the direct-axis current, the quadrature-axis current, the direct-axis voltage, the quadrature-axis voltage, the direct-axis inductance and the quadrature-axis inductance.

[0087] Optionally, the iteration module 330 includes:

[0088] A first calculation unit, configured to calculate an adaptive gain matrix based on the state matrix, the covariance matrix and a preset forgetting factor;

[0089] A first updating unit, configured to update the covariance matrix based on the adaptive gain matrix, the state matrix and the forgetting factor;

[0090] A second updating unit, configured to update the parameter matrix based on the updated covariance matrix and the output equation;

[0091] Adding units to increase the step size;

[0092] The iterative unit is used to repeatedly execute the above steps to iteratively deduce the parameters to be identified in the parameter matrix.

[0093] Optionally, the first computing unit is further used for:

[0094] Determine the initial estimated values ​​of the parameters to be identified, the initial value of the covariance matrix, and the initial value of the adaptive gain matrix.

[0095] Optionally, the determining module is specifically used to:

[0096] In response to determining that the variation range of the parameter to be identified is smaller than a preset threshold, it is determined that the parameter to be identified meets a convergence condition.

[0097] In the disclosed embodiment, the state matrix and output equation of the motor are first constructed respectively, and then a parameter matrix containing the parameters to be identified is constructed, wherein the parameters to be identified include at least the permanent magnet flux and the cross-axis inductance, and then the parameter matrix is ​​iteratively deduced based on the model equation of the least squares method, the state matrix and the output equation, and finally, in response to determining that the parameters to be identified meet the convergence condition and / or the number of iterations of the parameter matrix reaches a preset value, the estimated value of the parameter to be identified is determined to be the target motor parameter. Thus, the motor parameters such as the permanent magnet flux and the cross-axis inductance can be accurately identified, so that the motor's motion torque is stable, the noise is small, and the efficiency is high, thereby solving the problem that the motor parameters are mainly changed by the motor temperature rise, magnetic saturation, and operating state changes, making the vector control more accurate and not out of step, and can be achieved with the assistance of the existing load equipment without adding additional costs.

[0098] According to an embodiment of the present application, the present application also provides an electronic device, a readable storage medium and a computer program product.

[0099] Figure 4 A block diagram of an exemplary electronic device suitable for implementing embodiments of the present disclosure is shown. Figure 4 The electronic device 12 shown is only an example and should not bring any limitation to the functions and scope of use of the embodiments of the present disclosure.

[0100] like Figure 4 As shown, the electronic device 12 is in the form of a general purpose computing device. The components of the electronic device 12 may include, but are not limited to: one or more processors or processing units 16, a system memory 28, and a bus 18 that connects various system components (including the system memory 28 and the processing unit 16).

[0101] The bus 18 represents one or more of several types of bus structures, including a memory bus or memory controller, a peripheral bus, a graphics acceleration port, a processor or a local bus using any of a variety of bus structures. For example, these architectures include but are not limited to Industry Standard Architecture (ISA) bus, Micro Channel Architecture (MAC) bus, Enhanced ISA bus, Video Electronics Standards Association (VESA) local bus and Peripheral Component Interconnection (PCI) bus.

[0102] The electronic device 12 typically includes a variety of computer system readable media. These media can be any available media that can be accessed by the electronic device 12, including volatile and non-volatile media, removable and non-removable media.

[0103] The memory 28 may include computer system readable media in the form of volatile memory, such as random access memory (RAM) 30 and / or cache memory 32. The electronic device 12 may further include other removable / non-removable, volatile / non-volatile computer system storage media. By way of example only, the storage system 34 may be used to read and write non-removable, non-volatile magnetic media ( Figure 4 not shown, usually called a "hard drive"). Although Figure 4 Not shown in the figure, a disk drive for reading and writing a removable non-volatile disk (e.g., a "floppy disk"), and an optical disk drive for reading and writing a removable non-volatile optical disk (e.g., a compact disc read only memory (CD-ROM), a digital versatile disc read only memory (DVD-ROM), or other optical media) may be provided. In these cases, each drive may be connected to the bus 18 via one or more data medium interfaces. The memory 28 may include at least one program product having a set (e.g., at least one) of program modules configured to perform the functions of the various embodiments of the present disclosure.

[0104] A program / utility 40 having a set (at least one) of program modules 42 may be stored, for example, in the memory 28, such program modules 42 including but not limited to an operating system, one or more application programs, other program modules, and program data, each of which or some combination may include an implementation of a network environment. The program modules 42 generally perform the functions and / or methods of the embodiments described in the present disclosure.

[0105] The electronic device 12 may also communicate with one or more external devices 14 (e.g., keyboards, pointing devices, displays 24, etc.), may communicate with one or more devices that enable a user to interact with the electronic device 12, and / or may communicate with any device that enables the electronic device 12 to communicate with one or more other computing devices (e.g., network cards, modems, etc.). Such communication may be performed via an input / output (I / O) interface 22. Furthermore, the electronic device 12 may also communicate with one or more networks (e.g., a local area network (LAN), a wide area network (WAN), and / or a public network, such as the Internet) via a network adapter 20. As shown, the network adapter 20 communicates with other modules of the electronic device 12 via a bus 18. It should be understood that, although not shown in the figure, other hardware and / or software modules may be used in conjunction with the electronic device 12, including but not limited to: microcode, device drivers, redundant processing units, external disk drive arrays, RAID systems, tape drives, and data backup storage systems.

[0106] The processing unit 16 executes various functional applications and data processing by running programs stored in the system memory 28, such as implementing the methods mentioned in the above embodiments.

[0107] In the description of this specification, the description with reference to the terms "one embodiment", "some embodiments", "example", "specific example", or "some examples" etc. means that the specific features, structures, materials or characteristics described in conjunction with the embodiment or example are included in at least one embodiment or example of the present disclosure. In this specification, the schematic representations of the above terms do not necessarily refer to the same embodiment or example. Moreover, the specific features, structures, materials or characteristics described may be combined in any one or more embodiments or examples in a suitable manner. In addition, those skilled in the art may combine and combine the different embodiments or examples described in this specification and the features of the different embodiments or examples, without contradiction.

[0108] In addition, the terms "first" and "second" are used for descriptive purposes only and should not be understood as indicating or implying relative importance or implicitly indicating the number of the indicated technical features. Thus, a feature defined as "first" or "second" may explicitly or implicitly include at least one of the features. In the description of the present disclosure, "plurality" means at least two, such as two, three, etc., unless otherwise clearly and specifically defined.

[0109] Any process or method description in a flowchart or otherwise described herein may be understood to represent a module, segment or portion of code that includes one or more executable instructions for implementing the steps of a custom logical function or process, and the scope of the preferred embodiments of the present disclosure includes additional implementations in which functions may not be performed in the order shown or discussed, including performing functions in a substantially simultaneous manner or in reverse order depending on the functions involved, which should be understood by technicians in the technical field to which the embodiments of the present disclosure belong.

[0110] The logic and / or steps represented in the flowchart or otherwise described herein, for example, can be considered as an ordered list of executable instructions for implementing logical functions, and can be embodied in any computer-readable medium for use by an instruction execution system, device or apparatus (such as a computer-based system, a system including a processor, or other system that can fetch instructions from an instruction execution system, device or apparatus and execute the instructions), or in combination with these instruction execution systems, devices or apparatuses. For the purpose of this specification, "computer-readable medium" can be any device that can contain, store, communicate, propagate or transmit a program for use by an instruction execution system, device or apparatus, or in combination with these instruction execution systems, devices or apparatuses. More specific examples of computer-readable media (a non-exhaustive list) include the following: an electrical connection with one or more wires (electronic device), a portable computer disk box (magnetic device), a random access memory (RAM), a read-only memory (ROM), an erasable and programmable read-only memory (EPROM or flash memory), a fiber optic device, and a portable compact disk read-only memory (CDROM). In addition, the computer-readable medium may even be paper or other suitable medium on which the program is printed, since the program may be obtained electronically, for example, by optically scanning the paper or other medium and then editing, interpreting or processing in other suitable ways if necessary, and then stored in a computer memory.

[0111] It should be understood that the various parts of the present disclosure can be implemented in hardware, software, firmware or a combination thereof. In the above-mentioned embodiments, multiple steps or methods can be implemented in software or firmware stored in a memory and executed by a suitable instruction execution system. For example, if implemented in hardware, as in another embodiment, any one of the following technologies known in the art or a combination thereof can be used to implement: a discrete logic circuit having a logic gate circuit for implementing a logic function for a data signal, a dedicated integrated circuit having a suitable combination of logic gate circuits, a programmable gate array (PGA), a field programmable gate array (FPGA), etc.

[0112] A person skilled in the art may understand that all or part of the steps in the method for implementing the above-mentioned embodiment may be completed by instructing related hardware through a program, and the program may be stored in a computer-readable storage medium, which, when executed, includes one or a combination of the steps of the method embodiment.

[0113] In addition, each functional unit in each embodiment of the present disclosure may be integrated into a processing module, or each unit may exist physically separately, or two or more units may be integrated into one module. The above-mentioned integrated module may be implemented in the form of hardware or in the form of a software functional module. If the integrated module is implemented in the form of a software functional module and sold or used as an independent product, it may also be stored in a computer-readable storage medium.

[0114] The storage medium mentioned above may be a read-only memory, a disk or an optical disk, etc. Although the embodiments of the present disclosure have been shown and described above, it can be understood that the above embodiments are exemplary and cannot be understood as limitations of the present disclosure. A person of ordinary skill in the art may change, modify, replace and modify the above embodiments within the scope of the present disclosure.

Claims

1. A method for identifying motor parameters, It is characterized in that include: Construct the state matrix and output equation of the motor respectively; Constructing a parameter matrix including parameters to be identified, wherein the parameters to be identified include at least permanent magnet flux linkage and quadrature-axis inductance; Iteratively deducing the parameter matrix based on the model equation of the least squares method, the state matrix and the output equation; In response to determining that the parameter to be identified satisfies a convergence condition and / or the number of iterations of the parameter matrix reaches a preset value, the estimated value of the parameter to be identified is determined to be a target motor parameter.

2. The method according to claim 1, It is characterized in that The state matrix and output equation of the motor are constructed respectively, including: Constructing the state matrix based on the direct axis current and the rotor electrical angular velocity of the motor; The output equation is constructed based on the stator resistance of the motor, the direct-axis current, the quadrature-axis current, the direct-axis voltage, the quadrature-axis voltage, the direct-axis inductance and the quadrature-axis inductance.

3. The method according to claim 1, It is characterized in that The model equation based on the least square method, the state matrix and the output equation, iteratively deduces the parameter matrix, including: Calculating an adaptive gain matrix based on the state matrix, the covariance matrix and a preset forgetting factor; Based on the adaptive gain matrix, the state matrix and the forgetting factor, updating the covariance matrix; Based on the updated covariance matrix and the output equation, updating the parameter matrix; Increase step length; The above steps are repeatedly performed to iteratively deduce the parameters to be identified in the parameter matrix.

4. The method according to claim 3, It is characterized in that Before calculating the adaptive gain matrix and the covariance matrix based on the state matrix and the output equation, the method further includes: Determine the initial estimated values ​​of the parameters to be identified, the initial value of the covariance matrix, and the initial value of the adaptive gain matrix.

5. The method according to claim 1, It is characterized in that In response to determining that the parameter to be identified satisfies a convergence condition, the step includes: In response to determining that the variation range of the parameter to be identified is smaller than a preset threshold, it is determined that the parameter to be identified meets a convergence condition.

6. A motor parameter identification device, It is characterized in that include: A first building block is used to respectively build a state matrix and an output equation of the motor; A second construction module is used to construct a parameter matrix containing parameters to be identified, wherein the parameters to be identified include at least permanent magnet flux linkage and quadrature-axis inductance; An iteration module, used for iteratively recursively deducing the parameter matrix based on the model equation of the least square method, the state matrix and the output equation; The determination module is used to determine that the estimated value of the parameter to be identified is a target motor parameter in response to determining that the parameter to be identified meets a convergence condition and / or the number of iterations of the parameter matrix reaches a preset value.

7. The device according to claim 6, It is characterized in that The first building module is specifically used for: Constructing the state matrix based on the direct axis current and the rotor electrical angular velocity of the motor; The output equation is constructed based on the stator resistance of the motor, the direct-axis current, the quadrature-axis current, the direct-axis voltage, the quadrature-axis voltage, the direct-axis inductance and the quadrature-axis inductance.

8. The device according to claim 6, It is characterized in that The iteration module comprises: A first calculation unit, configured to calculate an adaptive gain matrix based on the state matrix, the covariance matrix and a preset forgetting factor; A first updating unit, configured to update the covariance matrix based on the adaptive gain matrix, the state matrix and the forgetting factor; A second updating unit, configured to update the parameter matrix based on the updated covariance matrix and the output equation; Adding units to increase the step size; The iterative unit is used to repeatedly execute the above steps to iteratively deduce the parameters to be identified in the parameter matrix.

9. An electronic device, It is characterized in that include: A processor, and a memory communicatively connected to the processor; The memory stores computer-executable instructions; The processor executes the computer-executable instructions stored in the memory to implement the method according to any one of claims 1 to 5.

10. A computer-readable storage medium, It is characterized in that The computer-readable storage medium stores computer-executable instructions, which are used to implement the method according to any one of claims 1 to 5 when executed by a processor.

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